I hope you've enjoyed the summer (or winter, if you're in the southern hemisphere) and are looking forward to getting the kids back to school.
Here, we've had a couple of really nice months of warm, sunny weather, which makes a huge change - I've lived in Scotland for 19 years and this is the first summer we've had in all that time. No, really, I'm not joking - my next door neighbour who is 40 and has lived here all his life tells me this is the first Scottish summer he's had. If he wants some sun he has to drive south of Hadrian's Wall or get a flight to catch a few rays. The things we have to do to get a mild suntan...
Anyway, now that the kids are back to school you'll no doubt have some time to spare to throw yourself into a good book or three.
This months FREE Data Science books will get you back into the swing of things, get the grey matter dusted off and restart that push towards your next promotion.
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Disclosure: The FREE ebooks were free to download at the time of posting but other links in this post may contain affiliate links. As Amazon Associates we may earn from qualifying purchases.
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What you need to know about Machine Learning
by Gabriel A. Canepa
Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging, contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project.
This book provides the basic understanding, knowledge, and skills that you can build on with experience and time.
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This book is intended for professionals in data science, computer science, operations research, statistics, machine learning, big data, and mathematics. In 100 pages, it covers many new topics, offering a fresh perspective on the subject. It is accessible to practitioners with a two-year college-level exposure to statistics and probability. The compact and tutorial style, featuring many applications (Blockchain, quantum algorithms, HPC, random number generation, cryptography, Fintech, web crawling, statistical testing) with numerous illustrations, is aimed at practitioners, researchers and executives in various quantitative fields.
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Building Data Science Teams
by DJ Patil
As data science evolves to become a business necessity, the importance of assembling a strong and innovative data teams grows. In this in-depth report, data scientist DJ Patil explains the skills,perspectives, tools and processes that position data science teams for success.
