Inside the Data Reading Room – Summer 2021 Edition

As regular readers of my blog know, I periodically post about some of the computer- and data-related books that I’ve been reading in a series I like to call “Inside the Data Reading Room.”

In today’s post we’ll examine three new books on the following topics: Python, data stewardship and deep learning. Let’s dig right in and begin…

The first book is a new edition of Black Hat Python, 2nd Edition: Python Programming for Hackers and Pentesters by Justin Seitz and Tim Arnold (No Starch Press, 2021, ISBN 978-1-7185-0112-6). The authors are both long-time Python developers.

Although I did not read the first edition of this book, I am impressed with the wide range of guidance and sample for using Python to create useful tools for security professionals. And make no mistake, the book is not geared toward beginners looking for a how-to learn Python book, but is designed for security analysts who use Python. The book is less than 200 pages long, but it contains a wealth of useful security techniques on topics ranging from exfiltration, web-hacking, forensics, Windows privileges, and more.

The code and examples in this edition of the book has been updated to Python 3.x, so it can be relied upon to be up-to-date. If you work as a computer security professional and want to code in Python, this is definitely a book that belongs on your bookshelf.

Moving along, the second book I’ll talk about today is also a second edition, Data Stewardship: An Actionable Guide to Effective Data Management and Data Governance by David Plotkin (Academic Press, 2021, ISBN: 978-0-12-822132-7). If you work in the field of data management, chance are you know of David Plotkin. He has worked in the field for decades and has written extensively on data, metadata, and related issues.

With the second edition of his book, Plotkin has written the quintessential book on data stewardship. He defines data stewardship and how it fits together with data governance, delivering best practices, policies, and procedures that make sense. The book offers up practical advice on implementing data stewardship and defines the roles and responsibilities of the data steward. But he also devotes a chapter offering guidance on training for data stewards, and another chapter on the metrics that can be used to measure the performance of data stewards.

And if your organization is new or just starting to implementing data governance and stewardship, then you absolutely must read Chapter 4, “Implementing Data Stewardship.” It will step you through the process of championing, communicating, and gaining support for data stewardship.

If your organization is serious about data governance and data stewardship, this book belongs in your company library.

Finally, let’s turn our attention to the third and final book I’ll be talking about today, Deep Learning for Data Analytics: Foundations, Biomedical Applications, and Challenges edited by Himansu Das, Chittaranjan Pradhan, and Nilanjan Dey (Academic Press, 2020, ISBN: 978-0-12-819764-6). The editors are all computer science professors at different colleges and universities with a background in analytics and machine learning.

This book is not for the novice. It focuses on advanced models, architectures, and algorithms in deep learning, which is a branch of AI and machine learning. It is probably most useful for university students studying AI, machine learning, and deep learning, as opposed to a book for the masses.

However, if you are interested in learning about applications of deep learning across a variety of different subject areas, this book offers a focused study on the design and implementation of deep learning concepts using data analytics techniques in large scale environments.

That’s all for now… and I wish you countless joyful hours reading about data and related technology until the next time we go inside the data reading room!


I'm a data management strategist, researcher, and consultant with over three decades of experience in all facets of database systems development and implementation.
This entry was posted in AI, analytics, book review, books, data governance, Machine Learning. Bookmark the permalink.

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