In Data Science interviews, interviewers often ask practical concepts based on time series to check your understanding and approach while solving complex time series problems. So, if you are preparing for Data Science interviews and looking for practice problems based…
Deep Neural Network Architectures, such as RNNs, LSTMs, GRUs, and Transformers, are used to build deep learning models for Natural Language Processing (NLP) problems. So, if you want to learn how to build deep learning models for NLP, this article…
If you are preparing for data science interviews, you need to practice interview problems based on SQL, pandas, NumPy, statistics, and many more foundational concepts in data science. So, if you are looking for Data Science interview problems, this article…
An ETL (Extract, Transform, and Load) pipeline extracts data from sources, transforms it, and loads it into a storage system. It helps create clean, usable data formats for analysis. PySpark is ideal for building ETL pipelines for large-scale data processing.…
Employers love to see projects based on real-world problems on your resume. If you aim for a career as a Machine Learning Engineer, Deep Learning Engineer, or maybe as an AI & ML Engineer, make sure you have worked on…
Data Scientists use SQL to solve complex data manipulation, aggregation, and transformation problems. In Data Science interviews, Interviewers often test your SQL knowledge to work with structured and semi-structured data, create readable queries, and handle real-world challenges like hierarchy, JSON,…
Climate change is one of the most critical challenges of our time, with rising carbon emissions playing a pivotal role in driving global temperature anomalies. Analyzing the relationship between CO₂ concentrations and temperature changes provides valuable insights into the underlying…
In today’s competitive job market, a well-crafted resume is your ticket to landing a dream role in Data Science. Your resume is not just a summary of your experiences but a marketing document that showcases your value to potential employers.…
Employers love to see projects based on real-world problems on your resume. If you are aiming for a career in Data Analytics, make sure you have worked on some real-world projects. It increases your chances of getting an interview call.…
Data Scientists often use practical statistical concepts while decision-making. In Data Science interviews, interviewers often ask problems based on practical statistics to test your proficiency in critical areas like hypothesis testing, dimensionality reduction, and uncertainty quantification. So, if you are…
Rainfall is a critical element of India’s climate system, which impacts agriculture, water resources, and the overall economy. Understanding rainfall patterns over time is essential for effective planning in sectors like agriculture, disaster management, and urban water supply. So, if…
Natural Language Processing (NLP) is a critical field within Data Science and Machine Learning that focuses on enabling machines to understand, interpret, and generate human language. As a beginner, you likely used NLTK to solve NLP problems, but the industry…
Employers look for real-world projects in your resume. If you are aiming for a career in AI & ML, make sure you have worked on some real-world projects. It will help you stand out and increase your chances of getting…
A Data Scientist’s day often starts and ends with data cleaning. Many practical questions in Data Science interviews focus on data cleaning concepts, given the importance of data cleaning in the data pipeline. So, if you’re preparing for Data Science…
Consumer Price Index (CPI) Analysis involves tracking the average price change over time for a basket of goods and services typically consumed by households. It serves as a primary measure of inflation, which helps companies and governments understand purchasing power…
If you are aiming for a career in AI & ML, you need to work on advanced AI & ML projects that you can mention on your resume. So, if you are looking for advanced AI & ML projects to…
There are many free and paid online courses for learning data science. But having a book with you in your learning journey is like having a go-to guide whenever you feel stuck. So, if you are looking for books you…
NumPy concepts are often asked in Data Science interviews to assess a candidate’s ability to perform efficient data manipulation and numerical computations. So, if you are preparing for Data Science interviews and looking for questions based on NumPy operations, this…
Hybrid machine learning models combine different types of algorithms to leverage their unique strengths, which results in improved predictive performance and robustness. If you want to learn about building a hybrid machine learning model, this article is for you. In…
Employers often look for Data Analytics projects in your portfolio that demonstrate your ability to understand the story behind the numbers, find problems in the data generated by the business, and create strategies to solve business problems or optimize current…
Learning the fundamentals of Data Science is like building the roots of a strong tree; the deeper they go, the more resilient and far-reaching your growth will be. If you don’t know what topics to learn to master Data Science…
A Data Scientist spends most of the time working with Pandas. This is why most practical questions in Data Science interviews are based on pandas concepts. So, if you are looking for Pandas concepts you should know about for Data…
In today’s data-driven world, mastering a diverse range of data science projects across various business domains can elevate your skills and set you apart as a versatile data scientist. In this article, I’ll take you through a list of 50+…
Fine-tuning large language models (LLMs) involves adapting a pre-trained model to perform well on a specific task or to reflect a specialized domain of language. Fine-tuning is essential when the model’s general knowledge needs refinement to meet the precision required…
From ML Algorithms to GenAI & LLMs is an expanded and comprehensive resource in machine learning and generative AI. It is the second edition of the popular Machine Learning Algorithms: Handbook. This edition is more than a simple update. It…
If you are learning Data Science, one of the best strategies you can follow is to work on projects based on the concepts you are learning. It will help you master every concept theoretically and practically. So, if you are…
The road to learning Generative AI starts with Machine Learning algorithms. However, some specialized models are used in Generative AI problems, that you must know if you are aiming for a career in Generative AI. So, in this article, I’ll…
If you are aiming for the role of a Data Scientist, build a habit of solving Data Science case studies that can prepare you for real-world challenges. So, if you are looking for such challenging problems and case studies, this…
Document analysis refers to extracting, interpreting, and understanding the information contained within a document. Traditionally, this involved manual review or simple keyword-based techniques, but with the rise of Large Language Models (LLMs) like GPT and BERT, LLMs are now preferred…
If you are learning Data Science and always find it difficult to understand how to solve real-time problems while working on completely new datasets, you need to work on projects that challenge you to explore new methods and the domain…
Deploying machine learning models involves multiple steps that ensure the model transitions from development to production. These steps require collaboration among various professionals, including data scientists, machine learning engineers, software engineers, DevOps professionals, and IT security teams. In this article,…
Machine Learning is used to build intelligent systems. Many people find it difficult to choose the right Machine Learning algorithm for a given problem, even after years of experience. So, if you face the same problem, this article is for…
Synthetic data is artificially generated data that mimics real-world data. It is created by algorithms, models, or simulations rather than being collected from actual events or real-world scenarios. So, if you want to learn about synthetic data generation using Generative…
In Data Science interviews, interviewers often ask questions based on the projects mentioned in your resume to check your understanding of what you did, how you did it, and what you found while working on the project. So, if you…
There are many concepts that no one will teach you in a course but are valuable in the real world while solving business problems. If you are aiming for the role of a Data Engineer and want to know the…
A text summarization model takes a long text and creates a shorter version while preserving its main points. It works by extracting key sentences directly (extractive summarization) or rephrasing the content into a shorter form (abstractive summarization). If you want…
There are various advanced concepts that no one teaches you in any course but are important for Data Analysts while solving business problems. So, if you want to know about the advanced concepts you should know as a Data Analyst,…
Generative AI means generating new data using existing data. If you are learning Generative AI and looking for projects where you can solve some real-world problems and mention about them in your resume, this article is for you. In this…
As data science professionals, we use various techniques to collect data. In some problems, you will be using the data generated by the business. And in some problems, you will use external data to understand how it affects your business.…
Content Strategy Analysis means analyzing how content is created, released, distributed, and consumed to achieve specific goals, such as maximizing audience engagement, viewership, brand reach, or revenue. If you want to learn how to analyze the content strategy of a…
There are some advanced concepts that no one teaches you in any course but are important for Data Scientists while solving business problems. So, if you want to know about the advanced concepts you should know as a Data Scientist,…
If you are aiming for a career in AI & ML, work on advanced projects based on Generative AI, NLP, and Deep Learning. Projects based on these techniques will help you show your skills as an AI & ML Engineer.…
SQL Data Analysis Functions are used to derive insights and perform calculations on your data for analytical purposes. If you are learning SQL for Data Science and want to know the functions you can use to analyze your data, this…
Mutual funds are investment plans that pool money from multiple investors to purchase a diversified portfolio of stocks, bonds, and other securities, managed by professional fund managers. A mutual fund plan is created by selecting the stocks where an investor…
To master AI & ML, you must specialize in three major areas: Machine Learning Algorithms, Deep Learning & NLP, and Generative AI & LLMs. So, if you are aiming for a career in AI & ML, this article is for…
If you are aiming for the role of a Data Analyst, you should have at least one advanced project mentioned in your resume where you have shown your analytical and problem-solving skills. So, if you are looking for such Data…
Some advanced concepts represent key areas that Machine Learning Engineers need to understand to effectively build, deploy, and optimize Machine Learning models in real-world applications. So, if you want to learn the advanced concepts that every Machine Learning Engineer should…
Screen time analysis refers to analyzing the amount of time a user spends interacting with their device (such as a smartphone, tablet, or computer) and various applications on a daily, weekly, or monthly basis. So, if you want to learn…
People often confuse the use of Large Language Models (LLMs) with Machine Learning Algorithms. Problems where Machine Learning Algorithms perform well LLMs can’t, and problems where LLMs perform well, ML algorithms can’t. So, when to use LLMs? Let’s understand in…
Working on research-based Data Science projects helps individuals develop problem-solving skills by applying advanced methodologies to real-world challenges. So, if you are looking for some project ideas for research-based projects, this article is for you. In this article, I’ll take…
A Data Scientist is a data professional who works with the data generated by a business to find the right growth opportunities, optimize the current state of the business, and make predictions to make better decisions. So, if you are…
Packaging Machine Learning Models
The work of a Data Scientist is to help a business identify the right opportunities and make better decisions by understanding the data generated by the business. To be good at your job as a data scientist, you should know…
If you are aiming for a career in Artificial Intelligence, then AI Engineer and AI/ML Engineer are the right roles that you can target. If you are preparing for any of these roles, you need to learn AI & ML…
SQL Subqueries are a powerful tool used to perform operations within a query by embedding one query within another. Data Analysts/Scientists use SQL subqueries for complex data retrieval and manipulation. So, if you want to learn about SQL subqueries, this…
Web scraping is a technique used to extract data from websites by sending requests to the server, retrieving the web pages, and parsing the HTML content to extract the necessary information. If you are learning Data Science, you should know…
A Data Engineer is a data professional who prepares data and the complete infrastructure needed to solve a particular problem or according to the needs of the business. If you are aiming for the role of a Data Engineer, this…
SQL aggregation functions allow you to perform calculations on data to summarise it in various ways. Data Analysts spend a lot of time using SQL aggregation functions while working with databases. So, if you want to learn about SQL aggregations,…
Analyzing reviews is one of the best data-driven strategies to improve your product or services. It helps in understanding what people think about your product, what they don’t like about it, and what more they expect from it. So, if…
Python is preferred in various fields in the industry like Data Science, Web Development, and API Development. If you are learning Python for any of these fields, do practice problem-solving using Python. So, if you are looking for practice problems…
A Data Science job interview contains various rounds to test your theoretical knowledge, practical skills, and how you think while approaching problems. So, if you are preparing for Data Science interviews, this article is for you. In this article, I’ll…
Data cleaning is an essential step in the process of solving a Data Science problem to ensure the data’s quality and reliability. If you are learning Data Science and want to understand how to clean your data using SQL, this…
Price Elasticity of Demand (PED) measures the responsiveness of the quantity demanded of a product to changes in its price. It is calculated as the percentage change in quantity demanded divided by the percentage change in price. If you want…
If you are learning Machine Learning, one of the best strategies you can follow is to work on projects based on the concepts you are learning. It will help you master every concept theoretically and practically. So, if you are…
