Tackling Big Data MIT Course

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

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Misc. Information

• Flyer (pdf)
• Web Page

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• http://professionaleducation.mit.edu
• Self Service Center: https://crm.orionondemand.com/crm/forms/C6700mB00x6G0x67028F

Login to EdX

• https://edge.edx.org/
• screen name: dominique

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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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Certificate

• [[Media:TacklingBigData_Certificate.pdf| Certificate]