Data Scientist Career Path & Resources
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 Scientists are more in-demand than ever! While many folks in the data space try to get their hands dirty with AI and Machine Learning, 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.
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
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
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.
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resources
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
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
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
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
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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resources
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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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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Related career paths
If you like Data Science, you might also be interested
in these other
career paths.
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