If you come from a healthcare background and want to build a career in Data Science, you will find various opportunities if you can use your educational background correctly. So, if you want to know how to learn Data Science…
SQL joins are essential when combining data from multiple tables in a relational database. It is one of the popular SQL topics asked in Data Science Interviews. So, if you want to learn about SQL joins in detail, this article…
Inflation is the rate at which the general level of prices for goods and services rises over time, which leads to a decrease in the purchasing power of money. It indicates how much more expensive a set of goods and…
One of the best ways to learn Data Analytics is by working on projects that help you understand the concepts you are learning and implementing them on real-world problems. So, if you are learning Data Analytics, this is for you.…
Generative AI is a subset of Artificial Intelligence where we generate new data samples using the existing data. It can include generating text, images, audio, or other types of data. Now that the industry has understood the use of Generative…
SQL is an essential tool in almost all Data Science roles. It is one of the most fundamental tools to work with data. So, if you don’t know how much knowledge of SQL is required for a career in Data…
A Machine Learning model is used to make predictions based on historical data. Similarly, a Generative AI model creates new original data by learning from the historical data. So, if you want to learn how to build a Generative AI…
One of the best ways to learn Data Science is by working on projects that help you understand the concepts you are learning and implementing them on real-world problems. So, if you are learning Data Science, this is for you.…
If you are learning Data Science, you must have heard about Kaggle. Kaggle is a Data Science community by Google to find datasets. There are many more communities for datasets that you can follow, which are focused on datasets based…
When building a resume for a data science role, the projects you choose to showcase can significantly impact your chances of standing out to potential employers. So, it’s essential to choose projects that not only demonstrate your skills but also…
Training a Machine Learning model is not a difficult task, the difficult part is to understand when to use which parameter and how to select the right value for the parameter. So, let’s talk about the linear regression algorithm. In…
Customer Satisfaction Analysis is the process of collecting, analyzing, and interpreting data regarding how satisfied customers are with a company’s products, services, and overall experience. If you want to learn how to analyze the satisfaction of customers with a business…
If you are aiming for Data Science roles like Data Analyst, Business Analyst, or Data Visualization Specialist, you need to work on at least one comprehensive project based on Tableau or Power BI. So, if you are looking for Data…
If you are someone from a finance background and want to transition your career into Data Science, then there are some Data Science roles where your experience or knowledge in finance will be preferred. So, if you want to know…
Pretrained models are Machine Learning models that have been previously trained on a large dataset to solve a specific task. These models are then used or fine-tuned for other related tasks without the need to train them from scratch. So,…
Price optimization is a strategic approach that uses data analysis and modelling to determine the optimal pricing of products or services. This process involves considering various factors, including market demand, competition, costs, and customer behaviour, to set prices that maximize…
If you are aiming for a Data Science job, you need to be good in programming languages like Python or R. Having said that, if you know you aren’t that good at coding but you are good at analyzing data,…
If you want to improve yourself at analyzing data, you need to find your own way of thinking about what questions to ask from your data, starting your analysis the right way, and finishing your analysis the right way. Let’s…
In Machine Learning, hyperparameters are external configurations used to control the training process of a machine learning model. They are like the settings that are configured before the training starts and remain constant throughout the process. There are some hyperparameters…
Demographics analysis involves examining the characteristics of a population based on factors such as age, gender, location, income, education, and more. It helps businesses and organizations understand their audience, tailor marketing strategies, personalize customer experiences, and make informed decisions. So,…
Regression analysis is a statistical method used to examine the relationship between a dependent variable and one or more independent variables. Fitting a regression model to the data helps to predict the value of the dependent variable based on the…
Computer Vision is a field of artificial intelligence and computer science that enables computers to interpret and process visual information from the world, such as images and videos. If you want to work in the industry of automatic and self-driving…
Regular expressions (regex) are powerful tools for pattern matching and string manipulation. They are instrumental in Data Science for cleaning, parsing, and transforming data. So, if you want to know about some regex operations used in Data Science, this article…
Music popularity prediction involves developing machine learning models to estimate the popularity of music tracks based on their audio features. Predicting the popularity of music can help music streaming platforms understand user preferences, optimize playlists and enhance recommendation systems to…
There are a wide range of problems in the industry where Data Science professionals use Natural Language Processing (NLP). Recommendation Systems and Sentiment Analysis are some examples where NLP is used. So, if you are looking for datasets where you…
Finance is one of the highest-paying domains for Data Science jobs. If you are preparing for a Data Science job in the finance domain, you should work on Data Science projects based on finance and mention them on your resume.…
Handling categorical data is a crucial step in data preprocessing before training Machine Learning models. If you think there’s only one technique: one hot encoding, to handle categorical data, then you are wrong. There are many more techniques you should…
Stock market portfolio optimization is the process of selecting the best combination of stocks to maximize returns while minimizing the risk, based on historical performance data and financial metrics. If you want to learn how to optimize a stock market…
Data Visualization is one of the most valuable skills for any Data Science role. It helps in understanding and presenting data. If you are trying to improve your data visualization skills, work on datasets that can help you understand and…
Data Science is a combination of statistics and computer science. There are thousands of resources and roadmaps to learn the computer science side of Data Science, but people often struggle to learn the statistics part. So, if you want to…
Annotations are critical in data visualization as they provide additional context, highlight key insights, and make the visual representation of data more understandable and impactful. There are some annotation techniques that every Data Scientist/Analyst should know for effective data visualization.…
You must have seen video chapters in a YouTube video. Video chaptering is the process of dividing a video into distinct segments, each labelled with a specific title or chapter name, to enhance navigation and user experience. So, if you want to…
Hypothesis Testing is a statistical method used to make inferences or decisions about a population based on sample data. If you want to practice hypothesis testing techniques, use datasets where you can divide and compare the data based on two…
As a Data Science professional, you should know at least one data visualization tool. There are many more data visualization tools besides Tableau and Power BI, but if you know how to work with Tableau or Power BI, you can easily…
Recommendation systems are essential in many online platforms to help users discover products, services, or content they might be interested in. Several algorithms can be used to build recommendation systems, each suited for different scenarios. So, if you want to know about the recommendation…
If you want to practice data storytelling, then analyzing and summarizing the complete cricket match is one of the best tasks you should try. I recently collected data about the match between India and the USA in the T20 World Cup 2024. If you…
Time Series Analysis is one of the most important topics you should know for any Data Science job. When finding datasets to practice time series analysis, you will end up with stock market data most of the time. So, how…
Learning Excel is an unsaid rule for Data Science. People think that Excel is only used to record data, but in the real world, Excel is much more than that. So, if you want to know how to learn Excel…
In Data Science, cleaning data is a crucial step as it ensures the accuracy and quality of the data for analysis. There are some data cleaning methods you should know as a Data Scientist/Analyst to clean your data for analysis. So, in this article,…
If you are highly active on Facebook and Instagram, you must have seen ads based on elections by any of the political parties, especially BJP and INC. All the parties in India usually spend a lot of money on election campaigns. I recently…
If you are learning Data Science, it’s important to work on datasets where you are challenged to dig deeper into the data and explore the domain knowledge behind the problem you are solving. So, if you are looking for such datasets, this article is…
Image data preprocessing is a crucial step in any image-related Machine Learning task. As a Data Scientist and Machine Learning Engineer, you should know how to preprocess images, especially in companies where working with image data is crucial. So, if…
If you are aiming for a career in Language Modelling, you need to learn LLMs. Like we have TensorFlow, Keras, and PyTorch for Deep Learning, there are some Python libraries you need to learn specifically for working with LLMs. So, if…
If you are learning Data Science, YouTube is an amazing data source for you. There are so many problems we can solve by collecting data from YouTube. So, if you want to learn how to collect data from YouTube and analyze that data…
After experiencing applications like ChatGPT, LLMs are now considered the benchmark of language modelling. If you are looking for a career in language modelling or natural language processing (NLP), you need to have at least one project mentioned on your resume based…
When you learn Machine Learning, you use scikit-learn and Tensorflow to build models. It’s because scikit-learn and Tensorflow are beginner-friendly and also used by Data Science professionals at all levels. However, many more tools are used in the industry to build Machine Learning…
Time series analysis is a crucial aspect of data science, particularly when dealing with datasets that are collected or recorded over time. Understanding time series data allows data scientists to identify trends, seasonal patterns, and potential anomalies to make predictions. So, if you want…
A code generation model is a type of artificial intelligence that can automatically generate source code based on a given input, which can be natural language instructions, existing code snippets, or structured data. So, if you want to learn how…
Cricket analytics involves the use of data analysis and statistical methods to evaluate and enhance player performance, team strategies, and overall game outcomes in cricket. If you are aiming for a career in cricket analytics and looking for some project ideas, this…
Many beginners in the Data Science industry are told that if you are not good at working with algorithms, you can aim for the role of a Data Analyst. As a Data Analyst, your major focus is on data manipulation, analysis, and visualization. But…
Learning Numpy, Pandas, Matplotlib, and other Python libraries for Data Science is essential for every Data Science job. However, there are some internal Python modules that people often miss and learn later. So, if you want to know about such…
If you are someone interested in learning about the use of Data Science in cricket analytics, this article is for you. The match between RCB and Delhi Capitals was one of my favourite matches in the IPL this year. So, in this…
Exploratory Data Analysis (EDA) is a critical step in the Data Science workflow. It involves summarizing the main characteristics of a dataset, often with visual methods, before making any assumptions or hypotheses. The goal is to uncover patterns, spot anomalies,…
Data visualization is an essential aspect of Data Science, which helps to reveal patterns, trends, and insights that might not be obvious from raw data alone. While visualizing or presenting data, there are some advanced concepts you should use to enhance the clarity,…
The use of cloud platforms in Data Science is to provide space to store data and computational resources to solve problems at scale. A company generates thousands to millions of data points according to the scale of its business on…
Metro Operations Optimization refers to the systematic process of enhancing the efficiency, reliability, and effectiveness of Metro services through various data-driven techniques and operational adjustments. So, if you want to learn how to optimize the operations of a service like…
Some people who used to create content and courses on Data Analysis have suddenly started selling courses on Large Language Models (LLMs). It is something that confuses a lot of people about whether they need to learn LLMs or not.…
If you are aiming for a career in Natural language Processing (NLP), learning about Large Language Models (LLMs) is a must for you now. Although you won’t use LLMs as a fresher, you will find LLMs in the job description…
Natural Language Processing (NLP) is a critical field in data science, especially with the growth in data generated from online sources like social media, reviews, and more. It doesn’t matter if you are looking for a career in NLP or…
Website Performance Analysis involves evaluating various metrics related to a website’s functionality, user engagement, and overall success in achieving business goals. This form of analysis is critical because it directly impacts user experience, conversion rates, and the profitability and reputation of…
People often ask me how to find Data Science project ideas to build unique projects. Building unique Data Science projects means either applying a new concept to solve a problem instead of the traditional approach or using the concepts you…
If you are preparing for Data Science interviews, you will face questions based on Python programming, SQL for data manipulation and querying, pandas for data manipulation and scenario-based questions for testing your way of thinking while solving real-world problems. So, if…
