Free Data Science eBooks - October 2016

If you're a regular subscriber to our newsletter you'll know that every month we scour the internet seeking out free eBooks to help you on your educational journey. Well, it has been so popular that rather than just send the links in the newsletters we decided to create a resources section here on the website.

I hope this will prove to be a valuable resource to you that you will visit regularly (and invite your friends too).

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This month, we’re taking advantage of O’Reilly offering a few of their Data Science eBooks for FREE and have picked up a few for you that we think you will find interesting. These are short and easy reports for you to read, so grab a coffee and a Danish, and let's get started...

 


 

Going Pro in Data Science

by Jerry Overton

Digging for answers to your pressing business questions probably won’t resemble those tidy case studies that lead you step-by-step from data collection to cool insights. Data science is not so clear-cut in the real world. Instead of high-quality data with the right velocity, variety, and volume, many data scientists have to work with missing or sketchy information extracted from people in the organization.

In this O'Reilly report, Jerry Overton - Distinguished Engineer at global IT leader CSC - introduces practices for making good decisions in a messy and complicated world. What he simply calls "data science that works" is a trial-and-error process of creating and testing hypotheses, gathering evidence, and drawing conclusions. These skills are far more useful for practicing data scientists than, say, mastering the details of a machine-learning algorithm.

Adapted and expanded from a series of articles Overton published on O’Reilly Radar and on the CSC Blog, each chapter is ideal for current and aspiring data scientists who want to go pro, as well as IT execs and managers looking to hire in this field.

 


 

Evaluating Machine Learning Models

by Alice Zheng

Data science today is a lot like the Wild West: there’s endless opportunity and excitement, but also a lot of chaos and confusion. If you’re new to data science and applied machine learning, evaluating a machine-learning model can seem pretty overwhelming. Now you have help. With this O’Reilly report, machine-learning expert Alice Zheng takes you through the model evaluation basics.

In this overview, Zheng first introduces the machine-learning workflow, and then dives into evaluation metrics and model selection. The latter half of the report focuses on hyperparameter tuning and A/B testing, which may benefit more seasoned machine-learning practitioners.

 


 

Data and Social Good

by Mike Barlow

Data may indeed be the "new oil" - a seemingly inexhaustible source of fuel for spectacular economic growth - but it's also a valuable resource for humanitarian groups looking to improve and protect the lives of less fortunate people. In this O'Reilly report, you'll learn how statisticians and data scientists are volunteering their time to help a variety of nonprofit organizations around the world.

Mike Barlow cites several examples of how data and the work of data scientists have made a measurable impact on organizations such as DataKind, a group that connects socially minded data scientists with organizations working to address critical humanitarian issues. There's certainly no lack of demand for data science services among nonprofits today, because these organizations, too, realise the potential of data for changing people's fortunes.

 


 

*BREAKING NEWS*

Practical Data Cleaning is now FREE...

(and accompanied by a FREE video course)

 


 

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