Your path to Senior 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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Opportunities and strategies to grow into a Senior Data Scientist role.

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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Growing into a Senior role pays off – $20,000 per year to be exact.

Not only is the progression from individual Data Scientist to a Senior role a natural progression, it also makes all the difference in your wallet.

On a global average, Senior Data Scientist receive a $20,000 salary hike when they go from individual contributor to senior personnel.

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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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Take the next step with a
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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Machine Learning, Natural Language Processing, and Data Science are amazing tools. With these skills in your belt you can work in a wide range of industries in Tech and Engineering. Let me help you master these technical skills so that you can accomplish amazing things. For 9 years between 2009 …
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Freelance Data Scientist and Data Science Instructor at Fiverr, Data Society
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Elliot is a freelance Data Scientist with 6+ year of experience. He co-founded a soccer computer vision company that secured $250,000 of funding. Elliot was a leader in creating computer vision products for the vertical jump and juggling a soccer ball. His sports algorithms have generated over $100,000 of profit …
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Lead Data Scientist, Google Developer Expert - ML | Ex-Intel | Ex-Red-Hat | Ex-Applied Materials at Schaffhausen Institute of Technology Academy, Zurich
Lead Data Scientist, Author & Consultant. Mentored over 500+ working professionals in Data Science.
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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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Researcher in Natural Language Processing
7+ years experience in Artificial Intelligence with a focus on NLP. PhD in NLP from a top 10 school.
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Hello, my name is Nikola! I am a researcher in Natural Language Processing (NLP) based in Switzerland. I have a PhD in NLP, and 8+ years of experience in doing research, and developing production-ready AI systems. I have mentored over 40 people in the past, that have gone on to …

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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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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.

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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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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