Health Monitoring and Analysis refers to the systematic use of health data to track and evaluate the health status of individuals or populations over time. It contains a range of activities from real-time physiological data collection (like heart rate, blood…
Statistical modelling is a form of mathematical modelling that involves statistics to estimate or predict real-world behaviours, trends, and future outcomes based on data. It involves the construction of a statistical model, which is a formal representation of relationships between variables, typically expressed in the form of mathematical…
In the e-commerce industry, Data Science plays a crucial role in making informed decisions that enhance customer experience, optimize operations, and boost profitability. If you are aiming to work in the e-commerce domain as a Data Science professional, this article is for you. In…
Types of questions for Data Science job interviews are carefully structured to evaluate a candidate’s technical skills, problem-solving capabilities, and industry-specific knowledge. The questions can vary widely in scope and difficulty depending on the role, the company, and the specific…
As a Data Science fresher, you should mention and work on projects that can show most of your skills in solving a Data Science problem. Always make sure you are working on a project based on a real-time business problem.…
As a Data Science fresher, it’s not easy to get your first job without having any work experience. However, a new concept is gaining traction that may bridge this gap: virtual job simulation. Virtual job programs simulate the work environment…
In the marketing industry, data-driven decision-making is crucial for optimizing campaigns, understanding consumer behaviour, and maximizing return on investment (ROI). If you are aiming to work as a Data Science professional in the marketing domain, this article is for you.…
APIs, or Application Programming Interfaces, are sets of protocols and tools that allow different software applications to communicate with each other. Data collection using APIs involves accessing these interfaces provided by online services, platforms, or data providers to retrieve structured…
Some organizations and courses provide you with certifications for Data Science. But the certifications I am introducing here are not a part of any course. You can only achieve these certificates after passing exams with the defined criteria. That’s why…
Many students coming from a commerce background like to focus on the finance domain for a career in Data Science over domains like tech and software. Finance is one of the highest-paid domains, especially for a career in Data Science.…
Data manipulation refers to the process of adjusting, organizing, or transforming data to make it more organized, clean, and accessible for analysis. A Data Scientist/Analyst spend 70% of the time on data manipulation while solving a problem, which is why…
Stock market anomaly detection refers to the process of identifying unusual patterns or behaviours in stock market data that deviate significantly from the expected norm. The key is that these events are unexpected and can lead to significant price movements…
Solving problem statements and case studies is one of the best ways to improve your practical skills in working with data. So, if you are looking for some challenging problem statements to improve your data analysis skills, this article is…
Unlabelled datasets are collections of data that lack explicit annotations or labels that identify or classify the data points in a meaningful way. In contrast to labelled datasets, where each entry is tagged with a label or outcome (such as…
A data professional working in the finance domain should have domain knowledge about how this industry works and how financial and investment decisions are made by a business. There are some essential formulas you should know for a Data Science…
In Machine Learning, imbalanced data refers to a situation in classification problems where the number of observations in each class significantly differs. In such datasets, one class (the majority class) vastly outnumbers the other class (the minority class). This imbalance…
Business Analytics means using data to make business decisions. It involves the analysis of past business performance to gain insights and drive business planning. So, if you are looking for some Data Science project ideas based on Business Analytics, this…
Data Science is used in every domain because the use of data helps every business understand what’s happening in the business and what should be done to grow the business. If you want to know what kind of Data Science…
Building neural networks involves several key steps and decisions based on the task at hand and the nature of the data. If you are someone who has used a neural network architecture before to solve a problem but didn’t understand…
Hypothesis Testing is a statistical method used to make inferences or decisions about a population based on sample data. It starts with a null hypothesis (H0), which represents a default stance or no effect, and an alternative hypothesis (H1 or…
There are various practical skills and theoretical concepts that together shape you into an ideal candidate for a Data Science job. You cannot learn all Data Science skills and concepts from just one course doesn’t matter how comprehensive it is.…
Building a Data Science mindset involves cultivating habits and practices that enable you to think about your data the right way and effectively apply data-driven solutions to a wide range of problems. So, if you want to know how to…
Data preprocessing is a critical step in the data analysis and machine learning workflow. It involves transforming raw data into a clean, understandable format suitable for analysis. It includes tasks like cleaning, transformation, normalization, and feature extraction. So, if you…
Market size analysis is a crucial aspect of market research that determines the potential sales volume within a given market. It helps businesses understand the magnitude of demand, assess market saturation levels, and identify growth opportunities. So, if you want…
Working on Machine Learning projects is one of the best ways to improve your skills in solving Machine Learning problems. If you are an absolute beginner, you should work on projects that can introduce you to the fundamentals of Machine…
One of the most valuable skills of an ideal Data Scientist/Analyst is the ability to analyze and interpret data visualization graphs. If you want to analyze and interpret any data visualization graph, spend time to understand each graph’s components. It’s…
Multivariate Time Series Forecasting involves predicting future values of multiple time-dependent variables using historical data. Unlike univariate time series forecasting, which predicts a single variable (e.g., sales over time), multivariate forecasting considers several variables simultaneously. So, if you want to…
LinkedIn is one of the best and most trusted platforms to look for jobs. But when it comes to Data Science jobs, LinkedIn is not enough. Always go through the career pages and jobs section of data consulting companies, as…
Visualizing data the right way is crucial for analysis and communication. There are some common data visualization mistakes often made by beginners and data science professionals that can hinder the interpretation, integrity, and effectiveness of visualized data. So, if you…
SQL is one of the most useful tools to work with data at every level. If you are learning SQL for Data Science and have completed the fundamentals of SQL, it’s time to work on some real-world Data Science problems…
Exploratory Data Analysis (EDA) is an approach in data analysis that uses various techniques to maximize insight into a dataset, uncover underlying structure, analyze relationships between variables, detect outliers and anomalies, test underlying assumptions, and feature selection for training Machine…
Always use the STAR method to explain your Data Science projects in interviews. The STAR method is a structured approach to answering interview questions by discussing the specific Situation, Task, Action, and Result of an experience. So, if you want…
In Machine Learning, Decision Trees work by splitting the data into subsets based on feature values, essentially dividing the input space into regions with similar output values. There are several algorithms for building decision trees, each with its unique way…
Pandas is a fundamental library in Python for data manipulation and analysis, which provides rich functionalities and methods that enable data professionals to process and analyze large datasets. There are some advanced functions in Pandas that you should know for…
User profiling refers to creating detailed profiles that represent the behaviours and preferences of users, and segmentation divides the user base into distinct groups with common characteristics, making it easier to target specific segments with personalized marketing, products, or services.…
Creating a portfolio is a crucial step for a career in Data Science to showcase skills, projects, and experiences to potential employers or collaborators. If you want to know about some platforms you can use to showcase your Data Science…
The assumptions of Machine Learning algorithms are foundational principles that guide the algorithm’s design and application. There are some assumptions of Machine Learning algorithms that are commonly asked in interviews to test if you will be able to select the…
PySpark is the Python API for Apache Spark, an open-source, distributed computing system that offers an interface for programming entire clusters with implicit data parallelism and fault tolerance. PySpark brings the power of Spark to Python developers, allowing them to…
The comparison of multiple Machine Learning models refers to training, evaluating, and analyzing the performance of different algorithms on the same dataset to identify which model performs best for a specific predictive task. So, if you want to learn how…
Visualizing data effectively is crucial for analysis and communication. There are some data visualization rules that you should always follow to ensure your data visualizations are accurate and informative. So, if you want to know the essential data visualization rules…
One of the best ways to improve your Data Science skills is to work on real-world problems. Working on real-world problems helps you understand how to solve a Data Science problem from a particular domain or a particular type of…
SQL is one of the most valuable tools for working with data. There are some advanced SQL queries you don’t learn while learning the fundamentals of SQL, but are helpful in your job while analyzing data. So, if you want…
Food Delivery Cost and Profitability Analysis is a comprehensive evaluation aimed at understanding and optimizing the financial dynamics of a food delivery operation. The goal is to identify areas where the service can reduce costs, increase revenue, and implement pricing…
Learning Data Science is a journey that involves acquiring a broad set of skills, ranging from understanding the fundamentals of SQL and statistics to gaining hands-on experience with machine learning algorithms and practical implementation by working on projects. So, if…
Time Series data is a series of data points indexed in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time, making it a sequence of discrete-time data. It is widely used…
Learning Microsoft Excel is an unsaid rule for any Data Science job. It is a powerful tool that can perform simple to complex data manipulations, analysis, and visualization. So, if you want to know the essential Excel Formulas that you…
A Fashion Recommendation System using Image Features leverages computer vision and machine learning techniques to analyze fashion items’ visual aspects (like colour, texture, and style) and recommend similar or complementary products to users. So, if you want to learn how…
Geospatial Analysis refers to the process of analyzing and interpreting data that is associated with specific geographic locations. It consists of a wide range of techniques and tools designed to understand patterns, relationships, and trends across spatial dimensions. The outcomes…
Interviewing for a data science position typically involves several rounds to thoroughly evaluate a candidate’s skills and suitability for the role. Although the process of Data Science interviews can differ from country to country, there is a general process followed…
ANOVA, which stands for Analysis of Variance, is a statistical method used to analyze differences among group means and their associated procedures. It’s an essential tool in Data Science, particularly when comparing three or more groups for statistical significance. So,…
Metro Network Analysis involves examining the network of metro systems to understand their structure, efficiency, and effectiveness. It typically includes analyzing routes, stations, traffic, connectivity, and other operational aspects. So, if you want to learn how to analyze the metro…
Clustering analysis is a Machine Learning technique used to group a set of objects in a way that the data points in the same group (called a cluster) are more similar to each other than to the ones in other…
In Data Science, understanding different types of data distributions and their spread is crucial, as they provide insights into the nature of the data, its behaviour, and any underlying patterns. So, if you want to know about the types of…
Statistical equations are mathematical expressions that describe relationships, summarize data, or allow inferences and predictions. Some statistical equations form the backbone of many Data Science techniques. So, if you want to know the statistical equations you should learn as a…
A Text Generation Model is a type of Natural Language Processing (NLP) model that automatically generates human-like text. It can produce coherent and contextually relevant text based on the input text. So, if you want to learn how to build…
Many beginners in Data Science often get confused about how to choose data visualization graphs to visualize the data they are working on. Choosing the right data visualization graph depends on the nature of your data and the specific insights…
Becoming a successful data professional isn’t just about mastering technical skills. It’s more about developing the mindset of an analyst by being curious and systematic in your approach while solving any data science problem. Start thinking like an analyst to…
Probability provides the theoretical foundation needed to make statistical inferences, while statistics applies those theories to analyze and make sense of real-world data. Understanding probability and statistics is crucial for learning data science as these concepts form the backbone of…
Quantitative Analysis in the stock market involves the use of mathematical and statistical techniques to understand, predict, and make decisions about financial investments. If you want to learn how to perform Quantitative Analysis on stock market data, this article is…
Today, your education background doesn’t restrict you enough to get a career in your desired field. When it comes to Data Science, different educational backgrounds offer unique perspectives and skill sets that can be valuable in various aspects of data…
According to an IBM report, the worldwide average cost of a data breach in the previous year was $4.45 million which is a 15% increase in the last three years. Hence, it becomes imperative for organizations to protect these priceless…
