Big Data Training for Learning Professionals

As a learning professional in your organization, your days are consumed by figuring out the best ways to facilitate outstanding learning outcomes in your organization’s workforce. But when’s the last time you engaged in your own learning and development? If you’re feeling pressure from higher up in the organization to leverage big data for better learning but feel overwhelmed by that prospect, chances are good that you need to improve the analytical capabilities on your learning staff.

Big Data Training for Learning Professionals

In a previous article, Laying the Foundation for Leveraging Big Data in Learning, I highlighted that this training needs to go beyond specific data analysis tools and techniques to include a strong focus on how to utilize data for improved decision-making. That’s a pretty specific form of training, which begs the question: Where do you get that kind of training for yourself and your organization’s other learning professionals? Below are two of the best and brightest big data training programs available to professionals:

Big Data Concepts Masterclass by Big Data Partnership

If you’re new to the whole concept of big data, consider taking a one-day crash course like this one offered by the UK-based Big Data Partnership. Geared towards senior management and business leaders, it offers the following key objectives:

  • Understand big data and how it can be applied to store, manage, process and analyze massive amounts of unstructured and poly-structured data.
  • Explore the technologies underpinning big data including Hadoop and NoSQL.
  • Determine how big data systems can complement traditional data warehousing and business intelligence solutions and processes.
  • Utilize big data to differentiate your business and provide better service to your customers.
  • Examine case studies of how big data is influencing society and businesses.

Tackling the Challenges of Big Data by MIT Professional Education

MIT first offered this groundbreaking online professional big data course in March 2014, and is about to make it available for a third time in February 2015. It’s a survey of state-of-the-art topics in big data, looking at data collection (smartphones, sensors, the Web), data storage and processing (scalable relational databases, Hadoop, Spark, etc.), extracting structured data from unstructured data, systems issues (exploiting multicore, security), analytics (machine learning, data compression, efficient algorithms), visualization, and a range of applications. Specific learning objectives of this 6-week course include the following:

  • Distinguish what is Big Data (volume, velocity, variety), 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’ve listed these two courses because they’re both appropriate for people like you – people that need to know more about big data without undergoing the extensive technical training needed in becoming a certified data scientist. Engaging in either one of the above courses will put you an entire quantum leap ahead of most learning professionals in terms of understanding the basics of big data and what it can do for your learning efforts.

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