A Beginner-to-Upper Intermediate Data Science Roadmap for 2025 #8: Feature Engineering

A Step-by-Step Roadmap to Start a Data Science Career In 2025

In the Seventh article of the series A Beginner-to-Upper Intermediate Data Science Roadmap for 2025, you will learn the fundamentals of Data Cleaning & Preprocessing for data science.

Feature engineering is one of the most important parts of a data scientist’s day. It’s something you’ll do regularly and it's an essential step before training any machine learning model.

Being able to clean your data effectively and engineering the features effectively will result in better results with less effort and computational power. I believe that the more you know, the better you will understand the data, which will help you to produce better results and be effective at work.

In this article, I’ll share essential courses to help you master feature engineering. I’ll also include additional resources for further learning, should you want to dive deeper into the topic.

This article is the Seventh article in the ongoing series of A Beginner-to-Upper Intermediate Data Science Roadmap for 2025:

• Introduction to Data Science & Data Methodology (Published!)

• Mathematics for Data Science (Published!)

• Python Fundamentals (Published!)

• Python for Data Science (Published!)

• Software Engineering Basics (Published!)

• Database & SQL Fundamentals (Published!)

• Data Cleaning & Preprocessing (Published!)

• Feature Engineering (You are here!)

• Mastering Machine Learning (Coming Soon!)

• Deep Learning Fundamentals (Coming Soon!)

• Generative AI & Large Language Models (LLMs) Fundamentals (Coming Soon!)

• Machine Learning Operations (MLOps) (Coming Soon!)

• Building Your Data Science Portfolio (Coming Soon!)

• Getting Ready for the Market (Coming Soon!)

Whether you’re a recent graduate or a professional looking to make a career change, Data Science and AI offer a wide range of exciting and lucrative opportunities.

In this series of articles, I will provide a comprehensive guide with a clear and actionable plan for building the skills and knowledge you need to succeed in this growing field. By following the steps outlined in this roadmap, you’ll be well on your way to a successful and rewarding career in Data Science and AI.

This roadmap will take you to an upper intermediate level, and you can land a job and start your career after finishing it. However, to go to an advanced level, you will need to take more in-depth courses, books, and research papers.

For each learning step, there will be compulsory material, optional material, and action points to make sure you put what you have learned into action. Also, there will be an estimated time for each of the learning resources in hours so you can calculate the time needed to finish this roadmap depending on your pace.

Table of Contents:

1. Feature Engineering For Machine Learning

2. Machine Learning with Imbalanced Data

3. Feature Selection for Machine Learning

4. Additional Resources

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