A day in the life of a Data Scientist can vary significantly depending on the organization, the specific industry, and the projects they are working on. However, there are common elements that many Data Scientists experience in their daily work.…
In any real-world data collection, missing values can occur due to various reasons like errors in data entry, non-response in surveys, equipment malfunctions, or data corruption. Missing value imputation refers to replacing missing data with substituted values in a dataset.…
Car insurance modelling refers to the use of statistical and mathematical models to accurately predict events such as the frequency of claims, the severity of claims, or the total cost associated with claims, which in turn helps in pricing insurance…
Project-based interview questions focus on the projects you’ve previously worked on, often those listed on your resume as personal projects or the projects you have worked on in your previous job. Such questions can help interviewers to dive deep into…
Learning Python for Data Science is an essential step for anyone looking to enter the field, given Python’s status as one of the most popular programming languages for data analysis and machine learning. So, if you want to know how…
In Machine Learning, Classification tasks involve predicting categorical labels for input data, and having robust evaluation metrics that capture the nuances of these predictions is crucial. If you want to know how to evaluate the performance of Classification models, this…
An analytics dashboard is a data visualization tool that aggregates, displays, and analyzes key performance indicators (KPIs), metrics, and other key data points related to a business, department, or specific process. If you want to learn how to build an…
Business forecasting is the process of making predictions about future business events and performance based on historical data, statistical models, and business intelligence. If you are aiming for the roles of a Business Analyst, a Data Analyst, or a Data…
The impact of AI technologies like ChatGPT on job markets is a topic of much debate and discussion. We cannot run away from the fact that every decade brings new technologies, and we see a huge change in the job…
End-to-end Machine Learning Projects refer to comprehensive projects that encompass the entire lifecycle of a machine learning system, from the initial problem definition to the final deployment and ongoing maintenance. Building end-to-end projects will help you learn and go through…
Whether you’re a fresher or a working professional, preparing for a Data Science interview involves a series of steps to ensure you’re well-equipped to showcase your skills and knowledge. If you want to know how to prepare for Data Science…
Time series data is a sequence of data points collected or recorded at regular time intervals. Some common examples of time series include daily stock prices, monthly weather data, and yearly sales figures. If you want to learn how to…
An End-to-End Machine Learning Model is a comprehensive system that contains all the stages of a machine learning project, from data collection and preprocessing to model development, deployment, and ongoing maintenance. If you want to learn how to build an…
The ICC Men’s World Cup 2023 went beyond being a cricket tournament; it showcased the interplay between athletic prowess and technological innovation. This event’s utilization of data science marked a milestone emphasizing the harmonious blend of traditional sports strategies with…
Regression Analysis is a statistical method used in Machine Learning to understand and model the relationship between a dependent variable (also known as the target or response variable) and one or more independent variables (also known as predictor or explanatory…
The use of Data Science in the industry is to solve business problems. Companies are investing heavily to collect as much data as they can. This helps them in using data to build data-driven products. If you want to understand…
Aggregating and grouping are fundamental techniques in Data Science, often used to summarize or transform data for analysis. These techniques are essential when dealing with large datasets, as they help understand patterns and trends within the data. So, if you…
Real Estate Price Prediction is the process of estimating or forecasting the future prices of real estate properties, such as houses, apartments, or commercial buildings. The goal is to provide accurate property rates to buyers, sellers, investors, and real estate…
Text analysis, also known as text mining, focuses on extracting meaningful information and insights from structured and unstructured text data. It is crucial in various domains, including marketing, customer service, social media monitoring, content recommendation, and fraud detection. So, if…
WhatsApp’s core service, which includes text messaging, voice calls, and video calls, is free for individual users. However, it charges businesses to use its WhatsApp Business API, where WhatsApp uses various data-driven techniques to help businesses interact with customers at…
NumPy is the backbone of numerical computing in Python and is preferred in Data Science due to its efficiency, versatility, and extensive mathematical capabilities. If you want to know how to use NumPy for numerical computations, this article is for…
App Reviews Sentiment Analysis means evaluating and understanding the sentiments expressed in user reviews of mobile applications (apps). It involves using data analysis techniques to determine whether the sentiments in these reviews are positive, negative, or neutral. If you want…
Hey there! Let’s chat about something that’s really shaking things up in healthcare: machine learning (ML). Think of it as a super-smart computer that’s learning how to help doctors diagnose and treat us better. It’s like having a brainy sidekick…
Marketing is a fundamental business activity aimed to promote and sell products or services to customers. The primary goal of marketing is to understand customer needs and preferences, create products or services that meet those needs, and effectively communicate their…
Merging of Datasets in Data Science is the process of combining multiple datasets into a single, unified dataset. It is done to create a more comprehensive and complete dataset for analysis. If you want to learn the techniques used for…
When preparing for Machine Learning interviews, it’s crucial to have a good understanding of performance evaluation metrics, as they are key to assessing and improving model performance. So, if you are looking for some challenging interview questions related to performance…
Cohort Analysis is a data analysis technique used to gain insights into the behaviour and characteristics of specific groups of users or customers over time. If you want to learn how to perform Cohort Analysis, this article is for you.…
Tableau is a powerful data visualization tool used by Data Science professionals to transform raw data into interactive and understandable visualizations. It allows users to connect to various data sources, create dynamic charts, graphs, maps, and dashboards, and share these…
Machine learning is an advanced technology that now has been used for developments of so many fields and industries. Aside from website and app building, some businesses have successfully implemented this technology to improve the quality of their products and…
Hypothesis testing is a statistical method used to make decisions about population parameters based on sample data. It involves formulating null and alternative hypotheses, collecting data, and using statistical tests to determine the validity of the null hypothesis. If you…
Data science broadly encompasses data analysis, machine learning, data engineering, and statistical modelling. The path you take can vary based on the role you’re aiming for, such as a Data Analyst, Data Scientist, Machine Learning Engineer, or Data Engineer. So,…
Search Queries Anomaly Detection means identifying queries that are outliers according to their performance metrics. It is valuable for businesses to spot potential issues or opportunities, such as unexpectedly high or low CTRs. If you want to learn how to…
Hypothesis testing is a statistical method used to make decisions about population parameters based on sample data. It involves formulating null and alternative hypotheses, collecting data, and using statistical tests to determine the validity of the null hypothesis. Hypothesis testing…
Hypothesis Testing is one of the most valuable concepts for Data Science professionals. Significance level (alpha) and p-values are fundamental concepts in hypothesis testing. The significance level determines the risk of Type I errors, while p-values quantify the strength of…
Data Transformation involves converting and modifying data from its original format into a more suitable format for analysis. It aims to enhance the quality, usability, and effectiveness of the data for various analytical tasks. If you want to learn how…
E-commerce, short for electronic commerce, refers to the buying and selling of goods and services over the Internet. In E-commerce, businesses set up online platforms (websites or mobile apps) to showcase their products or services. Customers browse through these platforms,…
SQL is one of the most valuable tools for data professionals. Data professionals use SQL queries to extract relevant data for analysis. If you want to build an SQL project for Data Science but don’t know how to get started,…
Data Manipulation means altering, transforming, or restructuring data to prepare it for analysis, reporting, or other data-related tasks. It is a fundamental step in data management and analysis, as raw data often needs to be cleaned, organized, and refined to…
Customer Behavior Analysis is a process that involves examining and understanding how customers interact with a business, product, or service. This analysis helps organizations make informed decisions, tailor their strategies, and enhance customer experiences. If you want to learn how…
In a Data Science interview, candidates are often assessed on their knowledge of statistics, as statistics is a fundamental component of data analysis and modelling. If you want to know what kind of interview questions based on statistics you can…
Data Visualization is the graphical representation of data to help individuals, including Data Scientists and decision-makers, comprehend, analyze, and draw insights from complex datasets. It involves using various visual elements such as charts, graphs, plots, and maps to present data…
In the Finance domain, there are various Data Science jobs for leveraging data-driven insights to make informed financial decisions, manage risks, detect fraud, and optimize investment strategies. If you are interested in the Finance domain and want to know the…
A Book Recommendation System is a data-driven application designed to suggest books to users based on their preferences, reading history, and behaviour. It employs various data science and machine learning techniques to provide personalized book recommendations, enhancing the reading experience…
The retail domain refers to the sector of the economy that involves the sale of goods and services to consumers for their personal or household use. Retailers purchase products from manufacturers or wholesalers and then sell them to individual consumers…
Scikit-learn is a powerful library that provides a wide range of tools for data preprocessing and machine learning. It is built on top of other popular Python libraries like NumPy, SciPy, and Matplotlib, making it an integral part of the…
Ads CTR Analysis stands for Click-Through Rate Analysis for advertisements. Ads CTR Analysis is the process of examining the effectiveness of online advertisements by measuring the rate at which users click on an ad’s link to reach the advertiser’s website.…
Data analysis is the systematic process of inspecting, cleaning, transforming, and interpreting raw data to uncover meaningful patterns, trends, and insights. If you are looking for a job as a Data Analyst, you need to have some Data Analysis projects…
Time Series Forecasting is a statistical technique used to make predictions based on historical time-ordered data points. It’s valuable when dealing with data that changes over time, such as stock prices, sales figures, weather data, or economic indicators. ARIMA and…
There are various Data Science roles in the industry. Each role requires you to be an expert in some specific skills to get your first Data Science job. If you want to know what skills you should have for your…
Market Basket Analysis is a data-driven technique used to uncover patterns and relationships within large transactional datasets, particularly in retail and e-commerce. It helps businesses understand which products or items are often purchased together, providing insights for optimizing product placement,…
Deep Learning is a subset of Machine Learning that leverages deep neural networks to learn and represent complex patterns and features from data. If you are looking for a job specifically focused on Machine Learning, you should have at least…
Regression metrics are quantitative measures used to evaluate the performance of regression models. They provide information about how well a regression model fits the data and how accurately it predicts the outcome variable. If you want to learn how to…
SQL is a language that allows users to communicate with relational databases, which are structured storage systems designed to organize and store data in tables. These tables consist of rows and columns, much like a spreadsheet. SQL provides a standardized…
Stock Market Comparison Analysis is the process of evaluating and comparing the performance of multiple stocks or financial instruments within the stock market. It aims to provide insights into how different stocks have performed relative to each other and the…
Neural network architectures refer to the structural and organizational designs of artificial neural networks (ANNs). These architectures determine how the network is organized, including the number of layers, the number of neurons in each layer, the connections between neurons, and…
Data Resampling is a technique used in Data Science to adjust the size or distribution of a dataset. It involves modifying the existing dataset by either increasing or decreasing the number of data points. If you want to learn how…
Data Science means using data and computer science to solve business problems. There are various problems in Data Science that a Data professional deals with daily. If you want to know about the types of problems we deal with in…
Text Analysis, also known as Text Mining or Natural Language Processing (NLP), is a technique used to extract meaningful information and insights from unstructured text data, including documents, emails, social media posts, customer reviews, and more. If you want to…
Machine Learning means using data and algorithms to build intelligent systems. There are various Machine Learning algorithms used for training models that help in solving problems and building intelligent systems. While learning Machine Learning, many people are often confused about…
In Machine Learning, Feature Selection is the process of choosing a subset of relevant and significant features (variables or attributes) from features available in a dataset. The goal of selecting features is to improve the performance of a Machine Learning…
