It’s hard to find a topic out there hotter than Data Science right now; and can be equally hard to find one more confusing. Data Science techniques have revolutionized nearly any industry you can imagine, and in some cases created whole new ones from thin air. Despite this, much of Data Science remains couched in mystery--a magic black box that is supposed to solve all of our problems.
An increase in data maturity correlates to an increase in business success. Yet though organizations gladly allocate budget to business projects, they neglect data maturity—even to the point of allowing it to deteriorate.
Want to become a data engineer but aren’t sure which technologies are the right fit for the job? People switching into big data are faced with a difficult decision—should you learn MapReduce or Spark? The answer seems simple, but requires more information and insight. Answering this and other questions correctly places you on the path to becoming a data engineer.
The data and analytics landscape is changing. Although many organizations are still analyzing structured data from their data warehouse, TDWI research indicates organizations have increasing interest in analyzing disparate kinds of data. This data is often large in volume and can require modernizing data infrastructures and platforms. The industry around big data and data science and the emerging role of the data scientist is one result of this evolution/revolution.
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