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Ph.D. in Economics

The Ph.D. program at Berkeley is designed for students interested in pursuing advanced study and conducting original research in Economics. The Ph.D. degree is awarded in recognition of the recipient's qualifications as a general economist and of the ability to make scholarly contributions in fields of specialization. Additionally, the Economics Ph.D. program is residential, there is no remote enrollment option. 

In advancing to the Ph.D. degree, students pass through two major stages:

• Preparation for candidacy typically takes two to three years. During the first two semesters, students take courses to achieve competence in econometric methods, methods of economic history and fundamentals of microeconomic and macroeconomic theory. During the next two years, students prepare for examination in two fields of specialization of their choosing, prepare a dissertation prospectus, and take an oral examination. When these steps are completed, students are advanced to candidacy.
• Completion of a dissertation after advancing to candidacy typically takes one to two years. The dissertation must be based on original research and represent a significant contribution to the body of Economic knowledge.

The entire process takes approximately five to six years, although some students are able to complete the program in less time. Below is an overview of the program requirements by year and other pertinent information.

ECONOMICS GRADUATE STUDENT SERVICES

The Economics Student Services Mission is to advise our students holistically by providing a high standard of service in a supportive and collaborative environment.  Professional and peer advisors work as a team to provide accurate information in a timely manner.  We partner with faculty to assist students in engaging with the campus and the global economic community.  We value fairness, diversity, and the important roles our students, faculty, and staff in the Department of Economics play at the University of California, Berkeley.

Meet the members of the Economics Graduate Student Services advising team!

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Master of Financial Engineering Program

Launch your career in finance, data science, or technology. Gain skills, insights, and hands-on experience. Connect with top firms. Prepare to make your mark.

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Challenge yourself in the nation's top-ranked mfe program.

Dive into an intense learning journey mapped out to take your career in the right direction.

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Join a brilliant, diverse, and talented community that embraces intellectual curiosity and supports your success.

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Find your place in a leading finance or technology firm through unrivaled, personalized career support.

Berkeley MFE by the Numbers

Students in the class of 2023 who received full-time offers

MFE Program Ranking ( TFE Times , 2023)

For Finance Research (CFAR Ranking, 2023)

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Financial Engineering

About the Program

The Berkeley Master of Financial Engineering (MFE) degree offers two options of study: one-year graduate degree option and our part-time degree option (completed over two-years). Both are offered by the Haas School of Business and cover the same curriculum. Students enrolled in the MFE Program learn to combine finance, mathematics, data science and machine learning, and computer programming skills to optimize decision making. They enter careers in areas like portfolio management, trading, data science, quantitative research, and development, as well as strats and modeling among many others.

Graduates of the MFE Program find positions in hedge funds, fintech firms, commercial and investment banking, insurance and reinsurance, corporate treasuries, private equity and asset management. Specializations include asset/liability modeling/optimization, security structuring, derivative valuation, sales and trading, consulting, asset management, research, option-based securities valuation, special hedging, real-option investment analysis, and risk management.

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Admission to the Program

Please visit the departmental website at mfe.haas.berkeley.edu  for more information.

Admission to the University

Minimum requirements for admission.

The following minimum requirements apply to all graduate programs and will be verified by the Graduate Division:

• A bachelor’s degree or recognized equivalent from an accredited institution;
• A grade point average of B or better (3.0);
• If the applicant comes from a country or political entity (e.g., Quebec) where English is not the official language, adequate proficiency in English to do graduate work, as evidenced by a TOEFL score of at least 90 on the iBT test, 570 on the paper-and-pencil test, 230 on the computer-based test, or an IELTS Band score of at least 7 (note that individual programs may set higher levels for any of these); and
• Sufficient undergraduate training to do graduate work in the given field.

Applicants Who Already Hold a Graduate Degree

The Graduate Council views academic degrees not as vocational training certificates but as evidence of broad training in research methods, independent study, and articulation of learning. Therefore, applicants who already have academic graduate degrees should be able to pursue new subject matter at an advanced level without need to enroll in a related or similar graduate program.

Programs may consider students for an additional academic master’s or professional master’s degree only if the additional degree is in a distinctly different field.

Applicants admitted to a doctoral program that requires a master’s degree to be earned at Berkeley as a prerequisite (even though the applicant already has a master’s degree from another institution in the same or a closely allied field of study) will be permitted to undertake the second master’s degree, despite the overlap in field.

The Graduate Division will admit students for a second doctoral degree only if they meet the following guidelines:

• Applicants with doctoral degrees may be admitted for an additional doctoral degree only if that degree program is in a general area of knowledge distinctly different from the field in which they earned their original degree. For example, a physics PhD could be admitted to a doctoral degree program in music or history; however, a student with a doctoral degree in mathematics would not be permitted to add a PhD in statistics.
• Applicants who hold the PhD degree may be admitted to a professional doctorate or professional master’s degree program if there is no duplication of training involved.

Applicants may apply only to one single degree program or one concurrent degree program per admission cycle.

Any applicant who was previously registered at Berkeley as a graduate student, no matter how briefly, must apply for readmission, not admission, even if the new application is to a different program.

