Data science and analytics are some of the top in-demand job categories in the technology industry today. Indeed, demand is higher than supply for these specialists, and many data science master's degree programs have sprouted in the past few years. The online learning curriculum has expanded significantly, too, with offerings from the big MOOC providers (massive open online course) such as Coursera and Udemy, as well as vendors who offer the technologies that enable big data, such as MapR and Confluent, among many others.
But aside from formal education, either online or offline, there are other ways to learn about this emerging field, and to gain some of the skills you need if this is the next step for you on your career journey. If you're an executive leading a team of data scientists, you might need better grounding to learn about the technology the group's members use to do their jobs.
InformationWeek has put together a collection of essential reading for data scientists, business analysts, executives, and others who are interested in this rapidly growing field.
Our collection features 10 books to help you understand everything from the ramifications of widespread algorithms and models for our future society, to how to use some of the most popular languages and tools to generate insights from data.
What are the essential skills for data scientists to possess? What are some of the key recipes for R users to leverage in their work? How can you use data to tell stories that compel your audience to action? How can you work with big data technologies such as Apache Hadoop and Apache Spark?
What are the cultural and economic ramifications of a future world where so many decisions are based on a black box of algorithms? Take a look at this list to find out. Are there any that you will add to your reading list? Did we miss any? Let us know in the comments below.
Python Machine Learning
Python is one of the top languages suggested for data scientists to learn, and it's a skill that commands more money during salary negotiations. For any data scientist, aspiring data scientist, or developer looking to add this language to their skill set, Python Machine Learning could be essential reading. The book promises to help readers leverage Python's open-source libraries for deep learning, data wrangling, and data visualization. It offers help with learning strategies and best practices for improving and optimizing machine learning systems and algorithms.
Author: Sebastian Raschka
Price: $22.39 on Kindle, $40.47 in paperback
Data Analytics Made Accessible
This book provides the reader with an overview of data analytics, so it could be a good book for a beginner wanting to learn more about the field or for managers who need a primer on the technologies and an understanding of how they all work. The book offers mini case studies at the beginning of each chapter and offers an overview of data mining techniques and platforms. It also provides a tutorial for the R statistical analysis platform.
Author: Anil Maheshwari
Data Smart: Using Data Science to Transform Information Into Insight
Written by the chief data scientist for MailChimp.com, this book concentrates heavily on using Microsoft Excel to gain insights from data, so don't expect to learn about R or Hadoop or Apache Spark here. But do expect to learn how to get the most out of data sets that can be handled by Excel.
Author: John W. Foreman
Price: $23.99 Kindle, $27.99 paperback
To see the full list, check out the original article out over on InformationWeek.