Big Data

Data analytics: Tips for entering the field

Data analytics is a hot and happening field. Here are some tips from a data analyst for how to get started in the field.

Grace Simrall is a data analyst and founder of iGlass Analytics, where she helps companies in a wide variety of verticals (healthcare, retail, finance) achieve their Business Intelligence goals.

I asked Grace what she would recommend someone do if he or she were interested in the field of data analysis. She said she thought it was too soon to tell the value of certification or master's degree programs in analytics and that it was probably a good idea to actually try out the work before investing in a degree. She said that it's more important to demonstrate that you're able to get your hands dirty and execute what's asked for.

In the meantime, she recommends several free online courses to help you learn what's involved in the field of analytics and whether you think you'll like it. Here are some of the free resources she recommended:

1. - Free Hadoop courses

2. Stanford University's free online courses with two tracks: basic - watch lectures, take simple quizzes; advanced - full class w/homework & tests

3. Youtube University - multiple channels

4. MIT Open Courseware

5. Khan Academy

Grace's experience with business intelligence (BI) began at the University of Chicago when she implemented an enterprise asset management system that would become the foundation for tracking operational efficiency across the university. Grace received her SB in Geophysical Science from the University of Chicago and her MS in Mechanical Engineering from the University of Louisville, Speed School of Engineering.


Toni Bowers is Managing Editor of TechRepublic and is the award-winning blogger of the Career Management blog. She has edited newsletters, books, and web sites pertaining to software, IT career, and IT management issues.


Thanks for sharing your post. Big data analytics is the process of examining big data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions. With big data analytics, data scientists and others can analyze huge volumes of data that conventional analytics and business intelligence solutions can't touch. Consider that your organization could accumulate (if it hasn't already) billions of rows of data with hundreds of millions of data combinations in multiple data stores and abundant formats. High-performance analytics is necessary to process that much data in order to figure out what's important and what isn't. Enter big data analytics.


Dear Toni,

Thanks for sharing the links.

I like to add one a use often:

It has very good free high level courses from renowed universities.

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