Required Documents for Applications

• Transcripts:   Applicants may upload  unofficial  transcripts with your application for the departmental initial review.  If the applicant is admitted,  then  official  transcripts of all college-level work will be required. Admitted applicants must request a current transcript from every post-secondary school attended, including community colleges, summer sessions, and extension programs. Official transcripts must be in sealed envelopes as issued by the school(s) attended.  If you have attended Berkeley, upload your unofficial transcript with your application for the departmental initial review.  If you are admitted,  an official transcript with evidence of degree conferral  will not  be required.
• Letters of recommendation:  Applicants may request online letters of recommendation through the online application system. Hard copies of recommendation letters must be sent directly to the program, not the Graduate Division.
• Evidence of English language proficiency:  All applicants from countries or political entities in which the official language is not English are required to submit official evidence of English language proficiency. This applies to applicants from Bangladesh, Burma, Nepal, India, Pakistan, Latin America, the Middle East, the People’s Republic of China, Taiwan, Japan, Korea, Southeast Asia, most European countries, and Quebec (Canada). However, applicants who, at the time of application, have already completed at least one year of full-time academic course work with grades of B or better at a US university may submit an official transcript from the US university to fulfill this requirement. The following courses will not fulfill this requirement: 1) courses in English as a Second Language, 2) courses conducted in a language other than English, 3) courses that will be completed after the application is submitted, and 4) courses of a non-academic nature. If applicants have previously been denied admission to Berkeley on the basis of their English language proficiency, they must submit new test scores that meet the current minimum from one of the standardized tests.
• GRE or GMAT:  The GRE/GMAT is optional for prospective students who meet the following criteria:

Have earned or will earn a Ph.D. degree before starting the program.

Have earned a four-year bachelor's degree from an accredited college or university in the U.S. with a GPA of 3.70 or higher. Please note that an undergraduate degree from an institution outside of the U.S. is not eligible.

Degrees from undergraduate institutions that do not grade on a 4.0 scale are not eligible. (GPAs cannot be rounded up to meet the minimum 3.70).

Applicants who are applying prior to the completion of their undergraduate degree must have earned the required GPA at the time of application to be eligible. Applicants must maintain the required minimum GPA through the completion of their undergraduate degree.

If you meet the above criteria but would still like to submit a GRE or GMAT score, you may do so and the admissions committee will consider it along with the rest of your application materials. Additionally, while a test score is not required to submit an application, the admissions committee reserves the right to ask for a valid GMAT or GRE score to further assess an applicant's candidacy for admission. 

Where to Apply

Please visit the departmental website at  mfe.haas.berkeley.edu  for application information.

Master's Degree Requirements

Unit requirements: 28 units.

Research Resources

Mfe data lab.

Dedicated lab that includes the following resources:  Bloomberg terminals (4), Access to FactSet, DataStream, Thompson Reuters, High Frequency trading server with NYSE TAQ, NASDAQ TotalView-ITCH, and ICAP EBS currency data; Software: SPSS, Mathematica, SAS, Visual Studio, EViews, RStudio, Anaconda, R, OneTick, kdb+, Rotman Interactive Trader, Numerix Bloomberg Edition, ETNA Trader, Kensho, and WRDS.  Access to high-performance research cluster is available for individuals and for group scenarios. 

Research Lab Manager

Dedicated staff member assists students with lab and technical needs.  Depending on project needs, the lab manager provides appropriate resources for high-performance computing.

Business and Economics Library at the Haas School of Business

Access to Financial Times, Wall Street Journal, and all library resources.

Professional Development Activities

1. Extensive assistance with placement in internship and full-time positions.

2. Workshops on job search skills, e.g., cover letter/resume writing and interviewing.

3. Financial Practice Seminars with professionals who discuss career paths available and industry needs. Workshops on relevant skills, e.g., programming languages.

For more information, visit the departmental website at mfe.haas.berkeley.edu .

Note: Not all courses are offered every year.

MFE 230A Investments and Derivatives 2 or 3 Units

Terms offered: Spring 2015, Spring 2013, Fall 2007 The course discusses the basic theories of asset pricing. It begins with the standard discounted cash flow analysis, and generalizes this approach to develop the No Arbitrage Pricing Technique for security valuation. Topics will be fixed income securities, derivatives, contingent claims, basic principles of optimal portfolio theory, models of equilibrium asset pricing, including CAPM and related Factor Models. Investments and Derivatives: Read More [+]

Hours & Format

Fall and/or spring: 8 weeks - 4-6 hours of lecture per week

Summer: 8 weeks - 4-6 hours of lecture per week

Additional Format: Four hours of lecture per week for eight weeks.

Additional Details

Subject/Course Level: Masters in Financial Engineering/Graduate

Grading: Letter grade.

Investments and Derivatives: Read Less [-]

MFE 230D Derivatives: Quantitative Methods 2 Units

Terms offered: Summer 2008 10 Week Session, Fall 2007, Summer 2007 10 Week Session This course emphasizes the pricing of derivatives in continuous time, from the formulation of the pricing problem to the implementation of computational and numerical solution techniques. Derivatives: Quantitative Methods: Read More [+]

Rules & Requirements

Prerequisites: 230A-230B

Fall and/or spring: 8 weeks - 4 hours of lecture and 4 hours of lecture per week

Summer: 10 weeks - 3 hours of lecture per week

Additional Format: Four hours of lecture per week for eight weeks. Four hours of lecture per week for eight weeks.

