Data Science Roadmap Curated by the Programming Community

What you'll learn

Intro to programming using Python
Essential Mathematics: Linear algebra, statistics, and probability
Machine Learning
Data analysis and visualisation using Python
Computer Vision and NLP

overview

Data in all forms is one of the most important resources in the modern world. All tech giants such as Google, Facebook, and Netflix rely heavily on data provided by their users free of cost to enhance their product. It’s crucial for any medium to large size organization to figure out patterns and behaviours of usage to derive business insights and take meaningful decisions. It’s a fairly complex and challenging task which demands expertise in multiple domains. In return, it offers well respected jobs with generous compensation which makes Data Science a very lucrative career.

Python

The Hackr.io community chose the following python courses as overall favorites. You only need to choose one of the courses to start with, and you can review the curriculum for each before you get started.

Introduction to Computer Science and Programming Using Python - MIT (edx.org)

Complete Python Bootcamp (udemy.com)

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Python Tutorial for Beginners | Full Python Programming Course (youtube.com)

Python for Everybody Specialization (coursera.pxf.io)

Resource

Python vs. R vs. SAS

Essential Mathematics

The Math for Machine learning course gives you a foundation for the data science roadmap. We also recommend a few other tutorials to brush up on your skills. Those already comfortable with math for data science can skip this section.

Essential Math for Machine Learning: Python Edition (edx.org)

Mathematics For Computer Science - MIT (ocw.mit.edu)

Introduction to Discrete Mathematics for Computer Science (coursera.pxf.io)

Math for Programmers (manning.com)

Machine Learning

Machine Learning is not a requirement for Data Science, rather it can be seen as a supplement to it. Without Machine Learning, it becomes tedious to deal with the huge amount of data being generated.

Machine Learning (Stanford University) (coursera.pxf.io)

Intro to Machine Learning with PyTorch (udacity.com)

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Data Analysis and Visualization

The Python Data Science Handbook tutorial will teach you the nuts and bolts of data science, that is, data manipulation and visualization. It covers NumPy, Pandas, Matplotlib, and a fair bit of machine learning. The other course is an MIT Sloan online program, designed to teach managers how to create value through data.

Python Data Science Handbook (jakevdp.github.io)

Data Monetization Strategy: Creating Value through Data (getsmarter.sjv.io)

Computer Vision and NLP

Even though you have amassed a good bit of knowledge at this point, you can keep going and learn about even more applications of data science. CV and NLP are two such high-tech applications. Here are a few courses and tutorials to get you started.

OpenCV Tutorials (official docs) (docs.opencv.org)

Natural Language Processing with Deep Learning in Python (udemy.com)

Deep Learning: Advanced NLP and RNNs (click.linksynergy.com)

How to follow this roadmap?

This roadmap uses Python, an interpreted language taught in many introductory computer science courses.
Another popular language among data scientists is R. We suggest you start with Python and make the switch later if you feel the need. We have attached a comparison if you wish to check it out before committing to this roadmap.
You do not need to finish introductory Python courses. Once you are confident that you can program in Python, you can move on to the other tutorials. However, we suggest completing them if you have the time.
You do not need to finish the machine learning course before getting started with data science. You can take both courses simultaneously.