Big Data University Series – Part III: More Data Science

In Part III of this seriesBig Data University logo, highlights three more “Data Science” courses that may be of interest to BI, Analytics & Data Science Professionals.

What is Big Data University?

As stated on the Big Data University website, Big Data University is an IBM initiative aimed at spreading big data literacy. Big Data University’s mission is to democratize access to practical skills for working with data by removing two big impediments: money and time. To that end, they have made everything you need to learn free. Free courses, free access to all the tools, free data – free everything and not for a few days or weeks – forever.  Big Data University courses are “self-paced”,  allowing you to take as long as you need to complete a course.

Data Science Courses:

  • Data Science Methodology – How does a data scientist think? What are the major steps involved in tackling a data science problem? In Data Science Methodology, John Rollins (Ph.D., Data Scientist at IBM) describes the major steps involved in practicing data science, with interesting real-world examples at each step: from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.
  • Text Analytics Essentials – The analysis of emails, blogs, tweets, forums and other forms of unstructured text data constitutes what we call text analytics.  Text analytics is applicable to most industries; for example, if your company is suspicious about company secrets being leaked to competitors by employees, text analytics can help analyze millions of employees’ emails.  If you would like to find common pain points your customers face when using your products, you can analyze their comments and questions in forums. If you would like to measure positive or negative perceptions of a company, brand, or product, you can perform sentiment analysis using text analytics. This course teaches you the basics of text analytics.
  • Predictive Modeling Fundamentals I – Predictive Analytics brings together advanced analytics capabilities spanning ad-hoc statistical analysis, predictive modeling, data mining, text analytics, entity analytics, optimization, real-time scoring, machine learning and more. IBM SPSS Modeler puts these capabilities into the hands of business users, data scientists, and developers. In this course in the Big Data University you will learn the basics to get started with Predictive Modeling.

In Part I of this series, highlighted three “Big Data” courses that may be of interest to BI, Analytics & Data Science Professionals. In Part II of this series, highlighted three “Data Science” courses.

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