Derivatives: Quantitative Methods: Read Less [-]

MFE 230E Empirical Methods in Finance 2 or 3 Units

Terms offered: Spring 2020, Spring 2019, Spring 2018 This course reviews probability and statistical techniques commonly used in quantitative finance. It includes a review of normal, lognormal, CEV distribution, estimation and nonparametric techniques commonly used in finance (MLE, GMM, GARCH). Students will be introduced to financial databases and estimation application software to estimate volatilities and correlations and their stability. Empirical Methods in Finance: Read More [+]

Prerequisites: Business Administration 230A-230B

Fall and/or spring: 8 weeks - 4-6 hours of lecture and 1-1 hours of discussion per week

Summer: 8 weeks - 4-6 hours of lecture and 1-1 hours of discussion per week

Additional Format: Six hours of lecture per week for eight weeks.

Empirical Methods in Finance: Read Less [-]

MFE 230G Equity and Currency Markets 2 Units

Terms offered: Fall 2015, Fall 2012, Fall 2006 This course reviews various aspects of equity and currency markets and their relative importance. It provides models of and historical evidence on the average returns and volatility of returns on equities, on the trade-to-trade equity price behavior, on trading volume and patterns, and primary financial risks. Determination of spot and forward rates and volatility, volume, high frequency dynamics and dealer behavior are examined. Equity and Currency Markets: Read More [+]

Summer: 7.5 weeks - 4 hours of lecture and 4 hours of lecture per week

Additional Format: Four hours of Lecture and Four hours of Lecture per week for 7.5 weeks.

Formerly known as: Business Administration 230G

Equity and Currency Markets: Read Less [-]

MFE 230GA Equity Markets 1 Unit

Terms offered: Prior to 2007 This course will cover active equity portfolio management including the more general quantitative theory of active management. We will view active management as an optimization problem trading off expected returns against risk and the cost of trading. Modules will cover forecasting returns, risk, and cost. We will also cover how to research active strategies and the analytics that support the enterprise. We will discuss various categories of active equity strategies and provide an introduction to current approaches. Equity Markets: Read More [+]

Credit Restrictions: Students will receive no credit for MFE 230GA after completing MFE 230G . A deficient grade in MFE 230GA may be removed by taking MFE 230G .

Fall and/or spring: 3 weeks - 6 hours of lecture per week

Additional Format: Six hours of lecture per week for three weeks.

Equity Markets: Read Less [-]

MFE 230GB Currency Markets 1 Unit

Terms offered: Prior to 2007 This course is dedicated to currency markets: market organization, determination of spot and forward rates, and links to international finance more broadly. Topics include: The FX market: organization, players, and instruments, FX arrangements and capital controls, Different approaches to exchange rate determination/forecasting, Conventional and unconventional monetary policy and exchange rate, Dominant currency paradigm/ dollar safety and FX Markets in the Post-COVID era. Currency Markets: Read More [+]

Credit Restrictions: Students will receive no credit for MFE 230GB after completing MFE 230G . A deficient grade in MFE 230GB may be removed by taking MFE 230G .

Currency Markets: Read Less [-]

MFE 230H Financial Risk Measurement and Management 2 Units

Terms offered: Fall 2015, Fall 2012, Fall 2008 This course examines risk measurement and management including market risk, credit risk, liquidity risk, settlement risk, volatility risk, kurtosis risk and other types of financial risks. Topics will include risk management techniques for different types of contracts and portfolios such as duration, portfolio beta, factor sensitivities, VAR, dynamic portfolio analysis and extreme value analysis and other risk management techniques. Financial Risk Measurement and Management: Read More [+]

Formerly known as: Business Administration 230H

Financial Risk Measurement and Management: Read Less [-]

MFE 230I Fixed Income Markets 2 or 3 Units

Terms offered: Fall 2007, Summer 2007 10 Week Session, Summer 2006 10 Week Session This course provides a quantitative approach to fixed income securities and bond portfolio management. Topics include fixed income security markets, pricing and uses for portfolio management or for hedging interest rate risk, bond mathematics, term structure measurement and theory, immunization techniques, and the modern theory of bond pricing, and derivative instruments. Fixed Income Markets: Read More [+]

Prerequisites: 230D

Fall and/or spring: 8 weeks - 3-4 hours of lecture per week

Summer: 8 weeks - 3-4 hours of lecture per week

Additional Format: Three or four hours of lecture per week for eight weeks.

Fixed Income Markets: Read Less [-]

MFE 230J Financial Innovation with Data Science Applications 1 - 2 Units

Terms offered: Fall 2015, Fall 2008, Fall 2006 This course will stress financial innovation in the traditional financial markets, and innovation opportunities in the newer disciplines of long and short term economic markets. Some examples of the later include livelihood insurance, home-equity insurance, inequality insurance, intergenerational social security, international agreements, and individual pension investment strategies. Financial Innovation with Data Science Applications: Read More [+]

Fall and/or spring: 10 weeks - 1-3 hours of lecture per week

Summer: 8 weeks - 2-6 hours of lecture per week

Additional Format: Two hours of lecture per week for seven and one-half weeks.

