Difference between revisions of "Tackling Big Data MIT Course"
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Latest revision as of 11:38, 23 April 2014
--D. Thiebaut (talk) 20:12, 3 March 2014 (EST)
Misc. Information
- http://professionaleducation.mit.edu
- Self Service Center: https://crm.orionondemand.com/crm/forms/C6700mB00x6G0x67028F
Login to EdX
- https://edge.edx.org/
- screen name: dominique
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
Certificate
