Though Twitter has been known to contribute to the community through open source projects, the company is now taking its contributions a step further. In what will likely feel like a dream come true for data science students, Twitter has teamed up with UC Berkeley to develop and teach a class about analyzing big data.
Twitter is a simple enough service, all built around short messages and links, but what happens behind the scenes is more complex than you’d think. Engineers have to manage the service’s massive number of users and predict patterns of use ahead of traffic spikes, all while handling natural language processing, recommendation algorithms and so on. In other words, this is heavy stuff.
From the course description:
How to store, process, analyze and make sense of Big Data is of increasing interest and importance to technology companies, a wide range of industries, and academic institutions. In this course, UC Berkeley professors and Twitter engineers will lecture on the most cutting-edge algorithms and software tools for data analytics as applied to Twitter microblog data. Topics will include applied natural language processing algorithms such as sentiment analysis, large scale anomaly detection, real-time search, information diffusion and outbreak detection, trend detection in social streams, recommendation algorithms, and advanced frameworks for distributed computing. Social science perspectives on analyzing social media will also be covered.
According to the course description, Twitter’s engineers “will help advise student projects, and students will have the option of presenting their final project presentations to an audience of engineers at the headquarters of Twitter in San Francisco (in addition to on campus).”
Overall, this is a great opportunity for talented engineering students to show their stuff, maybe score a job at the hot company, or even become the next Hilary Mason. For more information, visit the course page via the link below.
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