Financial Innovation with Data Science Applications: Read Less [-]

MFE 230K Dynamic Asset Management 2 Units

Terms offered: Spring 2015, Spring 2010, Spring 2009 This course reviews portfolio theory and pricing models. It includes: risk models for international portfolio returns, models of optimal allocation of funds, exchange rate uncertainty and criteria for judging the performance of managers and models; different types of portfolios/instruments, different types of applications, and strategies to achieve various investment objectives. Dynamic Asset Management: Read More [+]

Formerly known as: Business Administration 230K

Dynamic Asset Management: Read Less [-]

MFE 230M Asset-Backed Security Markets 2 Units

Terms offered: Fall 2015, Spring 2015, Spring 2010 This course extends the study of fixed income securities to advanced topics on mortage and other asset-backed securities. Topics will include basic mechanics of structuring deals for mortgage-related securities, credit cards, leases, and other debt markets and the risk management techniques employed in the securitization process for these assets. The valuation of pooled assets and derivative bonds using Monte Carlo and option pricing techniques , and trading strategies are also evaluated. Asset-Backed Security Markets: Read More [+]

Prerequisites: Business Administration 230D and 230I

Formerly known as: Business Administration 230M

Asset-Backed Security Markets: Read Less [-]

MFE 230N Applied Finance Project 0.0 Units

Terms offered: Fall 2015, Fall 2012, Fall 2008 Students will be required to complete an applied quantitative finance project that explores a quantitative finance problem that might be met in practice and involves the development or use of quantitative financial technique. Applied Finance Project: Read More [+]

Prerequisites: Participation requires prior approval of the supervising faculty

Summer: 7.5 weeks - 6 hours of lecture and 6 hours of lecture per week

Additional Format: Independent study. Independent study. Independent study.

Grading: Letter grade. This is part one of a year long series course. A provisional grade of IP (in progress) will be applied and later replaced with the final grade after completing part two of the series.

Formerly known as: Business Administration 230N-230O

Applied Finance Project: Read Less [-]

MFE 230O Applied Finance Project 1 - 3 Units

Terms offered: Spring 2015, Spring 2010, Spring 2009 Students will be required to complete an applied quantitative finance project that explores a quantitative finance problem that might be met in practice and involves the development or use of quantitative financial technique. Applied Finance Project: Read More [+]

Grading: Letter grade. This is part two of a year long series course. Upon completion, the final grade will be applied to both parts of the series.

MFE 230P Financial Data Science 2 Units

Terms offered: Fall 2015 This course proposes a guided tour through optimization models arising in practical finance. These problems include ones that are traditionally associated with optimization, including asset and liability management, asset pricing, and portfolio optimization. We also describe optimization models arising in model calibration, predication and estimation, and risk analysis. The course includes some recent approaches to the analysis of other kinds of financial data, such as text (financial news) data. Financial Data Science: Read More [+]

Summer: 6 weeks - 5 hours of lecture and 5 hours of lecture per week

Additional Format: Five hours of Lecture and Five hours of Lecture per week for 6 weeks.

Financial Data Science: Read Less [-]

MFE 230Q Stochastic Calculus with Asset Pricing Applications 2 Units

Terms offered: Spring 2018, Spring 2015, Fall 2007 The course introduces the students to techniques from stochastic analysis employed in mathematical finance. Topics include: stochastic processes, brownian motion, stochastic integral, differentials and Ito's formula; martingales. Stochastic Calculus with Asset Pricing Applications: Read More [+]

Summer: 8 weeks - 4 hours of lecture per week

Additional Format: Four hours of lecture per week for eight weeks. Four hours of lecture per week for eight weeks. Four hours of lecture per week for eight weeks.

Stochastic Calculus with Asset Pricing Applications: Read Less [-]

MFE 230R Advanced Computational Finance 2 Units

Terms offered: Fall 2008, Fall 2006, Fall 2005 This course builds on the techniques learned in 230D, Quantitative Methods for Derivative Pricing. The focus is to gain a deeper analysis of numerical and computational issues in pricing and calibration. The orientation of the course is hands-on, with heavy use of computational techniques applied to case projects. The primary objective of this course is to prepare students to tackle the latest challenges in quantitative pricing that they are likely to encounter in cutting-edge financial institutions. Advanced Computational Finance: Read More [+]

Summer: 8 weeks - 2-4 hours of lecture and 2-4 hours of lecture per week

Additional Format: Two to four hours of lecture per week for eight weeks. Two to four hours of lecture per week for eight weeks. Two to four hours of lecture per week for eight weeks. Two to four hours of lecture per week for eight weeks.

Advanced Computational Finance: Read Less [-]

MFE 230S Behavioral Finance 1 or 2 Units

Terms offered: Spring 2020, Spring 2019, Spring 2018 Over the last 25 years, psychologists have come to better understand the processes by which people make judgements and decisions. They have identified common judgement and decision heuristics and the biases associated with these. An understanding of one's own decision biases and those of others is an important tool for managers. Behavioral Decision Theory has also contributed to our understanding of financial markets. This course will discuss the common biases and heuristics. Behavioral Finance: Read More [+]

Fall and/or spring: 8 weeks - 4 hours of lecture, 4 hours of lecture, 1 hour of discussion, and 1 hour of discussion per week

Additional Format: Four hours of Lecture, Four hours of Lecture, One hour of Discussion, and One hour of Discussion per week for 8 weeks.

Behavioral Finance: Read Less [-]

MFE 230T Topics in Financial Engineering 1 - 5 Units

Terms offered: Spring 2015, Summer 2013 10 Week Session, Fall 2012 Advanced study in the field of finance engineering that will address current and emerging issues. Topics will vary with each offering and will be announced at the beginning of each term. Topics in Financial Engineering: Read More [+]

Repeat rules: Course may be repeated for credit when topic changes.