Data Visualization is a critical aspect of data science and analytics. It refers to presenting data in graphical or visual formats, such as charts, graphs, and maps, to help people understand the patterns, trends, and insights hidden within large and…
Customer Acquisition Cost (CAC) Analysis is a critical aspect of business strategy where Data Science plays a vital role. CAC refers to the cost a company incurs to acquire a new customer. Understanding and optimizing this cost is crucial for…
“Machine Learning Algorithms: Handbook” is a book based on a step by step guide to all Machine Learning algorithms with implementation using Python. In this article, I’ll give you a complete overview of the book so that you can understand…
Many people find solving a Data Science problem difficult even after mastering Machine Learning algorithms. The problem is that they focus more on algorithms than skills and the mindset of working with data. Data Science is not all about mastering…
Data manipulation is a fundamental process in data science that involves making changes, transformations, or adjustments to raw data to prepare it for analysis. This process is critical because real-world data is often messy, incomplete, or in a format that…
Cloud platforms, also known as cloud computing platforms, are comprehensive services provided by technology companies like Amazon, Microsoft, Google, and IBM, among others. These platforms offer a wide range of computing resources, including servers, storage, databases, networking, analytics, Machine Learning,…
Air quality index (AQI) analysis is a crucial aspect of environmental data science that involves monitoring and analyzing air quality in a specific location. It aims to provide a numerical value representative of overall air quality, essential for public health…
Domain knowledge refers to expertise and understanding of a specific field or subject matter. It encompasses the concepts, terminology, practices, and challenges associated with that domain. In Data Science, domain knowledge means a deep understanding of the industry, business, or…
Feature Engineering is a critical step in solving a Data Science problem that involves creating or selecting the most relevant and informative features (input variables) from raw data. These features are then used to train machine learning models, so the…
Data storytelling is a compelling way of communicating insights and information derived from data by constructing a narrative around it. It involves presenting data-driven findings in a clear, engaging, and relatable manner, similar to how a storyteller weaves a tale.…
Dating Recommendation refers to using data-driven techniques to provide personalized matchmaking suggestions to individuals seeking partners through online dating platforms. These recommendations are based on various factors and preferences, aiming to increase the likelihood of successful and compatible matches. If…
In Data Science interviews, technical questions are designed to assess your fundamentals of working with data and your understanding of using Data Science concepts in a given problem. If you want to understand what types of questions are asked in…
In Machine Learning, Ridge and Lasso Regression are regularization techniques used in linear regression to prevent overfitting and improve the model’s generalization to unseen data. They work by adding a penalty term to the linear regression loss function. If you…
Everyone has their own way of approaching a Data Science problem. If you are a beginner in Data Science, then your way of approaching the problem will develop over time. But there are some steps you should follow to start…
Fitness Watch Data Analysis involves analyzing the data collected by fitness wearables or smartwatches to gain insights into users’ health and activity patterns. These devices track metrics like steps taken, energy burned, walking speed, and more. So, if you want…
Questions like User Engagement Analysis are popular SQL questions in Data Science interviews. To solve this problem, you have to analyze user engagement to find the most engaging posts and users based on the given conditions. So, if you want…
Machine Learning means using data and algorithms to build intelligent systems. Machine Learning algorithms are computational procedures that enable machines to learn patterns and relationships from data without being explicitly programmed. These algorithms allow machines to make predictions, decisions, and…
Supply Chain involves coordination among various entities, such as suppliers, manufacturers, distributors, retailers, and customers, to optimize the process to minimize costs, enhance quality, and meet customer demands. If you are aiming for a Data Science job in retail, manufacturing, logistics,…
Demand Forecasting means estimating future customer demand for a product or service based on historical data and relevant factors. Inventory Optimization is the strategic management of inventory levels to ensure that the right amount of goods is available at the…
While learning Data Science, the decision between pursuing a degree or opting for courses and certifications can significantly impact your career trajectory. Your educational background plays a crucial role in determining the most suitable path for your journey in data…
In the Finance industry, various institutions and entities come together to facilitate financial transactions, manage risks, allocate resources, and make informed decisions, including commercial banks, investment banks, asset management firms, insurance companies, stock exchanges, regulatory bodies, and more. If you…
Python is a popular programming language for Data Analysis for several reasons. In the same way, Tableau and Power BI are powerful tools used in the industry for Data Analysis. It confuses a lot of people, especially beginners, why we…
Anomaly detection in transactions means identifying unusual or unexpected patterns within transactions or related activities. These patterns, known as anomalies or outliers, deviate significantly from the expected norm and could indicate irregular or fraudulent behaviour. If you want to learn…
Questions like Sales by Day of the Week Report are popular SQL questions in Data Science interviews. To solve this problem, you need to group the sales data by the day of the week, calculate the total sales amount for…
Marketing is a strategic and dynamic process to promote products or services to a target audience. It involves activities that create awareness, generate interest, and influence consumer behaviour. If you are aiming to work as a Data Science professional in…
Hyperparameter tuning is an essential process in Machine Learning that involves optimizing the settings that guide the training of a Machine Learning model. But these settings themselves are not learned from the data. These settings, called hyperparameters, play a vital…
A Web Data ETL (Extract, Transform, Load) pipeline is a systematic process used in data engineering to collect, transform, and load data from various sources on the internet into a structured and usable format for analysis and storage. So, if…
Questions like grouping sold products by date are popular SQL questions in Data Science interviews. In this problem, your task is to group the products sold on each date and calculate the total number of products sold on that date.…
In Data Science, time series refers to a sequence of data points recorded at specific time intervals. Time series data captures observations over time, where each data point corresponds to a particular moment or period. Time series analysis involves uncovering…
Data cleaning, also known as data cleansing or scrubbing, is a crucial step in data preparation and analysis. It refers to the systematic and meticulous identification, correction, and removal of errors, inconsistencies, and inaccuracies from data. The main objective of…
Forecasting the number of subscriptions Netflix will achieve in a time period is a vital business practice that enables them to plan, strategize, and make data-driven decisions. It enhances operational efficiency, financial planning, and content strategy, ultimately contributing to their…
In today’s competitive Data Science field, employers prefer candidates who can seamlessly bridge the gap between working with data and domain knowledge. By delving into projects that align with specific industries, you can demonstrate a deep understanding of the domain’s…
Questions like calculating the average selling price of various products are popular SQL questions in Data Science interviews. The average selling price is calculated by adding up all the selling prices of a specific product and dividing the total by…
Plotly is a powerful and versatile data visualization library used by Data Science professionals to create interactive and visually appealing plots, charts, and graphs. It enables users to represent complex datasets in a graphical form, making it easier to comprehend…
If you are looking for your first data science job, you need advanced and unique projects mentioned in your resume. A data science professional works with APIs to collect real-time data from company databases and third-party applications. Having at least…
A Music Recommendation System is an application of Data Science that aims to assist users in discovering new and relevant musical content based on their preferences and listening behaviour. Personalized music recommendations have become an essential tool in the digital…
Data analysis is the systematic process of inspecting, cleaning, transforming, and interpreting raw data to uncover meaningful patterns, trends, and insights. A data analyst is a Data Science professional skilled in working with data and equipped with the necessary tools…
Outliers are data points that deviate significantly from the rest of the data. These data points lie far away from the majority of the data points and can have a substantial impact on statistical analysis and modelling. If you want…
Recommendation systems are one of the popular applications of Data Science in every domain. The recommendation system of Amazon works by using both collaborative filtering and content-based filtering techniques. Amazon uses collaborative filtering to analyze user behaviour and recommend products…
Many web platforms and applications perform A/B testing to run experiments to find the best design, layout, or themes for their platform. Instead of relying on assumptions or hunches, applications and websites can test different design variations on real-time users…
Data Analysis means understanding the story behind the numbers. It is a systematic process of examining, cleaning, transforming, and interpreting raw data to uncover the story behind the data points. If you are learning Data Analysis, one of the best…
Neural Networks are a type of computational model inspired by the structure and functioning of a human brain. They are composed of interconnected nodes called neurons, organized into layers. Each neuron receives inputs, processes them, and produces an output that…
SQL is widely used in Data Science because it allows data science professionals to extract and manipulate data efficiently from databases. It is a powerful language for managing and analyzing structured data. If you are looking for some practical SQL…
Bank Account Summary is a popular SQL question in Data Science interviews. It involves generating a bank account summary in a specific format using SQL. So, if you want to know how to create a bank account summary, this article…
Matplotlib is a popular data visualization library for Python. It provides functions and tools enabling data scientists to create various plots and charts. Matplotlib allows data scientists to create high-quality visualizations that effectively communicate insights and patterns hidden within the…
Next Word Prediction means predicting the most likely word or phrase that will come next in a sentence or text. It is like having an inbuilt feature on an application that suggests the next word as you type or speak.…
A Machine Learning Engineer uses data and algorithms to build intelligent systems. If you are new to machine learning, you need to work on beginner-level machine learning projects to understand how to use machine learning algorithms on datasets to solve problems. If…
Python and R are preferred languages for data science due to their rich ecosystem of libraries, extensive community support, and powerful tools for statistical analysis, data manipulation, machine learning, and visualization. You can choose either Python or R for Data…
Calculating Ride Cancellation Rate is a popular SQL question in Data Science interviews. It involves analyzing the cancellation rate for ride requests, specifically from users who are not banned. So, if you want to know how to calculate the ride…
Pandas is a powerful open-source library in Python that provides high-performance data manipulation and analysis tools. Pandas enhances the productivity and efficiency of Data Science professionals by providing a comprehensive set of tools for data manipulation, analysis, and exploration. Its…
User Engagement Analysis is a data-driven approach to assess and understand user involvement, interaction, and satisfaction with a product, service, or platform. It involves analyzing various metrics and behavioural patterns to gain insights into user behaviour and preferences. It aids…
Data Engineering focuses on the foundational aspects of managing and processing data, providing the necessary infrastructure and pipelines to support Machine Learning and Analytics workflows. It ensures that data is reliable, accessible, and processed efficiently to enable data-driven decision-making and…
Market Analysis is a popular SQL question in Data Science interviews. It is often asked to understand your ability to manipulate and analyze data using SQL operations such as filtering, aggregating, sorting, and joining tables. So, if you want to…
NumPy, which stands for Numerical Python, is a powerful library in Python providing support for large, multi-dimensional arrays and matrices, along with a vast collection of mathematical functions to operate on these arrays. It serves as a fundamental building block…
Credit scoring and segmentation refer to the process of evaluating the creditworthiness of individuals or businesses and dividing them into distinct groups based on their credit profiles. It aims to assess the likelihood of borrowers repaying their debts and helps…
There are various Data Science tools used in the industry. The usage of different data science tools across companies and professions stems from the diverse nature of their data, objectives, and roles within the industry. Companies, ranging from small startups to large…
The problem of fixing names in a table is a popular SQL question in Data Science interviews. It involves using SQL queries to correct or modify names stored in a particular column. It helps the interviewer understand your data manipulation…
Market basket analysis is a Data Science concept that aims to understand customers’ buying habits to uncover associations and relationships between products. It focuses on analyzing the contents of customers’ shopping carts to identify frequently purchased items together. If you…
