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This Online X course will survey 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.  Each module will introduce broad concepts as well as provide the most  recent developments in research.

The course will be taught by a team of world experts in each of these areas from the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).

With backgrounds in data, programming finance, multicore technology, database systems, robotics, transportation, hardware, and operating systems, each MIT Tackling the Challenges of Big Data professor brings their own unique experience and expertise to the course.


Module One: Introduction and Use Cases

The introductory module aims to give a broad survey of Big Data challenges and opportunities and highlights applications as case studies.

  • Introduction: Big Data Challenges (Sam Madden)
  • Case Study: Transportation (Daniela Rus)
  • Case Study: Visualizing Twitter (Sam Madden)

Module Two: Big Data Collection

The data capture module surveys approaches to data collection, cleaning, and integration.

  • Data Cleaning and Integration (Mike Stonebraker)
  • Hosted Data Platforms and the Cloud (Matei Zaharia)

Module Three: Big Data Storage

The module on Big Data storage describes modern approaches  to databases and computing platforms.

  • Modern Databases (Mike Stonebraker)
  • Distributed Computing Platforms (Matei Zaharia)
  • NoSQL, NewSQL (Sam Madden)

Module Four: Big Data Systems

The systems module discusses solutions to creating and deploying  working Big Data systems and applications.

  • Multicore Scalability (Nickolai Zeldovich)
  • Security (Nickolai Zeldovich)
  • User Interfaces for Data (David Karger)

Module Five: Big Data Analytics

The analytics module covers state-of-the-art algorithms for very  large data sets and streaming computation.

  • Machine Learning Tools (Tommi Jaakkola)
  • Fast Algorithms I (Ronitt Rubinfeld)
  • Fast Algorithms II (Piotr Indyk)
  • Data Compression (Daniela Rus)
  • Case Study: Information Summarization (Regina Barzilay)
  • Applications: Medicine (John Guttag)
  • Applications: Finance (Andrew Lo)


Prerequisite(s): This course is designed to be suitable for anyone with a bachelor’s level education in computer science. It is not free.

  • Title: Tackling the Challenges of Big Data 
  • Dates: November 4th - December 16th 2014 and February 3rd - March 17th 2015
  • Contact:  [email protected]

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