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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.

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.

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.

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.

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Hi there! I have conducted numerous DS/DA interviews and thus possess a deep understanding of the qualities and skills needed to succeed for the data analyst or data scientist. Last year, I helped 10+ mentees (5 are from non-tech background) to land their jobs in the data field. Whenever you …

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10+ yrs data scientist and analytics experience in Tech, Insurance and Consulting

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WHY DO I WANT TO BE A MENTOR? Feeling not good enough? Don't think you are fit to be in the data science industry? Feeling lonely, stressed or even depressed? Wondering why you can't break into the industry? Wondering why no one is taking a chance on you? Considering additional …

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👋 Hey, I'm Mert, 5+ years of experience in AI and Data Science - and I've been right where you are in the tech world. I started in a non-technical role in Turkey, and the journey to where I am today, working with NLP tech and generative AI in Germany, …

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I am a Software Engineer with very deep knowledge of back-end systems, cloud infrastructure, databases, data engineering, and building data-driven products and services. I've been coding since my school days and have spent a good part of the last decade and a half writing code. I'm a self-taught programmer, and …

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Hi, I'm Radoslaw, but you can call me Radek. My background lies in artificial intelligence and machine learning, with a 20+ years of experience as an software architect, tech lead, engineer, and a mentor. Based in United Kingdom in Scotland I Led globally distributed engineering teams (UK, India, US, Sri …

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As a mentor with a background in both Data Science and Responsible AI, I have a wealth of 7+ years of experience to draw from when guiding mentees through projects in ML. I focus on helping Data Scientist and ML Engineers specialize in Responsible AI/AI Ethics, upskill in Machine Learning, …

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Govind has been working in AI/Machine Learning data science consulting industry for 10+ years and has solved industry problems through building scalable machine learning solutions across multiple domains :- Insurance, banking, telecom and healthcare. He has helped mentees landing their first data science jobs or transition into new roles.

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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.

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.

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