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Hand-picked resources: How to learn Data Science

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Elliot is a freelance Data Scientist with 7+ years of experience, including working with an Olympic team and creating an NBA salary model for Hazan Sports Management, an NBA sports agency. He co-founded a soccer computer vision company that secured $250,000 of funding. Elliot was a leader in creating computer …

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Unlock your potential with a truly multi-faceted mentor & let's bring the best bits of the Amazon-way into your day-to-day!! I can help you master data and financial analysis (Excel modelling, SQL, storytelling, etc.), strengthen your program management skills (mechanisms, Scrum & Agile, etc.), support you in becoming a thought …

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Shaksham has 3+ years of experience in data science. He is an experienced ML and NLP practitioner and is currently working as a Data Scientist at UBS, developing NLP/ML-based products. He has also done extensive research in ML and NLP, where most of his work was focused on using ML …

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Tanvi is a Senior Data Scientist working with United Health Group Inc and worked with Oracle India Private, Ltd. She contributed to the Research &Development of Oracle Cloud Infrastructure (OCI) that enables customers to build and run a wide range of applications. She has also contributed to Infiniti Research, the …

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Hi! I'm Dipanjan, you can call me DJ. I’ve worked as a lead data scientist with Fortune 500 companies & startups including Intel, Applied Materials, Red Hat / IBM and more. I have led data science teams as well as worked hands-on in data science and machine learning projects building …

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Ayan Sengupta is a Senior Machine Learning Scientist in a decade-leading startup in Tokyo. Previously he was an AI researcher in the Central Research Laboratory at NEC Corporation, Japan. He works in the domain of Deep Learning and Reinforcement Learning. Ayan specialises at: 1. Architecting state-of-the-art models specifically tailored for …


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Welcome to my mentoring page! My name is Nikola and I am an experienced researcher/engineer in the field of Natural Language Processing (NLP) and Machine Learning based in Switzerland. I have a PhD in NLP and over 8 years of experience in both research and the development of AI systems. …

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I'm a biology-researcher-turned-machine-learning-engineer. I landed my machine learning job in 4 months without a degree or experience. I've helped 60+ career changers in landing their dream jobs with a lifestyle that seemed impossible (unnecessarily hard) in the beginning of their journeys. However, my journey started way before that 4 months. …

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Best books to build Data Science understanding.

A well-written and thorough book can be an amazing path to build deeper understanding and also act as a handbook as you discover the internet's vast resources.

These are our and our experts top picks to get started building career-relevant skills.

Introduction to Statistical learning

Introduction to Statistical learning

ISL is a fundamental book and popular amongst undergrad and grad students for its clarity and simplicity with explaining concepts. The math required to understand the book is kept to a minimum, making it unique in its format.

Statistics for Data Scientists

Statistics for Data Scientists

Statistical methods are a key part of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.

Python Data Science Handbook

Python Data Science Handbook

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.

Head First Statistics

Head First Statistics

Whether you're a student, a professional, or just curious about statistical analysis, Head First's brain-friendly formula helps you get a firm grasp of statistics so you can understand key points and actually use them. Learn to present data visually with charts and plots; discover the difference between taking the average with mean, median, and mode, and why it's important; learn how to calculate probability and expectation; and much more.

R for Data Science

R for Data Science

This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science.

Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides.

Courses to deepen your Data Science skills.

These days, courses are no longer a sequence of videos. They are usually accompanied by projects and a learning community, keeping you accountable and on the path.

Our experts recommend these courses, from free selections to paid programs.

Fast.ai

Fast.ai

With the motto "making neural nets uncool again", fast.ai is a straight-to-the-point practical (and free!) course that is valued by Machine Learning enthusiasts and engineers worldwide. Fast.ai comes with a community, many practical projects and great content.

Machine Learning A-Z™

Machine Learning A-Z™

Kirill Eremenko's course on Udemy is a classic with almost a million (!) students worldwide. A-Z takes you from a bit of coding knowledge to making your own predictions and building ML models pretty swiftly. At prices between $10 - $20 it's also cheaper than many alternatives.

MIT Open: Linear Algebra

MIT Open: Linear Algebra

Math is the foundation of Machine Learning and much needed if you need to work on the inner logic of its systems. Senior engineers are encouraged to propose and submit their own papers – and getting your LinAlg back in order is a must for that.

Lex Fridman's MIT Deep Learning

Lex Fridman's MIT Deep Learning

Lex Fridman is the instructor of an immensely popular and fundamental Deep Learning course at MIT. Together with the other MIT AI courses, this can help polish your skills and get the foundations right.

Data Science A-Z

Data Science A-Z

Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!

Successfully perform all steps in a complex Data Science project, read statistical software output for created models and receive professional step-by-step coaching in the space of Data Science

Harvard Online Data Science

Harvard Online Data Science

To become an expert data scientist you need practice and experience. By completing this course you will get an opportunity to apply and gain knowledge in R data analysis. This final project will test your skills in data visualization, probability, inference and modeling, data wrangling, data organization, regression, and machine learning.