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(Image: MichaelJay/iStockphoto)

(Image: MichaelJay/iStockphoto)

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.

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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.

Jessica Davis has spent a career covering the intersection of business and technology at titles including IDG's Infoworld, Ziff Davis Enterprise's eWeek and Channel Insider, and Penton Technology's MSPmentor. She's passionate about the practical use of business intelligence, ... View Full BioWe welcome your comments on this topic on our social media channels, or [contact us directly] with questions about the site.Previous1 of 11NextComment  | Email This  | Print  | RSSMore InsightsWebcasts Malicious Insiders: Real Defense for Real Businesses
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4/29/2016 | 2:21:31 PM The focus should be on the principles, not the tools
Once people understand what they're doing, then they can worry more about the finer points of the tools.  Thus, to start with a Python book was completely inappropriate. Reply  |  Post Message  |  Messages List  |  Start a Board Technocrati Login50%50% Technocrati,
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4/26/2016 | 2:13:21 PM Using Excel for Big Data Insight

Interesting to see a Book referenced here that maximizes the use of Excel.   The Analytics Industry would love that analysts use the more complex tools for Big Data analysis, but Excel is still very heavily relied upon and probably the fastest way to start to examine and gain insight from the Data.

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4/26/2016 | 2:08:17 PM Big Data Findings: How Do We Convey it ?

I am glad a book on Story Telling was included in this list.  This is a major issue for Analyst.  

After all the numbers and data have been crunched.  It is essential to be able to convey findings and approach easily to those who are less technically inclined.

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4/26/2016 | 2:04:41 PM Get Started With Understanding Big Data

Thank you Jessica for providing an interesting and useful listing of Big Data resources.  Those of us in tech can no longer easily ignore Big Data and your recommended reading list of great help.

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4/25/2016 | 8:50:55 PM The Misuse and Misunderstanding of Big Data

" ...if a poor student can't get a loan because his zip code shows he is too risky, then he's cut off from the kind of education that could pull him out of poverty."


 Just one example of how the use of Big Data is limited and counterproductive if it is not well structured from the start.

This is one of the recent arguments I have been making about the use of Big Data. I am glad that Ms. O'Neil addresses the issue. 

I would consider this a must read for Analytics professionals both new and old.


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