Become a better Data Scientist

There is a global shortage of data scientists in the industry! Enter the world of stats, maths and data & bring valuable insights to the world's top corporations!

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Best books to further your Data Science understanding.

A well-written and thorough book can be an amazing path to build deeper understand 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.

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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Courses to practice crucial 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.

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.

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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Data Science mentor

There is no better source of accountability and motivation than having a personal mentor. What used to be impossible to find is now just two clicks away! All mentors are vetted & hands-on!

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Software Engineer at Starry Internet
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I am a full stack web developer who is passionate about JavaScript. I have effective communication skills that help me break down difficult concepts for others to understand. Both our time is valuable and I will work hard to make sure we use each other's time effectively.
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Lead - Analytics & Data Science at Amazon
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I have 8+ years of experience working and leading teams in the domain of Data Science & Analytics. Currently leading the Analytics & Data Engineering team at Amazon in the Consumer Devices Organization. Prior to this, I worked with a Marketing Analytics start-up and helped scale customer Loyalty programs of ...
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PhD-Lead Machine Learning Engineer at Sylvera
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I am Machine Learning product lead in a tech startup in London with 3+ years of experience in computer vision and deep learning. I have a Ph.D. from Princeton University (USA) with multiple years of teaching experience at graduate and undergraduate levels. I can offer mentorship in a variety of ...
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I know what it’s like to try to break into data science. In September 2014, I drove to San Francisco from my home in New Haven, CT to attend a data science bootcamp with dreams of landing a job in the field. Three years and two data science positions later ...

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The Data Science must-reads you shouldn't miss.

Key articles and posts of industry experts can help you get a better picture of what you are getting into.

In our opinion, these are some must-reads you really shouldn't miss.

Karpathy on "Software 2.0"

Andrej Karpathy is the Director of AI at Tesla. Before that, though, he authored this blog post in 2017 talking about Deep Learning as "Software 2.0" of some sort. A must-read if you ever want to have another way of thinking about ML.

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Simple Reinforcement Learning with Tensorflow

This 8-part series by Arthur Juliani (Deep RL researcher at Unity) is an amazing entry point to the new and mysterious advancements of Reinforcement Learning, perfectly suited for folks coming from other topics in Machine Learning.

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Opportunities and projects in the Data Science space.

In the end, advancing your career is all about getting the right opportunities at the right time and a good portion of luck.

These are some interesting things going on in the Data Science space and you probably don't want to miss them.

Specialize with Kaggle

It wouldn't be the first time I've seen someone get hired over good Kaggle results! Kaggle competitions are data science and ML projects that are graded through a public leaderboard. A good place on the leaderboard shows that you know your craft and can apply your knowledge to real-life problems!

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Get into open-source

The world thrives on open-source software and this is no exception. Core contributors to core libraries and fast-growing tech like React, scikit-learn, Bitcoin and TensorFlow prove their abilities by going into the inner workings of a framework to improve it. For many companies, that's a desirable skill!

These projects are always looking for fresh faces. Grab an issue from the issue board or review a PR to get started!

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