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UC Berkeley Data Science


About UC Berkeley Data Science

Location: Berkeley

Designed for data science professionals, the University of California - Berkeley School of Information's Online Master of Information and Data Science prepares students to derive insights from real-world data sets, using the latest tools and analytical... Read More


Data Science

Cost: $60,000
Duration: 18 weeks
Locations: Berkeley
In-person Only
Course Description:

A dedicated and diverse community of scientists, administrators, and funding institutions at UC Berkeley are reshaping the culture of research by establishing best practices in data science and testing the limits of computation. Several organizations apart from the D-Lab and the Simons Institute have a hand in data science on campus, and while data science may have recently emerged as a hot field, many of these data-focused organizations are not new. The Data Lab in the Doe Library has been available to help students locate and analyze data since October 2008. The Department of Statistics has long hosted consulting-style office hours for researchers in other fields. In addition to these and other university-wide initiatives, many departments have their own workshops and seminar series to arm researchers with field-specific skills to attack data-heavy research questions.

Now in its inaugural year, BIDS is a multifaceted, collaborative effort by professors and scientists from diverse disciplines across campus, the UC Berkeley library, the Gordon and Betty Moore Foundation, and the Alfred P. Sloan Foundation. BIDS leaders hope to extend their ambitious agenda beyond the five years of funding they have received. They are establishing a presence in the Doe Library, which is both a strategic and symbolic move—the library is physically in the middle of campus, and as the central library, it is by definition an interdisciplinary space.

Other groups that practice data science on campus focus on specific fields and problems. The AMPLab (short for “Algorithms, Machines, and People”) was established in 2011 specifically to deal with big data problems. AMPLab is a government- and industry-funded collaborative research team that focuses on creating powerful analytics tools combining machine learning, cloud computing, and crowdsourcing to solve big data problems. Its faculty and students mostly belong to the Department of Electrical Engineering and Computer Science. In the social sciences, the Social Science Matrix (SSM) is another new center dedicated to fostering creative, interdisciplinary research at Berkeley. It funds over 60 research projects, working groups, and research centers around campus, many of which are interdepartmental collaborations. Additionally, the School of Information recently rolled out a new Master of Information and Data Science (MIDS) program. Its curriculum is designed to prepare students to solve real world problems involving complex data.

Python, Data Structures, Data Science

UC Berkeley Data Science Reviews

Average Ratings (All Programs)

UC Berkeley Data Science logo

5.0/5 (2 reviews)

Programist, student | Graduated: 2020


Data Science



Job Support

"What I've experienced was a miracle, I've tried 2apps and websites non of them worked well but this one worked very well"

As I said I've been I've tried 3 times to learn python non of them was effective for me, but this one is to best choice I have ever made, the description were clear, the problems were ideal for the class. And most importantly it introduces the uses of... Read More

Chris Walker


Data Science



Job Support

"Full disclaimer: I have not yet completed the program, so the 5-star rating is subject to change"

Full disclaimer: I have not yet completed the program, so the 5-star rating is subject to change, but here is my review, cross-posted from Chris Walker's answer to What is it like to go through UC Berkeley's new Data Science MS program?

I entered the MIDS... Read More

I've learned a great deal about programming and statistics as they relate to data science, but there is also a great deal of time and thought devoted to issues of biases, policy, user/data consumer experience, and the role of the data scientist within their organization and society at large. In my opinion, this is the greatest strength of the program - pushing beyond the technical aspects to develop strong intuition and conscientious, ethical approaches to DS problems.

Where the technology is concerned, I've been exposed to a wide variety of tools, some of which I had little or no previous experience with: R, Python, scikit-learn, Tableau, D3, Pig, AWS, Solr, and a number of APIs and public data sources.

A typical week consists of about 60-90 minutes of video material per class, supporting readings and assignments, and a live 90 minute web conference class session. I work full time while also pursuing the degree, thanks in no small part to my wife's and employer's patience with my schedule. Doing both is a challenge, but is definitely possible. I'd certainly recommend the program to anyone interested in pursuing a career in data science. I know that some of my classmates have already taken jobs in the field.

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