How to Become a Job Ready Data Scientist

Many freshers don’t feel confident enough to apply for their first job as a data scientist. This happens when you follow the trends but forget the fundamentals. To become a job-ready data scientist, you need to practice solving real-world problems that companies expect data scientists to solve 90% of the time. So, in this article, I’ll take you through some common problems you should solve to become a job-ready data scientist.

Become a Job-Ready Data Scientist: Solve These Problems

Below are the most common problems that companies expect data scientists to solve 90% of the time. Solving such problems will help you become a job-ready data scientist.

Data Cleaning & Preprocessing

Garbage in, garbage out. Poor data quality leads to unreliable insights and inaccurate models. A data scientist spends a significant amount of time on this because it’s the foundation of any successful data project.

Let’s take the example of Walmart. Walmart faces significant data cleaning challenges due to its massive data volume and variety. Such as:

1. Missing customer purchase history entries, caused by system errors or incomplete transactions, require imputation using techniques like averaging similar customer segments or predictive modeling.  
2. Inconsistent product names and categories across stores necessitate standardization for accurate aggregation and analysis.  
3. Duplicate customer records, resulting from multiple registrations or errors, must be identified and removed to avoid skewed analysis.
4. Finally, outliers in sales data, often due to promotions or seasonality, need careful handling to prevent distortion of overall analysis and predictive model accuracy.

Here are some resources that will help you master data cleaning & preprocessing:

1. Course: Process Data from Dirty to Clean
2. Project 1: B2B Courier Charges Accuracy Analysis
3. Project 2: Building a Data Preprocessing Pipeline

Feature Engineering & Selection

Feature engineering involves creating new features from existing ones to improve the performance of machine learning models. Selecting features is then the next step which involves choosing the most relevant features for the model.

Let’s take an example of feature engineering at Airbnb. Let’s say Airbnb aims to predict listing popularity using data like location, price, amenities, and reviews. A data scientist in this case might engineer features such as distance to the city centre or average rating of similar local listings. Then, we can identify the strongest predictors of popularity among these features, both original and engineered, which will result in a more robust and accurate recommendation system.

Here are some resources that will help you master feature engineering and selection:

1. Project 1: Dynamic Pricing Strategy
2. Project 2: Food Delivery Cost & Profitability Analysis

Predictive Modeling & Optimization

This involves building statistical or machine learning models to predict future outcomes. It includes selecting appropriate model types (e.g., linear regression, decision trees, neural networks), training the models on data, evaluating their performance, and tuning their parameters to optimize accuracy and generalization.

Let’s take the example of JP Morgan. Let’s say JP Morgan aims to forecast trading volumes for the next quarter. A data scientist would build a predictive model using historical trading data, market volatility indicators, and economic forecasts (e.g., interest rates, GDP growth). They would evaluate the model’s accuracy using metrics like Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE) and optimize its parameters (e.g., coefficients in a regression model, or the architecture of a neural network) to generate the most reliable trading volume forecast.

Here are some resources that will help you master predictive modelling and optimization:

1. Project 1: Building a Hybrid Machine Learning Model
2. Project 2: End to End Predictive Model

A/B Testing & Experimentation

A/B testing is a method for comparing two versions of something (e.g., a website design, or a marketing email) to determine which performs better. It involves creating two groups (A and B), exposing them to the different versions, and analyzing the results to see which version drives desired outcomes (e.g., higher click-through rates, and increased conversions).

Let’s take an example of Google to understand the use of A/B Testing. Let’s say Google wants to improve the click-through rate on their search results pages. They create two versions of the search results layout (A and B), perhaps one with larger snippets and another with more prominent ad placements. A data scientist designs an A/B test, randomly assigning users to either version when they perform a search. By analyzing metrics like click-through rate on organic results, click-through rate on ads, and time spent on the search results page, the data scientist can determine which layout is more effective at driving user engagement and revenue.

Here are some resources that will help you master A/B Testing:

1. Project: Hypothesis Testing
2. Course: Marketing Analytics and Measurement

Recommendation Systems

Recommendation systems personalize user experience, increase engagement, and drive sales. Data scientists develop and optimize these systems using various techniques like collaborative filtering and content-based filtering.

Let’s say a music streaming platform wants to improve its music recommendations. A data scientist would build a recommendation system that analyzes users’ listening history, genre preferences, and interactions with other users. This system would then suggest personalized playlists and artists, to increase user satisfaction and time spent on the platform.

Here are some resources that will help you master Recommendation Systems:

1. Project 1: Music Recommendation System using Spotify API
2. Project 2: Fashion Recommendation System using Image Features

Data-Driven Decision Making & Business Intelligence

This involves using data analysis and insights to inform business strategies and decisions. It includes identifying key performance indicators (KPIs), creating dashboards and reports, and communicating findings to stakeholders.

Let’s say a retail chain wants to understand why its online sales have been declining. A data scientist will analyze sales data, website traffic, and customer reviews. Let’s say, the data scientist identifies that slow shipping times and a cumbersome checkout process are contributing factors. The business can then implement changes to improve shipping and simplify the checkout process, leading to a rebound in online sales.

Here are some resources that will help you master Data-Driven Decision Making & Business Intelligence:

1. Project 1: Netflix Content Strategy Analysis
2. Project 2: Creating a Mutual Fund Investment Plan

Summary

So, here are the most common problems that companies expect data scientists to solve 90% of the time, which will help you become a job-ready data scientist:

1. Data Cleaning & Preprocessing
2. Feature Engineering & Selection
3. Predictive Modeling & Optimization
4. A/B Testing & Experimentation
5. Recommendation Systems
6. Data-Driven Decision Making & Business Intelligence

I hope you liked this article on how to become a job-ready Data Scientist. Feel free to ask valuable questions in the comments section below. You can follow me on Instagram for many more resources.