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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.

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Hi there! I have conducted numerous 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 need …

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Machine Learning practitioner with 10+ years of experience scaling data at top-tier Silicon Valley companies. I’ve led projects spanning from Machine Learning, Data Engineering, to Recommender Systems. I've been a people manager for over 4 years. I am happy to - review resume; - mock system design interviews; - mock …

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I am a data scientist with experience in creating insights from data, making analyses, building predictive models. I love to impact while getting impacted on. I look forward to building a community of data-aware individuals and communities who would use data to solve the unique problems of their community.

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Hi there, Are you looking for additional support to navigate your data science career? Do you feel you can't speak to your manager freely? Are you feeling a bit confused on how to take your career to the next level? When I was starting out 10 years ago, I had …

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I'm a Senior Product Manager at Lyft, with 4 YOE of experience managing ML/AI products, data, search features, and web applications. I've worked in the consulting, startup, and large tech space, and am a career transitioner with no technical background in coding or data. I am living proof of someone …

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I've been working in enterprise software for the last 16 years with senior roles spanning across Marketing, Sales, Customer Success, Product Management and Operations. 12 years of this experience has been working in successful start-ups where I have developed and grown teams, products and markets all around the world. More …

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Data science is a constantly evolving field and I am passionate about sharing my knowledge with folks who are aspiring to succeed in this field. I have 4+ years of experience, currently working with 'Amazon' WW Delivery Program, prior to this with 'MuSigma Business Solutions'. I have been solving business …

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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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