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How to learn Data Science

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Read the best books to build Data Science understanding.

A well-written and thorough book can be an amazing path to build deeper understanding of Data Science 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.

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.

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.

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.

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.

Work with one of our 159 Data Science mentors available

Self-teaching is a great tool, but working with an experienced mentor can accelerate your learnings by 10x.

Our mentors are hand-picked Data Science experts who will help you learn Data Science and guide you towards your goals.


With over 15 years of dedicated experience as a Machine Learning Engineer, I have honed my skills in developing comprehensive ML/AI solutions for prominent companies, tackling some of their most critical business challenges. My journey has taken me through leading projects such as developing the ML infrastructure for Airbnb’s Trust …

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Experience in Quant Finance, Engineering, Data Science, Startups. My background in applied mathematics and my experience in coding helped me with this challenging transition. Throughout my ten-year career, I have been involved in R&D as well as product development within both the financial and engineering industries. I can help you …

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**Why can I help you? ** I had been a software engineer at Microsoft for 6+ years, a research intern at NASA, and completed my PhD in Computer Science at Virginia Tech in 2023. I am currently a clinical assistant professor at Questrom School of Business at Boston University, where …

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Hello! I'm Venky, and I’m thrilled to share a bit about my journey and how it shapes my approach as a mentor. Currently, I lead a team of applied research scientists and machine learning engineers at Amazon AI in Sunnyvale, California. My work focuses on developing advanced deep learning and …

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​​Hi! I'm Sundeep, a Leader in Applied AI & Neuroscience with 16+ years of diverse international experience in USA, UK, France and India across leading organisations at Big Tech (Amazon Alexa AI), unicorn B2C startup (Swiggy), early-stage B2B SaaS startup (Docsumo) & Academia (Wellcome Trust Fellow at University of Oxford, …

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I'm an electrical engineer with 8+ years of experience with data analytics (IBM). I have a master degree and Ph.D. degree in Engineering. I've been working as mentor for Udacity for the last 3 years and I'm an instructor at Data Science Dojo. I've been a consultant for Shell, Fastshop, …

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Please note: If the technology side of Data Science or breaking into the field is your main interest, please consider connecting with other talented individuals on the platform. I can help you to become a strategic partner instead of someone that needs to be managed. Do any of the below …

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👋 Hi everyone! I am a Senior Software Engineer that worked in some of the most important tech companies, including Amazon, Google and Meta. I have a 11+ professional experience, interviewed more than 100 people in my career and helped promoting about 10 engineers with employee mentoring programs. 👨‍💻 I …

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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.

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.

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.

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.

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.

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