Dynamic Pricing is an application of data science that involves adjusting the prices of a product or service based on various factors in real time. It is used by companies to optimize revenue by setting flexible prices that respond to…
Mentioning projects in a resume is of the utmost importance as it allows potential employers to assess your practical skills and abilities. For Data Science jobs, it is essential to mention projects in your resume the right way, as it…
The problem of User Activity Analysis is a popular SQL question in Data Science interviews. It involves extracting and analyzing the user activity data within the designated timeframe of the number of given days. So, if you want to know…
RFM analysis stands for recency, frequency, and monetary value analysis. It is a data science concept used in marketing and customer relationship management. It involves analyzing customer behaviour and segmenting customers based on their transactional history, specifically focusing on three…
Data preprocessing is a critical step in data science tasks, ensuring that raw data is transformed into a clean, organized, and structured format suitable for analysis. A data preprocessing pipeline streamlines this complex process by automating a series of steps, enabling data…
Nike is an American multinational corporation engaged in the design, development, manufacture, marketing and worldwide sale of footwear, apparel, sports equipment, accessories and services. There are many ways how Nike uses Data Science to stay ahead of its competitors worldwide.…
The problem of product sales analysis is a popular SQL question in Data Science interviews. It involves analyzing product sales and identifying top-selling products based on total revenue. So, if you want to know how to solve this problem, this…
ARIMA (autoregressive integrated moving average) is a widely used statistical model for time series analysis and forecasting. It combines three key components: autoregression (AR), differentiation (I), and moving average (MA). In ARIMA models, the p, d, and q values are…
RFM Analysis is used to understand and segment customers based on their buying behaviour. RFM stands for recency, frequency, and monetary value, which are three key metrics that provide information about customer engagement, loyalty, and value to a business. So,…
Customer Segmentation is used to divide a company’s customer base into distinct groups based on common characteristics. It helps companies better understand their customers. If you want to understand Customer Segmentation and how Data Science professionals use this technique, this…
The problem of finding top-performing ads is a popular SQL question in Data Science interviews. It involves analyzing ad performance and identifying top-performing ads based on click-through rate (CTR). So, if you want to know how to solve this problem,…
Python has a remarkably clear and intuitive syntax, which emphasizes readability and thus reduces the burden of program maintenance. It is designed to be easy to understand and implement, making it ideal for beginners as well as Data professionals. So…
In Machine Learning, Scaling and Normalization are techniques used in data preprocessing to transform features or variables in a dataset. These techniques ensure that the data is in an appropriate range and distribution, which facilitates efficient training of Machine Learning…
A hybrid recommendation system is a recommendation technique that offers a complete and balanced approach by mixing two or more recommendation techniques. It aims to provide more accurate, diverse and personalized recommendations to users leveraging the strengths of different techniques…
Exploratory data analysis (EDA) is a Data Science concept where we analyze a dataset to discover patterns, trends, and relationships within the data. If you want to understand Exploratory Data Analysis, this article is for you. In this article, I will…
The problem of deleting duplicate emails is a popular SQL question in Data Science interviews. It involves identifying and removing duplicate records from a table that stores email addresses. So, if you want to know how to solve this problem,…
Exploratory data analysis (EDA) is a Data Science concept where we analyze a dataset to discover patterns, trends, and relationships within the data. It helps us better understand the information contained in the dataset and guides us in making informed…
The currency conversion rate or exchange rate is an important economic indicator affecting several sectors, such as import-export businesses, foreign investment and tourism. By analyzing past data and predicting future exchange rates, we can gain valuable insights to help stakeholders…
Lifetime value analysis calculates the expected value a customer/user will generate over the lifetime of their relationship with the company. So, if you want to know what Lifetime Value Analysis is and how a Data Science professional can help businesses…
The problem of employees earning more than their managers is a popular SQL question asked in Data Science interviews. It involves querying a database to identify employees earning a higher salary than their respective managers. So, if you want to…
H&M is a Sweden-based multinational clothing company that focuses on fast fashion clothing for men, women, teens and children. It is one of GenZ’s favourite brands worldwide. There are many ways in which H&M uses data science to keep its…
In today’s fast-paced e-commerce industry, fast and efficient order delivery is crucial to business success. To ensure seamless order fulfilment, businesses often partner with courier companies to ship their products to customers. However, managing the charges collected by these courier…
Demographic Analysis refers to analyzing data about a population, such as age, gender, income, education, and location, to better understand its characteristics, behaviour, and preferences. So, if you want to know what Demographic Analysis is and how a Data Science…
The problem of Customers Who Never Order is a common SQL question asked in Data Science interviews. It means querying a database to identify customers who have never placed an order. So, if you want to know how to solve…
A Business Analyst is a Data Science professional who uses the data generated by the business to analyze and forecast the performance of a business, design and manage the creation of new data products, and bridge the gap between business stakeholders and…
Loan Approval Prediction is one of the problems that Machine Learning has solved in fintech businesses like banks and financial institutions. Loan approval prediction means using credit history data of the loan applicants and algorithms to build an intelligent system…
Analyzing and Forecasting Time Series is a statistical technique used to analyze data points collected over time. It helps companies identify patterns and trends in the data, which can be used to make informed decisions about the future. So, if…
Finding uncommon words in two sentences is one of the problems in coding interviews. In this problem, you have to find all the words that appear only once in two given sentences. So, if you want to know how to…
In Machine Learning, Polynomial Regression is a regression algorithm used to model nonlinear relationships between input features and output labels. So, if you are new to Machine Learning and want to know how the Polynomial Regression algorithm works, this article…
Stock Market Performance Analysis involves calculating moving averages, measuring volatility, conducting correlation analysis and analyzing various aspects of the stock market to gain a deeper understanding of the factors that affect stock prices and the relationships between the stock prices…
Cohort Analysis is one of the real-time concepts that every Data Science professional should know. Cohort Analysis helps companies analyze and understand the behaviour of their customers or users over time. So, if you want to know what Cohort Analysis…
Transpose Matrix is one of the popular problems in coding interviews. This problem requires you to manipulate a matrix by flipping its rows and columns. So, if you want to know how to transpose a matrix, this article is for…
Support Vector Machine (SVM) is a popular supervised learning algorithm used for classification and regression problems in Machine Learning. The basic idea behind SVM is to find a decision boundary that separates data points of different classes with the maximum…
Customer lifetime value analysis is used to estimate the total value of customers to the business over the lifetime of their relationship. It helps businesses make data-driven decisions on how to allocate their resources and improve their customer relationships. So,…
A/B Testing is one of the real-world concepts you need to know as a Data Science professional. A/B Tests help businesses compare two different options or strategies, such as two different advertisements or website designs, to see which performs better…
Buddy Strings is a popular problem in coding interviews. In this problem, you need to check if it is possible to get two strings, different from exactly a swap of two letters. So, if you want to learn how to…
In Machine Learning, the Random Forest algorithm is an ensemble learning method for classification and regression tasks. It is composed of multiple decision trees, where each tree is built on a random subset of the input features and a random…
Not everyone is always available on social media all the time. Some people limit the use of social media during festive seasons, while some avoid social media during their examinations. So, as Content creators, we need to decide when to…
Content-based filtering and collaborative filtering are two common approaches used in recommendation systems. A Content-based recommendation system uses information about the recommended item, while a collaborative system uses user behaviour data. So, if you want to know how these approaches…
Binary Search is an algorithm used to find an element in a sorted array by repeatedly dividing the search interval in half. In coding interviews, there is always a question on any search algorithm. So, if you want to learn…
In Machine Learning, Decision Tree is an algorithm that works like a flowchart or tree structure to make decisions based on input data. It starts with a question or condition and then follows different branches based on the answers to…
Retail price optimization involves determining the optimal selling price for products or services to maximize revenue and profit. So, if you want to learn how to use machine learning for the retail price optimization task, this article is for you.…
As Data Science beginners, sometimes we need to find datasets to work on projects. And as Data Science professionals, we sometimes need to find synthetic data based on the problem we are working on. So, if you want to know…
Set Mismatch is a popular question in coding interviews. In this problem, you need to find two numbers in an array that are missing or duplicated. So, if you want to know how to solve this problem using Python, this…
In Machine Learning, Naive Bayes is an algorithm that uses probabilities to make predictions. It is used for classification problems, where the goal is to predict the class an input belongs to. So, if you are new to Machine Learning…
Store sales and profit analysis is the task of analyzing the performance of a retail store in terms of its sales and profits. It helps businesses identify areas for improvement and make data-driven decisions to optimize their operations, pricing, marketing,…
Data-driven business models are the types of business models in which data is used for overall business operations. Most big tech product companies you hear about today have data-driven business models. So, if you want to understand what Data-Driven business models…
User Funnel Analysis is a way to analyze the flow of users on a website or an application. It helps analyze the conversion rate on each page visited by the users. So, if you want to know how to analyze…
In Machine Learning, Logistic Regression is a statistical model used for binary classification problems. It is used to predict the probability of an outcome based on the input features. It uses a sigmoid function to map the input features to…
The supply Chain is the network of production and logistics involved in producing and delivering goods to customers. And Supply Chain Analysis means analyzing various components of a Supply Chain to understand how to improve the effectiveness of the Supply…
The first step to getting a job today is to create and upload your resume. The way you used to create a resume some years ago is very different from the way companies want you to. So, if you want…
Reorder routes to make all paths lead to the city zero is a popular question in coding interviews. In this problem, you need to reorder the connections between the cities so that all paths lead to the city labelled as…
In Machine Learning, Linear Regression Algorithm is a statistical technique for calculating the value of a dependent variable based on the value of an independent variable. The goal of linear regression is to find the best-fit line that describes the…
A chatbot is a computer program that understands the intent of your query to answer with a solution. Chatbots are the most popular applications of Natural Language Processing in the industry. So, if you want to build an end-to-end chatbot,…
Myntra is one of the fashion e-commerce platforms in India. It is known for its trending collection of clothing, footwear, and accessories. Myntra uses a lot of Data Science techniques that make it different from other e-commerce platforms. So, if…
Message Encryption means keeping the message secret. In simple words, Message Encryption means putting the message in a secret box that only the receiver can open. If you want to learn how to encrypt messages using Python, this article is…
You must have observed that some applications on your smartphone don’t charge anything but are still profitable. Most companies that are free for a user make money with the data of the users. So, if you want to know how…
Developing Data ETL pipelines is one of the most valuable skills for Data Engineers. Data ETL is a process where data is extracted from a place, then the data is transformed in some way, and then data is loaded into…
The price of a product is one of the most important decisions for every business. It means setting a price for a product or service that is affordable to the target consumer and helps the business remain profitable in the…
The problem of detecting capital is a popular question in coding interviews. A word is written in capital letters if all of its letters are uppercase or if only the first letter is uppercase and the rest are lowercase. In…
Data Preparation is one of the most valuable skills every Data Science professional should have. One challenging task in data preparation is filling in missing values and deciding what measure you should consider between mean, median, and mode to fill…
Accelerometer is a device used to measure the acceleration or vibrations of a motion. The data provided by an accelerometer is three-dimensional and can be used in data-driven applications for solving problems like fall detection and health monitoring. So, if…
Supply Chain Management is the management of the flow of goods from raw materials to the final products and ensuring the delivery of the demanded products at the right place and at the right time. There are many use cases…
