6 Resources to Ramp Up Your Knowledge of Data Science

If you’re like me, you always keep a good book close at hand. And even if you don’t, these times might give you plenty of time to blow the dust off your bookshelves or try that Kindle you got for birthday. I did so two months ago and quickly rediscovered my passion for reading again. It escalated when I ordered 4 new reads thinking that they will last throughout the summer. I devoured them in less than 3 weeks.

And then I had an idea… What do my developer colleagues read these days? So I asked them, hoping to steal some of their gold. I was immediately stunned by that spark of enthusiasm that I triggered. I soon received so many great suggestions that I couldn’t keep them for myself.

I created a shared document where SO1ners can exchange their favorite resources. I also talked with our office manager who will soon build our shiny new SO1 library! Meanwhile, I wanted to share some picks also with you. You, software developers, DevOps or machine learning engineers keen to cram some extra knowledge of the field. Whether from e-books, YouTube or good old hardcovers. Let’s have a look:

The Phoenix Project: A Novel about IT, DevOps, and Helping Your Business Win

Although not directly related to data science, I had to include this piece as it was the most recommended book on the list. At SO1, we use DevOps practices to achieve the shortest possible development cycles and continuous, high-quality delivery. “I feel reflected on some of the characters that confronted me with the benefits of DevOps mindset and forced me to see things that are just not right.” says Saúl, our Spain-based DevOps Engineer.

“This made the “novel” side of the book quite enjoyable for me. I like how it gives you some perspective and makes you think about the business side, the operations side and the security side. It’s a great parable about collaboration, transparency and sane processes (Lean / DevOps / Theory of Constraints / Code Quality). All in all, this is a great book to recommend to the business / management side, or anyone who wants to understand what is all the buzz from DevOps and its benefits. In my case it made me feel empowered to change things that needed to be changed.”

The Phoenix Project: A Novel About IT, DevOps, and Helping Your Business Win >>

Pattern Recognition and Machine Learning, A Probabilistic Perspective, Deep Learning

The next three books recommended by our Senior Data Scientist — Dmitry — are real gold when it comes to Machine Learning basics, but they are in no way light-weights with a page count of around 800 each. I already hear you saying “what a bummer, I wanted some quick and dirty tips to master ML in weeks”. Unfortunately it’s not that easy but do not despair. These books include a wide variety of ML areas from which you can only choose what’s relevant and leave the rest for later. These topics include computer vision, speech recognition, recommender systems, and many-many others.

The books assume you have some background in math, computer science or programming, but you don’t have to possess any knowledge of Machine Learning, Deep Learning or NLP. Hence the books are a great introduction for beginners and an equally good refresher for skilled pros.

Pattern Recognition and Machine Learning>>

Machine Learning: A Probabilistic Perspective>>

Deep Learning (Adaptive Computation and Machine Learning)>>

Natural Language Processing with Deep Learning

After these reads and some practice, you’re already on the right track to become a skilled machine learning engineer. But to hone your craft to the next level, you might also need to master NLP, and this is a great resource to do so. And the best part? It’s not a book for a change, it’s a Stanford course available completely free on YouTube:

Course syllabus>>



Designing Data-Intensive Applications

Finally to put all the pieces together in a smooth-working application, you might want to read this piece from O’REILLY. Here at SO1, we work with FMCG retailers, meaning we have to process incredibly large datasets containing the shopping history of millions of consumers (every one of which purchases 20 – 30 additional goods every week). With so much data, topics such as scalability, consistency, reliability, efficiency, and maintainability are essential, and this book covers them all.

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems>>

There you have it. I know there’s much more good stuff out there and hence I call you – let me know about your favorite book, article or course on any data science-related topics. We might include them next time!


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