Software Architect / Microsoft MVP (AI) and Technical Author

Analytics and Big Data, Blogging, General Development, Machine Learning, Prototyping, Sentiment Analysis, Social Media

Tackling the challenges of Big Data

Slight change from the machine learning posts of the past few weeks, I’ve recently paid and enrolled on a course being delivered by Massachusetts Institute of Technology called “Tackling the Challenges of Big Data“.

You can see a short video what’s covered here:

The course is held over six weeks and will provide the following:

  • Five modules covering 18 topic areas: with 20 hours of video
  • Five assessments

I’m looking forwarded to learning about the following:

  • Distinguish what is Big Data (volume, velocity, variety), and will learn where it comes from, and what are the key challenges
  • Determine how and where Big Data challenges arise in a number of domains, including social media, transportation, finance, and medicine
  • Investigate multicore challenges and how to engineer around them
  • Explore the relational model, SQL, and capabilities of new relational systems in terms of scalability and performance
  • Understand the capabilities of NoSQL systems, their capabilities and pitfalls, and how the NewSQL movement addresses these issues
  • Learn how to maximize the MapReduce programming model: What are its benefits, how it compares to relational systems, and new developments that improve its performance and robustness
  • Learn why building secure Big Data systems is so hard and survey recent techniques that help; including learning direct processing on encrypted data, information flow control, auditing, and replay
  • Discover user interfaces for Big Data and what makes building them difficult
  • Measure the need for and understand how to create sublinear time algorithms
  • Manage the development of data compression algorithms
  • Formulate the “data integration problem”: semantic and schematic heterogeneity and discuss recent breakthroughs in solving this problem
  • Understand the benefits and challenges of open-linked data
  • Comprehend machine learning and algorithms for data analytics

I’m sure I’ll be able to apply what I learn in this course to Social Opinion and add new features to improve the product.

Undertaking this course, coupled with a full time job and being in the running to win the Twitter AdTech Challenge – and hopefully becoming a recognised Twitter Official Partner will mean the blogging may have to take a back seat for a few weeks until I get some of these things out of the way!





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