Become a 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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Why should you become a
Data Scientist?

Data Scientist

Data Scientists are more in-demand than ever! While many folks in the data space try to get their hands dirty with AI, they are leaving a big gap in the job market for data analysts and scientists.

In the US today, over 3,000 new job postings for Data Scientists are found. The space is growing and getting more open for career changers.

A Data Scientist can expect to be among the top range of tech salaries. Well into the six-figures in the US on average, and at top ranges all around the world!

Best books to build Data Science understanding.

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

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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.

With the motto "making neural nets uncool again", is a straight-to-the-point practical (and free!) course that is valued by Machine Learning enthusiasts and engineers worldwide. 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.

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Get the guidance you need 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!

Stephen Gabriel

2 spots left

Stephen Gabriel  Quick Responder

Software Engineer (Ops & Data) - Indie
4.8 stars
4.8 (4 reviews)
Personal Chat To-Dos Projects & Challenges 1-on-1 Calls Hands-On Support

I am a python software engineer with 3 years of industry experience, with a special interest in analytics engineering, data platform, and data engineering. During the day, you can find me designing scalable data pipelines or a reactive machine learning system, or maybe solving a deep learning problem. I also build conversational (chatbot) systems, and backend powered by graph engines.

I lit...

Engineering & Data AWSData SciencePython
7 Day Trial China China
 What can I expect from this mentor?
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Linda Oranya

5 spots left

Linda Oranya 

Data Scientist - Innovate for Africa
5.0 stars
5.0 (1 review)
Personal Chat To-Dos Projects & Challenges 1-on-1 Calls Hands-On Support

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

Engineering & Data PowerBIData SciencePython
7 Day Trial Nigeria Nigeria
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Adam Green

1 spot left

Adam Green 

Data Scientist - Gridcognition
4.3 stars
4.3 (7 reviews)
Personal Chat To-Dos Projects & Challenges 1-on-1 Calls  (2x/mo) Hands-On Support

I'm looking to mentor students at any level who want to learn data science or programming with Python.

I have five years of teaching & mentoring data scientists.

I'm currently working as a Data Scientist at Gridcognition.

My interests are in using machine learning for climate change, such as projects on optimizing the dispatch of electric batteries to creating a database of climate...

Engineering & Data machine learningdata sciencereinforcement learning
7 Day Trial New Zealand New Zealand
one-off sessions starting
at $69
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Tracy Pham

3 spots left

Tracy Pham  Quick Responder

Head of Artificial Intelligence - CanopyLAB
5.0 stars
5.0 (2 reviews)
Personal Chat To-Dos Hands-On Support

Majoring in Natural Language Processing and Deep Learning, I have worked with multiple transformer-based models (such as BERT, GPT-2) for Text Processing and Text Generation, and using Distillation methods to transfer the knowledge to a much simpler model like LSTM, to deploy as an AWS Lambda service.

I have been a Teaching Assistant in Deep Learning class of a non-profit organization as we...

Engineering & Data ChatbotPytorchData Science
7 Day Trial Vietnam Vietnam
one-off sessions starting
at $89
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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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Get the help you need & advance your career

Ready to enter the competitive world of Data Science? Our mentors can help, whenever you are getting lost!