Fall and/or spring: 15 weeks - 1-6 hours of lecture per week

Summer: 8 weeks - 2-12 hours of lecture per week

Additional Format: Two to ten hours of lecture per week for eight weeks. Two to ten hours of lecture per week for eight weeks. Two to ten hours of lecture per week for eight weeks. Two to ten hours of lecture per week for eight weeks.

Grading: The grading option will be decided by the instructor when the class is offered.

Topics in Financial Engineering: Read Less [-]

MFE 230V Credit Risk Modeling 2 Units

Terms offered: Fall 2008, Fall 2005, Fall 2004 Focuses on the techniques currently used to model credit risk. The course will cover default probabilities, loss given default, correlation, credit portfolio analytics, bond valuation, loan valuation, and credit derivative valuation. Emphasis will be placed on model building, model validation, and interpreting model output. Students will be required to do some high-level programming in a package such as Matlab. Some empirical testing exercises will also be part of the project work. Credit Risk Modeling: Read More [+]

Summer: 8 weeks - 4 hours of lecture and 4 hours of lecture per week

Credit Risk Modeling: Read Less [-]

MFE 230VA Credit Risk: Economic Concepts 1 Unit

Terms offered: Spring 2010, Summer 2006 10 Week Session Introduction to credit risk modeling and conceptual overview of current techniques. Covers default probabilities, loss given default, correlation, credit portfolio analytics, bond valuation, loan valuation, and credit derivative valuation. Prepares students who are interested in a second course that will focus on model building. Students not interested in the technical details of modeling but who desire an understanding of how credit risk modeling is used in practice will benefit from taking this course. Credit Risk: Economic Concepts: Read More [+]

Fall and/or spring: 6 weeks - 3 hours of lecture and 3 hours of lecture per week

Additional Format: Four hours of Lecture per week for 8 weeks. Three hours of Lecture and Three hours of Lecture per week for 6 weeks.

Credit Risk: Economic Concepts: Read Less [-]

MFE 230VB Credit Risk: Quantitative Modeling 1 Unit

Terms offered: Fall 2006 Focuses on the techniques currently used to model credit risk. The course will cover default probabilities, loss given default, correlation, credit portfolio analytics, bond valuation, loan valuation, and credit derivative valuation. Emphasis will be placed on model building, model validation, and interpreting model output. Students will be required to do some high-level programming in a package such as MATLAB. Some empirical testing exercises will also be part of the project work. Credit Risk: Quantitative Modeling: Read More [+]

Summer: 6 weeks - 3 hours of lecture and 3 hours of lecture per week

Additional Format: Three hours of Lecture and Three hours of Lecture per week for 6 weeks.

Credit Risk: Quantitative Modeling: Read Less [-]

MFE 230W Accounting and Taxation of Derivatives 1 Unit

Terms offered: Fall 2007, Summer 2007 10 Week Session, Summer 2006 10 Week Session This course provides a framework to allow students the understanding of the accounting and tax issues related to derivatives and hedging. It also fulfills the needs of students seeking jobs in the corporate sector and/or seeking securities-structuring assignments in the financial services sector. A basic understanding of financial accounting is required. Accounting and Taxation of Derivatives: Read More [+]

Summer: 8 weeks - 2.5 hours of lecture and 2.5 hours of lecture per week

Additional Format: Two and one-half hours of Lecture and Two and one-half hours of Lecture per week for 8 weeks.

Accounting and Taxation of Derivatives: Read Less [-]

MFE 230X High Frequency Finance 1 or 2 Units

Terms offered: Spring 2015 This course introduces basic concepts of high frequency finance and discusses recent developments in market microstructure, electronic trading, and high frequency data modeling. Topics include trading basics and price discovery, distributional properties of financial time series, tick data analysis, trade direction algorithms, trading benchmarks, sources of risk, and trading strategies (including back-testing challenges, benchmark and hedging strategies, and arbitrage and program trading). High Frequency Finance: Read More [+]

Fall and/or spring: 10 weeks - 3 hours of lecture per week

Additional Format: Three hours of lecture per week per unit. Three hours of lecture per week per unit. Three hours of lecture per week per unit. Three hours of lecture per week per unit.

High Frequency Finance: Read Less [-]

MFE 230Y Ethics and Regulation in Financial Markets 1 Unit

Terms offered: Prior to 2007 This course is an introduction to the legal rules which govern financial markets and institutions in general but also, specifically related to derivatives. The main purpose of legal rules and regulations is to ensure a smooth functioning of financial markets, as well as the safety and soundness of the overall financial system. We will examine the main areas of law and regulation, as they pertain to the centralized exchanges and the over the counter markets and the role of regulatory arbitrage. We will specifically focus on Dodd-Frank and Basel III and how these rules came about as a response to the financial crisis. We will also explore the role of ethics in filling in the gaps that the law fails to fill. Ethics and Regulation in Financial Markets: Read More [+]

Fall and/or spring: 6 weeks - 3 hours of lecture per week

Summer: 6 weeks - 3 hours of lecture per week

Ethics and Regulation in Financial Markets: Read Less [-]

MFE 230ZA Deep Learning and Applications I 1 Unit

Terms offered: Prior to 2007 Topics include supervised, unsupervised, and reinforcement learning industry tools to develop machine learning systems. Data collection and processing (APIs, web scraping, and Hadoop, MapReduce, Spark), multilayer perceptron (deep neural nets, training deep neural nets, convolutional, neural networks, recurring neural networks, Word2Vec). The course will end with a session on solving practical problems with deep learning. Deep Learning and Applications I: Read More [+]

Prerequisites: MFE 230P or equivalent

Fall and/or spring: 4 weeks - 4 hours of lecture per week

Additional Format: Four hours of lecture per week for four weeks.