The problem of checking perfect numbers is a popular coding interview question. A perfect number is a positive integer equal to the sum of its positive divisors, excluding the number itself. In this problem, you will be given an integer,…
There are many types of questions you are asked for a job interview to understand your behaviour, technical skills, domain expertise, and how you will use your skills in solving real-time business problems. If you are looking for some of…
Employee attrition analysis means analyzing the behaviour of the employees who left your organization and comparing them with the current employees in your organization. It helps in finding which employee may leave soon. So, if you want to learn how…
LinkedIn is a social media platform for working professionals. People use LinkedIn to know about industry trends, job seeking, and growing their professional network. So, if you want to know how LinkedIn uses Data Science, this article is for you.…
The problem of relative ranks is a popular coding interview question. Here you will be given an array of integers representing the scores of athletes, assuming that the athletes with the 1st rank will have the highest score, and you…
Customer Retention means keeping the existing customers loyal towards the business. Retaining customers helps businesses in repeat orders, profitability, and goodwill which attracts more new customers. Today businesses use your data to formulate strategies to retain you as a customer.…
Demand and Supply Analysis is a valuable concept used to analyze the relationship between the quantity demanded and the quantity supplied. If you want to learn about demand and supply analysis and how it helps businesses in the real world,…
Twitter is one of the popular social media applications today. It is known for expressing views in a short piece of text which makes it different from other social media applications. So, if you want to understand how Twitter uses…
The problem of License key formatting is a popular coding interview question. Here you will be given a license key, and you need to reformat the key such that each group contains the same characters except the first group. So,…
Advertising is a marketing strategy that businesses use to promote their products and services. Advertising is not a new concept, it has been used by businesses of all sizes to get more customers over the years. So, if you want…
A recession is an economic situation that arrives when the circulation of money in the economy is low for two consecutive quarters. When the circulation of money is low, it means people are not spending money in the market. When…
Mastercard Inc. is the second largest payment processing corporation worldwide. It offers a range of payment and financial services to businesses and customers worldwide. So, if you want to know how Mastercard uses Data Science for its business, this article…
The problem of constructing a rectangle is a popular coding interview question. Here you will be given the area of a rectangle, and you need to return the height and width of the rectangle to solve this problem. So if…
There is a huge competition in the Data Science field, making it hard to mention unique projects on your resume. An interviewer goes through hundreds of projects every day, so it’s essential to mention unique projects on your resume so…
Topic Modelling means assigning topic labels to a collection of text documents. The goal of topic modelling is to identify topics present in the text documents. So, if you want to learn how to perform topic modelling, this article is…
Samsung is a South Korean multinomial conglomerate. It is known for its midrange and flagship smartphone collection worldwide. There are many ways how Samsung uses Data Science and Machine Learning techniques in its technology and business. So, if you want…
The problem of max consecutive ones is a popular coding interview question. Here you will be given a binary array, and you need to return the maximum number of consecutive 1s in the array. So, if you want to know…
Your LinkedIn profile is like an online resume. Today, companies do have a look at your LinkedIn profile before hiring you, especially in the tech industry. So, if you want to know how to create a LinkedIn profile to get…
Text emotions classification is the problem of assigning emotion to a text by understanding the context and the emotion behind the text. One real-world example is the keyboard of an iPhone that recommends the most relevant emoji by understanding the…
There have been many innovations in the healthcare industry since the outbreak of covid in 2019. The use of data in the healthcare industry has helped healthcare professionals get more accurate information about the medical conditions of patients. In this…
The problem of hamming distance between two integers is a popular coding interview question. Here you will be given two integers, and you need to calculate the number of positions at which the corresponding bits of the integers are different. So, if…
A Data Engineer is a data professional who collects, transforms, and process data according to the need of the business. Data Engineering is one of the highest-paying jobs in Data Science. So, if you are looking for a roadmap to…
App user segmentation is the task of grouping users based on how they engage with the app. It helps find retained users, find the user segment for a marketing campaign, and solve many other business problems where you need to…
The problem of the Repeated Substring Pattern is a popular coding interview question. Here you will be given a string, and you need to check if the given string can be formed by repeating a substring of the string. So,…
Data Analysis means analyzing data to gather valuable insights that can be used in decision-making and solving various business problems. The role of a Data Analyst is one of the best jobs to start your journey as a Data Science…
Algorithmic Trading is the use of algorithms in the financial market to make trading decisions. JP Morgan Chase & Co. is one of the businesses that use Algorithmic Trading for investment decisions. So, if you want to learn how to…
Tata Consultancy Services is an Indian multinational corporation that provides IT, data processing and consulting services. TCS is one of the highly active recruiters in India for its IT and data processing services. So, if you want to know how…
The problem of assigning cookies is a popular coding interview question. Here you will be given a list of children with their greed factors and a list of cookies with their sizes. To solve this problem, you need to assign…
The decision Tree algorithm is a supervised Machine Learning algorithm used for both classification and regression problems. It is based on a sequential decision process. If you want to know everything about the Decision Tree algorithm, this article is for…
Ads Click Through Rate is the ratio of how many users clicked on your ad to how many users viewed your ad. For example, 5 out of 100 users click on the ad while watching a youtube video. So, in…
Dream11 is a fantasy sports platform where you can create a virtual team of real sports players and participate in fantasy games. Dream11 has many use cases of Data Science that make it different from all other fantasy gaming applications.…
Calculating the distance between two locations helps companies like Swiggy and Zomato calculate the delivery time for an order. If you want to learn how to calculate the distance between two locations by using their latitude and longitude points, this…
A box plot is a data visualization technique for analyzing the distribution and patterns of numerical data points. Understanding data visualization graphs is not easy for everyone. So if you want to know how to analyze a box plot, this…
A career aspirations survey gathers information about the goals and aspirations of people in their careers. It includes questions based on long-term goals, preferred work environment, interests, and values. So, if you want to learn how to analyze the information…
JP Morgan Chase & Co. is an American multinational investment banking and financial services company. Like other successful financial services businesses, Data Science is one of the reasons behind the success of JP Morgan Chase. So, if you want to…
The problem of counting the number of segments in a string is a popular coding interview question. Here you will be given a string, and you have to count the number of non-space characters in the string. So, if you…
A scatter plot is a data visualization technique to analyze relationships between two variables and find outliers. Understanding data visualization graphs is not easy for everyone. So if you want to know how to analyze a scatter plot, this article…
Food Delivery services like Zomato and Swiggy need to show the accurate time it will take to deliver your order to keep transparency with their customers. These companies use Machine Learning algorithms to predict the food delivery time based on…
Zomato is an Indian restaurant aggregator and food delivery company. It is known for faster delivery of food from anywhere in your city. Like every successful business today, Data Science is one of the reasons behind the success of Zomato.…
The problem of finding the third maximum number is a popular coding interview question. Here you will be given an array, and you have to return the third largest number in the array as an output. So, if you want…
Getting a Data Science job is hard for freshers without any work experience. So what should you do as a fresher? As a fresher, you can focus on entry-level job roles in Data Science to get experience. In this article,…
The Social Progress Index (SPI) is a measure that gives an understanding of social progress globally. It helps in understanding how much countries care about the overall development of the citizens. If you want to know how to analyze the…
Nykaa is one of the popular e-commerce platforms in India. It is known for beauty, wellness, and fashion products. Like every successful business today, Data Science is one of the reasons behind the success of Nykaa. So, if you want…
The FizzBuzz problem is a popular coding interview question. Here you need to check if a number is divisible by 3 and 5 and return the output based on some conditions. If you want to know how to solve the…
As a Data Science beginner, you need to work on various projects to get your desired job. Working on projects is valuable because your projects are the highlights of your resume when you are a fresher. Once you get experienced,…
Recommendation Systems are one of the popular applications of Data Science for every e-commerce platform. When it comes to fashion, an e-commerce platform wants to recommend the most trending fashion to its users for higher sales and engagement. Myntra is…
Spotify is an audio streaming platform with over 456 million monthly active users. Spotify is one of the best examples of companies that use Data Science and Machine Learning techniques to provide its services. So, if you want to know…
The problem of finding the first unique character in a string is a popular coding interview question. Here you will be given a string, and you have to return the index of the first non-repeating character in the string as…
One of the reasons behind the success of many businesses is how they use the data generated by their business and customers in decision-making and improving their products and services. So, if you want to know how companies worldwide use…
A recommendation system is a popular application of Data Science that recommends personalized content based on the users’ interests. Almost all the popular websites you visit today use a recommendation system. As the name suggests, a job recommendation system is…
Paytm is a digital payments and financial services company. Just like other fintech companies, data is the ABC for Paytm. So, if you want to know how Paytm uses Data Science, this article is for you. In this article, I…
Validating perfect squares is a popular coding interview question. Here you will be given an integer, and you have to check if the integer is a perfect square. So, if you want to learn how to validate perfect squares, this…
In Machine Learning, we use data and algorithms to build intelligent systems. In the next ten years, you will explore many new high-paying jobs that require you to know about Machine Learning. So the time you will spend today learning…
Banks and credit card companies calculate your credit score to determine your creditworthiness. It helps banks and credit card companies immediately to issue loans to customers with good creditworthiness. Today banks and credit card companies use Machine Learning algorithms to…
Youtube is a popular social media platform where we can upload videos to educate or entertain an audience of every kind. There are many areas where Youtube uses Data Science for its business and to provide a better user experience.…
Counting Bits is a popular coding interview question. Here you will be given an integer, and you have to return an array of length (length of the integer + 1) by counting the number of 1’s in the binary representation…
Data Science is one of the highest-paying career options today. There are many jobs in the Data Science field you can prepare for as a fresher or an experienced professional. So, if you want to know about the highest-paying career…
Consumer Complaint Classification means classifying the nature of the complaint reported by the consumer. It is helpful for consumer care departments as they receive thousands of complaints daily, so classifying them helps identify complaints that need to be solved first…
Apple is one of the most loved tech brands worldwide. Most of the products of Apple are known for productivity, user experience, and privacy. There are many ways how Apple is using Data Science and Machine Learning in its products…
The problem of the power of three is a popular coding interview question. Here we need to check whether the input value is the power of three or not. So, if you want to know how to solve this problem…
A Business Analyst is a Data Science professional who analyzes and forecasts business trends, understands and analyzes business strategies, and performs budgeting and financial analysis of the business. If you are aiming for the role of a Business Analyst and…
Every sports event generates a lot of data which we can use to analyze the performance of players, teams, and many highlights of the game. As the ICC Men’s T20 world cup has just finished, it has generated a lot…
Netflix is a subscription-based streaming platform that allows users to watch movies and TV shows without advertisements. One of the reasons behind the popularity of Netflix is the way Data Science is used at Netflix to improve its services as…
Moving zeroes is one of the popular questions in coding interviews. Here we need to move all the 0’s in an array to the end of the array while keeping the order of non-zero elements the same. So, if you…
Data Science is one of the highest-paying career options today. But many freshers find it hard to get their first Data Science job. Companies need experience, freshers look for work to become experienced, and the loop continues. So how to…
A/B Testing means analyzing two marketing strategies to choose the best marketing strategy that can convert more traffic into sales (or more traffic into your desired goal) effectively and efficiently. A/B testing is one of the valuable concepts that every…
