Tackling Big Data MIT Course

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--D. Thiebaut (talk) 20:12, 3 March 2014 (EST)


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Overall Syllabus

MODULES, TOPICS, AND FACULTY

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)

Note: Schedule and faculty are subject to change without notice

Notes


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