Deep Learning and Applications I: Read Less [-]

MFE 230ZB Deep Learning and Applications II 1 Unit

Terms offered: Prior to 2007 Topics include spectral representation, long memory processes “shallow models”: ARMA, filter banks, SVR/SVM, random forests and Probabilistic graphical networks; deep models: RNNs and CNNs for sequential modeling, attention networks deep learning frameworks, basic models and causal loss functions for financial time-series prediction. Distributed representations of discrete entities and applications in Natural Language Processing data and model fusion strategies , irregular time series low cost modeling strategies (model compression, cascades and low rank modeling). Deep Learning and Applications II: Read More [+]

Deep Learning and Applications II: Read Less [-]

MFE W230A Investments and Derivatives 2 - 3 Units

Terms offered: Prior to 2007 The course discusses the basic theories of asset pricing. It begins with the standard discounted cash flow analysis, and generalizes this approach to develop the No Arbitrage Pricing Technique for security valuation. Topics will be fixed income securities, derivatives, contingent claims, basic principles of optimal portfolio theory, models of equilibrium asset pricing, including CAPM and related Factor Models. Investments and Derivatives: Read More [+]

Credit Restrictions: Students will receive no credit for MFE W230A after completing MFE 230A . A deficient grade in MFE W230A may be removed by taking MFE 230A .

Fall and/or spring: 8 weeks - 4-6 hours of web-based lecture per week

Summer: 8 weeks - 4-6 hours of web-based lecture per week

Additional Format: Four to six hours of web-based lecture per week for 8 weeks. Four to six hours of web-based lecture per week for 8 weeks.

Online: This is an online course.

MFE W230D Derivatives: Quantitative Methods 2 Units

Terms offered: Prior to 2007 This course emphasizes the pricing of derivatives in continuous time, from the formulation of the pricing problem to the implementation of computational and numerical solution techniques. Derivatives: Quantitative Methods: Read More [+]

Prerequisites: MFE 230A , MFA 230E, MFE 230Q . Co-requisite MFE 230I

Credit Restrictions: Students will receive no credit for MFE W230D after completing MFE 230D . A deficient grade in MFE W230D may be removed by taking MFE 230D .

Fall and/or spring: 8 weeks - 4 hours of web-based lecture per week

Summer: 10 weeks - 3 hours of web-based lecture per week

MFE W230E Empirical Methods in Finance 2 - 3 Units

Terms offered: Prior to 2007 This course reviews probability and statistical techniques commonly used in quantitative finance. It includes a review of normal, lognormal, CEV distribution, estimation and nonparametric techniques commonly used in finance (MLE, GMM, GARCH). Students will be introduced to financial databases and estimation application software to estimate volatilities and correlations and their stability. Empirical Methods in Finance: Read More [+]

Prerequisites: MFE 230A -230B

Credit Restrictions: Students will receive no credit for MFE W230E after completing MFE 230E . A deficient grade in MFE W230E may be removed by taking MFE 230E .

Fall and/or spring: 8 weeks - 4-6 hours of web-based lecture and 1-2 hours of web-based discussion per week

Summer: 8 weeks - 4-6 hours of web-based lecture and 1-2 hours of web-based discussion per week

Additional Format: One to two hours of web-based discussion and four to six hours of web-based lecture per week for 8 weeks. One to two hours of web-based discussion and four to six hours of web-based lecture per week for 8 weeks.

MFE W230G Equity and Currency Markets 2 Units

Terms offered: Prior to 2007 This course reviews various aspects of equity and currency markets and their relative importance. It provides models of and historical evidence on the average returns and volatility of returns on equities, on the trade-to-trade equity price behavior, on trading volume and patterns, and primary financial risks. Determination of spot and forward rates and volatility, volume, high frequency dynamics and dealer behavior are examined. Equity and Currency Markets: Read More [+]

Credit Restrictions: Students will receive no credit for MFE W230G after completing MFE 230G . A deficient grade in MFE W230G may be removed by taking MFE 230G .

Fall and/or spring: 7 weeks - 4 hours of web-based lecture per week

Summer: 7 weeks - 4 hours of web-based lecture per week

Additional Format: Four hours of web-based lecture per week for seven weeks. Four hours of web-based lecture per week for seven weeks.

MFE W230H Financial Risk Measurement and Management 2 Units

Terms offered: Prior to 2007 This course examines risk measurement and management including market risk, credit risk, liquidity risk, settlement risk, volatility risk, kurtosis risk and other types of financial risks. Topics will include risk management techniques for different types of contracts and portfolios such as duration, portfolio beta, factor sensitivities, VAR, dynamic portfolio analysis and extreme value analysis and other risk management techniques. Financial Risk Measurement and Management: Read More [+]

Credit Restrictions: Students will receive no credit for MFE W230H after completing MFE 230H . A deficient grade in MFE W230H may be removed by taking MFE 230H .