Like all other jobs, the salary of a Machine Learning Engineer depends on many factors like work experience, living standards in the country, and demand for Machine Learning Engineers in the country. Out of all factors, work experience is the most important…
Grammarly is one of the best examples of applications that use Machine Learning for their business. Grammarly helps write a text for any purpose and uses Machine Learning to make that text more readable and engaging. So, if you want…
Ugly numbers are the numbers whose prime factors are limited to 2, 3, and 5. Finding the Ugly number is a popular question in coding interviews. Here you need to check if a number is an ugly number or not.…
Data Science is one of the highest-paying career options today. Data Science is not like Engineering, where you need an engineering degree with a computer science background. Data Science is a skill that helps companies make decisions with data. So…
Twitter is one of the popular social media applications where people share what they feel in a limited number of words. Twitter is popular but not in the stock market. As Twitter is being delisted from the New York stock…
Like all other jobs, the salary of a Data Scientist also depends on many factors like work experience, living standards in the country, and the demand for Data Science professionals in the country. Out of all these factors, work experience…
Instagram is one of the most popular social media applications today. Just like every business today, there are many areas where Instagram uses Data Science. So, if you want to know how Instagram uses Data Science, this article is for…
The problem of adding digits is a popular coding interview question. Here we need to add all the digits of the integer till we get to a single-digit integer. So, if you want to know how to solve this problem using…
Data Science is one of the highest-paying career options today. There are many free and paid platforms today where you can learn everything about Data Science. So, if you are looking for the platforms you should follow to learn Data…
Salary differs according to the job profile of the person. But generally, it’s the working experience that determines the salary. Salary prediction is a popular problem among the Data Science community for complete beginners. So, if you are a beginner…
With the increase in competition in the computer science field, coding interviews are getting tougher. It is now hard for freshers to get their first job in their dream companies. Although there are many free resources on the internet, if you are…
Google is the most used search engine worldwide. One of the reasons behind the success of Google as a search engine is the way Google uses Data Science to improve its search results for every user. So, if you want…
The problem of Power of Two is a popular coding interview question. Here we need to check whether the input value is the power of two or not. So, if you want to know how to solve this problem using Python, this…
Screen Time Analysis lets you know how much time you spend on what kind of applications and websites using your device. And screen time analysis gives a visual report of the same. So, if you want to learn how to…
A time series data is a sequence of data collected over an interval of time. When we work on time series data to analyze and find patterns, it is known as Time Series Analysis and forecasting. So, if you are looking for…
Amazon is one of the largest e-commerce platforms today. One of the reasons behind the success of Amazon is the way Amazon uses Data Science for its business. So, if you want to know how Amazon uses Data Science, this article is for…
The duplicate values problem is a popular coding interview question. Here we need to check if any value appears at least twice in an array. So, if you want to know how to solve this problem using Python, this article…
Data Scientists and Data Analysts are Data Science professionals with some skills in common and different responsibilities. So, if you don’t know how a Data Scientist differs from a Data Analyst, this article is for you. In this article, I…
In Data Science, weather forecasting is an application of Time Series Forecasting where we use time-series data and algorithms to make forecasts for a given time. If you want to learn how to forecast the weather using your Data Science…
Kaggle is a Data Science community owned by Google. You will find thousands of datasets and case studies on Kaggle to improve your Data Science skills. So, if you are looking for some case studies to practice your Data Science…
Tinder is an online dating and geosocial platform. Many users find Tinder helpful for meeting new people for friendships and relationships. Just like every business today, Tinder also use Data Science for its business. So, if you want to know how Tinder uses…
The majority element problem is one of the popular coding interview questions. Here we need to find the element that appears more than the other element in an array. So, if you want to know how to solve this problem,…
Data Scientists and Data Engineers are Data Science professionals with some skills in common and different responsibilities. If you don’t know how a Data Scientist differs from a Data Engineer, this article is for you. In this article, I will…
Flipkart introduces a sale every year during the festive season in India. This sale is known as the Big Billion Days sale. The focus of the sale is on electronics of every kind, including flagship smartphones. So, if you want…
There are many tools Data Science professionals use daily to solve business problems with data. To become a Data Science professional, you need to learn all the necessary tools. So, if you want to know about the tools and software…
Uber is an American mobility-as-a-service provider that currently operates in 72 countries. Uber is also among the companies that use data and algorithms as a necessity for their business. So, if you want to know how Uber uses Data Science, this…
The excel sheet column title is one of the popular coding interview questions. Here we need to return the corresponding column title of an integer as displayed in an excel sheet. So, if you want to know how to solve…
Data Scientists and Machine Learning Engineers are Data Science professionals with different skillset and responsibilities. So, if you are confused about how a Data Scientist differs from a Machine Learning Engineer, this article is for you. In this article, I…
Credit card clustering is the task of grouping credit card holders based on their buying habits, credit limits, and many other financial factors. If you want to learn how to use clustering analysis to group credit card holders, this article…
In Machine Learning, we use data and algorithms to build intelligent systems. If you are preparing for a job based on Machine Learning and confused about what kind of projects you should work on to mention in your resume, this…
In Machine Learning, we use data and algorithms to build intelligent systems. It doesn’t matter if you want to become a Machine Learning Engineer, Data Scientist, or Data Analyst. If you want any job in the Data Science field, you need…
The Single Number problem is one of the popular coding interview questions. Here you will be given an array of integers where each item appears twice except for one that we need to find. So, if you want to know how…
Regression analysis is a machine learning technique used to predict continuous values. Once you have trained a regression model to predict Continuous values based on the problem you are solving, it is valuable to evaluate the model’s performance. So, if…
A diamond is one of the most expensive stones. The price of diamonds varies irrespective of the size because of the factors affecting the price of a diamond. So, if you want to learn how to use your Data Science…
Python is one of the most popular programming languages today. The popularity and beginner-friendly nature of Python’s syntax have made it accepted in almost every domain worldwide. To get your first job as a Python developer, you need to work…
Python is one of the most popular programming languages today. It is preferred by Data Science professionals and Web Developers worldwide. There are a lot of free and paid courses on the internet to learn Python. So, if you are…
Palindrome words are words that are read the same backward as forward. For example, mom, dad, and madam are palindrome words. Validating palindrome words is one of the popular coding interview questions. So, if you want to learn how to…
Data Science is the combination of data mining and computer science. As the use of data has increased in every domain, Data Science is one of the highest-paying career options today. If you want to learn Data Science and looking…
Apple iPhones are among the top-selling smartphones worldwide. There is huge competition among smartphone brands in India, where you can get the latest technology in a smartphone at half the price of an iPhone. Still, there are high sales of iPhones…
Instagram is one of the most popular social media apps today. It contains valuable features for both content creators and consumers. There are many areas where Instagram uses Data Science to make it more helpful to its users. So, if…
Data Science is one of the highest-paid career options today. If you want to learn Data Science, you don’t need to spend a lot as there are so many free resources on the internet to learn Data Science. So, if…
Finding the best time to buy and sell stocks is one of the popular coding interview questions. Here we need to return the maximum profit we can achieve from buying and selling a stock. So, if you want to learn…
A recommendation system is a popular application of Data Science. Almost all the popular websites you visit use recommendation systems. As the name suggests, a news recommendation system is an application that recommends news articles based on the news a user is…
Business Forecasting is one of the popular applications of Data Science. In Business Forecasting, we use the data generated by the business to forecast the future sales, expenditure, or revenue of the company. If you are looking for some project…
In Machine Learning, we use data and algorithms to build intelligent systems. It doesn’t matter if you want to become a Machine Learning Engineer, Data Scientist, or Data Analyst. If you want any job in the Data Science field, you…
Pascal’s Triangle is one of the popular coding interview questions. It is asked in the coding interviews by FAANG companies many times. In Pascal’s Triangle problem, we need to return the number of rows of a Pascal’s Triangle. So, if…
Data Analysts and Business Analysts are Data Science professionals with different skillset and responsibilities. Both these professions are not completely different. So, if you are confused about how Business Analysts and Data Analysts are different, this article is for you.…
Business Forecasting is one of the applications of Time Series Forecasting. In Business Forecasting, we aim to forecast future sales, expenditure, or revenue by using the historical Time Series data generated by the business. So, if you want to learn…
A Business Analyst is a Data Science professional who uses the data generated by the business to analyze and forecast the performance of a business. If you are aiming for the role of a Business Analyst and want to know what…
MySQL is an open-source relational database management system. Knowing MySQL will always help you in your career in any field of computer science. So, if you are a MacBook user and want to know how to install MySQL on your…
Climbing Stairs is one of the popular coding interview questions. It is asked in the coding interviews by FAANG companies many times. In the climbing stairs problem, you need to count the number of distinct ways to climb to the…
Python is one of the most popular programming languages today. It is preferred by Data Science professionals and Web Developers worldwide. If you are someone who has just started learning Python or looking for a step-by-step roadmap to learn Python,…
Natural Language Processing (NLP) is a subset of Artificial Intelligence where we aim to train computers to understand human languages. Some real-world applications of NLP are chatbots, Siri, and Google Translator. While working on any problem based on NLP, we…
Working on Data Science projects is one of the best ways to improve your Data Science skills. It not only boosts your skills but also strengthens your resume. So, if you are a Data Science beginner and want to know how to…
A Data Engineer is a Data Science professional who collects, transforms, and process data according to the need of the business. Data Engineering is one of the most in-demand Data Science jobs. So, if you are aiming for the position…
Finding the square root of a number is asked in the coding interviews by FAANG companies many times. A square root is a number that produces the specified value when multiplied by itself. For example, 5×5 gives 25, so the square root…
Python has a vast collection of built-in modules we don’t learn while learning the fundamentals of Python. As a beginner, you don’t need to learn all the modules to become an expert in Python. So, if you want to know…
Password Strength Checker is an application that checks how strong a password is. Some popular password strength meters use machine learning algorithms to predict the strength of your password. So, if you want to learn how to use machine learning…
Apple is one of the most loved smartphone brands worldwide. Apple’s iPhone is known for productivity, user experience, and privacy. There are a lot of areas where Apple uses Machine Learning to improve the overall user experience of an iPhone.…
Data Science is a combination of data mining and computer science. It is one of the most in-demand career options in the industry. Still, many Data Science freshers struggle to get their first job as most companies look for experienced Data…
The Plus One problem is one of the popular coding interview questions. It is asked in the coding interviews by companies like FAANG many times. Here you need to increment the last digit of an array by one and return…
A Python Developer is one of the developers in the development team who works at the backend to create, design, debug and deploy applications using Python. If you are good at Python and looking for your first job as a…
The rent of a house depends on a lot of factors. With appropriate data and Machine Learning techniques, many real estate platforms find the housing options according to the customer’s budget. So, if you want to learn how to use…
Deep Learning is a subset of Machine Learning where we need a large amount of data to train powerful models capable of finding relationships in the data to solve complex problems. So, if you are looking for courses that will…
Statistics means collecting, organizing, analyzing, interpreting, and presenting data. Statistics is one of the most valuable concepts for Data Science. A person with statistical background is preferred for the role of a Data Science professional. So, if you are looking…
Removing duplicate values from a sorted array is one of the popular coding interview questions. It is asked in coding interviews by companies like FAANG many times. Here you need to return an array of unique values by removing all the…