Master of Information and Data Science

University of California - Berkeley


Issuing School School of Information
Description from the School

The Master of Information and Data Science (MIDS) program delivered online from the UC Berkeley School of Information (I School) prepares data science professionals to be leaders in the field. By blending a multidisciplinary curriculum, experienced faculty from top data-driven companies, an accomplished network of peers, and the flexibility of online learning, the WASC-accredited datascience@berkeley program brings UC Berkeley to students, wherever they are.

In-State Cost $69,741
Out-of-State Cost $69,741
Delivery Model Fully Online
Completion Time 20+ months
Diploma Different? No
Application Deadline 1 Sep 20, 2021
Application Deadline 2 Sep 20, 2021
Alumni Companies
Exam Required None
Transfer Available No
Credits Needed to Complete 27
College Funded Aid No
Admission Requirements

Online application Official transcripts from all educational institutions attended from your undergraduate degree on Statement of Purpose and additional admissions statements Two professional letters of recommendation A working knowledge of fundamental computer science concepts including: data structures, algorithms and analysis of algorithms, and linear algebra Current resume TOEFL Scores (if applicable) Application fee of $120 for domestic applicants or $140 for international applicants Optional: Official Graduate Record Examination (GRE) or Graduate Management Admission Test (GMAT) scores

A bachelor's degree

You should have a superior scholastic record — normally well above a 3.0 GPA. The recognized equivalent to a bachelor's degree is also accepted, if earned from an accredited institution.

Many schools issue transcripts electronically, either through their own web services or through vendors. If this option is available through the institutions you attended, please specify that your transcript(s) be sent to applicationservices@datascience.berkeley.edu as this will expedite the delivery of your transcript(s) and the completion of your application.

If you would like your transcripts to be sent by mail, please use the following address:

Application Processing Services

datascience@berkeley 7900 Harkins Road Suite #501 Lanham, MD 20706

A high level of quantitative ability

This should be demonstrated by at least one of the following qualifications:

Work experience that demonstrates your quantitative abilities Academic coursework that demonstrates quantitative aptitude A problem-solving mindset A high level of analytical reasoning ability and a problem-solving mindset as demonstrated in academic and/or professional performance. A working knowledge of fundamental concepts

This includes knowledge of data structures, algorithms and analysis of algorithms, and linear algebra. Applicants who lack this experience in their academic or work background but meet all other requirements for admission will be asked to complete a bridge course before enrolling in the Applied Machine Learning course. Applicants may also consider enrolling in third party courses to expand their knowledge of math and programming.

The ability to communicate effectively

This should be demonstrated by at least one of the following qualifications:

Academic performance Professional experience Strong essays that demonstrate effective communication skills

Programming proficiency

Proficiency in programming languages, such as Python or Java, should be demonstrated by prior work experience or advanced coursework. Applicants who lack this experience in their academic or work background but meet all other admission requirements will be required to take the Introduction to Data Science Programming course in their first term.

Contact an Admissions Counselor today with any questions at 855-918-2299, or fill out a brief form to learn more about the Master of Information and Data Science.

Courses

Foundation Courses (12 Units)

Introduction to Data Science Programming Research Design and Application for Data and Analysis Statistics for Data Science Fundamentals of Data Engineering Applied Machine Learning

Advanced Courses (12 Units)

Experiments and Causal Interference Behind the Data: Humans and Values Deep Learning in the Cloud and at the Edge Statistical Methods for Discrete Response, Time Series, and Panel Data Machine Learning at Scale Natural Language Processing with Deep Learning Data Visualization

Capstone Course (3 Units) Synthetic Capstone Course

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