One in a series of articles profiling university programs focusing on big data and analytics education.
Machine learning was the most popular course in the master’s in computer science program at Stanford University in 2012.
Long a talent feeder to Silicon Valley—count the founders of Hewlett-Packard, Sun Microsystems, Google, Yahoo and Instagram as former students—Stanford has used those technology industry ties to adapt its computer science curriculum for analytics.
In 2008, the university’s computer science department updated the curriculum for the Database Systems concentration, refocusing it as Information Management and Analytics.
“This foundation in analytics applies to a variety of systems,” said Mehran Sahami, the associate chairman for education in the computer science department. “The feedback on the new curriculum was uniformly positive.”
Sahami said the change reflected a growing awareness in the market that newer technologies like machine learning, social network analytics and predictive analytics would be the next foundation for the future of computing. Machine learning was the most popular course in the master’s in computer science program in 2012, he said.
“Since we’re in the heart of Silicon Valley, we pretty much through osmosis see what the industry is looking for,” Sahami said. “We have pretty strong ties to the marketplace.”
The industry connections also provide Stanford students with resources like Amazon’s EC2 cloud platform to do large scale computing.
“The students get to work with scalable industry level horsepower to do their coursework,” Sahami said.
The computer science department offers a series of specializations, including coursework that overlaps with big data and analytics: artificial intelligence, bio computation, mobile Internet computing and security.
- Computer science department founded in 1965.
- Approximately 250 graduate students in the department; 500 undergraduate.
- Most students complete the program between one and two years.
- Graduate tuition is $43,950 for fall, winter and spring quarters.
- Students may choose a single concentration or a primary and secondary area of depth.
- No thesis or final project required, but a research project possible. Students are required to build “significant” implementations before graduating.
- Internships not required but recommended.
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