Fall and/or spring: 7 weeks - 4 hours of web-based lecture per week 7 weeks - 4 hours of web-based lecture per week

MFE W230I Fixed Income Markets 2 - 3 Units

Terms offered: Prior to 2007 This course provides a quantitative approach to fixed income securities and bond portfolio management. Topics include fixed income security markets, pricing and uses for portfolio management or for hedging interest rate risk, bond mathematics, term structure measurement and theory, immunization techniques, and the modern theory of bond pricing, and derivative instruments. Fixed Income Markets: Read More [+]

Prerequisites: MFE 230D or MFE W230D

Credit Restrictions: Students will receive no credit for MFE W230I after completing MFE 230I . A deficient grade in MFE W230I may be removed by taking MFE 230I .

MFE W230J Financial Innovation with Data Science Applications 1 - 2 Units

Terms offered: Prior to 2007 This course will stress financial innovation in the traditional financial markets, and innovation opportunities in the newer disciplines of long and short term economic markets. Some examples of the later include livelihood insurance, home-equity insurance, inequality insurance, intergenerational social security, international agreements, and individual pension investment strategies. Financial Innovation with Data Science Applications: Read More [+]

Credit Restrictions: Students will receive no credit for MFE W230J after completing MFE 230J . A deficient grade in MFE W230J may be removed by taking MFE 230J .

Fall and/or spring: 7 weeks - 2-6 hours of web-based lecture per week

Summer: 7 weeks - 2-6 hours of web-based lecture per week

MFE W230K Dynamic Asset Management 2 Units

Terms offered: Prior to 2007 This course reviews portfolio theory and pricing models. It includes: risk models for international portfolio returns, models of optimal allocation of funds, exchange rate uncertainty and criteria for judging the performance of managers and models; different types of portfolios/instruments, different types of applications, and strategies to achieve various investment objectives. Dynamic Asset Management: Read More [+]

Credit Restrictions: Students will receive no credit for MFE W230K after completing MFE 230K . A deficient grade in MFE W230K may be removed by taking MFE 230K .

Additional Format: Four hours of web-based lecture per week for seven weeks.

MFE W230M Asset-Backed Security Markets 2 Units

Terms offered: Prior to 2007 This course extends the study of fixed income securities to advanced topics on mortage and other asset-backed securities. Topics will include basic mechanics of structuring deals for mortgage-related securities, credit cards, leases, and other debt markets and the risk management techniques employed in the securitization process for these assets. The valuation of pooled assets and derivative bonds using Monte Carlo and option pricing techniques, and trading strategies are also evaluated. Asset-Backed Security Markets: Read More [+]

Credit Restrictions: Students will receive no credit for MFE W230M after completing MFE 230M . A deficient grade in MFE W230M may be removed by taking MFE 230M .

Fall and/or spring: 7.5 weeks - 4 hours of lecture per week

Additional Format: Four hours of lecture per week for seven and one-half weeks.

MFE W230O Applied Finance Project 1 - 3 Units

Terms offered: Prior to 2007 Students will be required to complete an applied quantitative finance project that explores a quantitative finance problem that might be met in practice and involves the development or use of quantitative financial technique. Applied Finance Project: Read More [+]

Credit Restrictions: Students will receive no credit for MFE W230O after completing MFE 230O . A deficient grade in MFE W230O may be removed by taking MFE 230O .

Fall and/or spring: 7 weeks - 3-6 hours of web-based lecture per week

Summer: 7 weeks - 3-6 hours of web-based lecture per week

MFE W230P Financial Data Science 2 Units

Terms offered: Prior to 2007 This course proposes a guided tour through optimization models arising in practical finance. These problems include ones that are traditionally associated with optimization, including asset and liability management, asset pricing, and portfolio optimization. We also describe optimization models arising in model calibration, predication and estimation, and risk analysis. The course includes some recent approaches to the analysis of other kinds of financial data, such as text (financial news) data. Financial Data Science: Read More [+]

Credit Restrictions: Students will receive no credit for MFE W230P after completing MFE 230P . A deficient grade in MFE W230P may be removed by taking MFE 230P .

Summer: 5 weeks - 6 hours of web-based lecture per week

Additional Format: Six hours of web-based lecture per week for five weeks.

MFE W230Q Stochastic Calculus with Asset Pricing Applications 2 Units

Terms offered: Prior to 2007 The course introduces the students to techniques from stochastic analysis employed in mathematical finance. Topics include: stochastic processes, brownian motion, stochastic integral, differentials and Ito's formula; martingales. Stochastic Calculus with Asset Pricing Applications: Read More [+]

Credit Restrictions: Students will receive no credit for MFE W230Q after completing MFE 230Q . A deficient grade in MFE W230Q may be removed by taking MFE 230Q .

Summer: 8 weeks - 4 hours of web-based lecture per week

MFE W230S Behavioral Finance 1 - 2 Units

Terms offered: Prior to 2007 Over the last 25 years, psychologists have come to better understand the processes by which people make judgements and decisions. They have identified common judgement and decision heuristics and the biases associated with these. An understanding of one's own decision biases and those of others is an important tool for managers. Behavioral Decision Theory has also contributed to our understanding of financial markets. This course will discuss the common biases and heuristics. Behavioral Finance: Read More [+]

Credit Restrictions: Students will receive no credit for MFE W230S after completing MFE 230S . A deficient grade in MFE W230S may be removed by taking MFE 230S .

Fall and/or spring: 8 weeks - 2-4 hours of web-based lecture and 1-1 hours of web-based discussion per week

Additional Format: One hour of web-based discussion and two to four hours of web-based lecture per week for 8 weeks.

MFE W230X High Frequency Finance 1 - 2 Units

Terms offered: Prior to 2007 This course introduces basic concepts of high frequency finance and discusses recent developments in market microstructure, electronic trading, and high frequency data modeling. Topics include trading basics and price discovery, distributional properties of financial time series, tick data analysis, trade direction algorithms, trading benchmarks, sources of risk, and trading strategies (including back-testing challenges, benchmark and hedging strategies, and arbitrage and program trading). High Frequency Finance: Read More [+]

Credit Restrictions: Students will receive no credit for MFE W230X after completing MFE 230X . A deficient grade in MFE W230X may be removed by taking MFE 230X .

Fall and/or spring: 10 weeks - 3-6 hours of web-based lecture per week

MFE 293 Individually Supervised Study for Graduate Students 1 - 5 Units

Terms offered: Fall 2015, Spring 2015, Fall 2012 Individually supervised study of subjects not available to students in the regular schedule, approved by faculty adviser as appropriate for the students' programs. Individually Supervised Study for Graduate Students: Read More [+]

Prerequisites: Graduate standing

Repeat rules: Course may be repeated for credit without restriction.

Summer: 8 weeks - 1-5 hours of independent study and 1-5 hours of independent study per week

Additional Format: One to Five hour of Independent study and One to Five hour of Independent study per week for 8 weeks.

Individually Supervised Study for Graduate Students: Read Less [-]

Contact Information

Haas School of Business

Phone: 510-642-4417

Graduate Student Affairs Officer

Sonia Moctezuma

Berkeley-Haas MFE

[email protected]

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Study In Usa > Colleges In Usa > UC Berkeley > Programs > Ph.D[Finance]

Doctor of Philosophy [Ph.D] [Finance] From University of California

Doctor of Philosophy [Ph.D] (Finance)

Field of Study : 

₽1,113,229 /Yr

• The Doctor of Philosophy in Finance at the  University of California  is a 5 years program.
• It is an on-campus program offered on a full-time basis.
• A Haas PhD student interested in finance needs a strong course background in mathematics and statistics.
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• Students also attend frequent seminars to gain exposure to ongoing research, with speakers featured from other universities throughout the world.
• These seminars provide a forum for doctoral students to assess the relevance of academic research in their fields.
• Instruction in the program is separated into three general phases. The first encompasses formal coursework in basic and advanced subjects and generally requires two years.
• The second and third phases together usually require two to three years for completion.
• In addition to coursework, students are normally expected to serve as either teaching or research assistants for one or more semesters.
• In the classroom, faculty members emphasize both theory and practice by using a variety of teaching methods. 
• Rankings of academic reputation consistently place the Haas faculty solidly in the top ten of business schools worldwide.

Important Dates

Tuition Fees

Other Expenses

All admitted doctoral students receive a fellowship that covers the cost of tuition and health insurance, and provides a stipend.

Eligibility & Entry Requirement

Academic Eligibility:

• Students must complete a master's degree from an accredited institution.
• A GPA of 3.0 (B+ grade) on a 4.0 scale in the graduate school’s scale is required to be eligible for the program.
• Students have enough undergraduate training to do graduate work in their chosen field.
• The submission of GRE  and GMAT scores is compulsory for this program.

Indian Student Eligibility:

Indian students are eligible to apply if they meet one of the following eligibility criteria:

• Students must complete a 4-year undergraduate degree with first class in a relevant field from a recognized institution
• Or, students completed a master's degree with first-class in a relevant field from a recognized institution.

Along with the minimum eligibility requirements, international students hailing from non-English speaking countries need to prove English proficiency through  IELTS / TOEFL /any equivalent test to get admission to this program.

Scores Required

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Required Document List

The following required documents are:

• Resume:  It is the outline of academic achievements and/or awards, publications, relevant work, and/or volunteer experience.
• Three   Letter of Recommendation: Letter of recommendation which include information about the student, their connection with the person they are recommending, their qualification, and the specific skills they have.
• Statement of Purpose -  This is an essay or any written statement written by the student while applying.
• Personal History Statement:  It describe how their background, accomplishments, and life experiences led to their decision to pursue the graduate degree for which they are applying.
• College Transcripts:  The marks statement provided by the education board after completion of the previous academic qualifications.
• ELP Scores:  Students have to submit their English language proficiency scores like IELTS, TOEFL, or other test scores.

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Certificate Program in Finance

Hosea Lin is a senior FP&A manager, commercial, at Bio-Rad Laboratories.

Graduate, Certificate Program in Finance Read Hosea Lin's Story .

Enhance your understanding of financial risk management and learn to make effective investing, financing and working capital management decisions. Whether you are a financial professional—an investment adviser, securities broker or CPA—or wish to enter the field, you'll broaden your investment management and capital markets knowledge, as well as learn to create and deploy financial instruments to personal and institutional investors.

Required Courses

4 Course(s)

0 Course(s)

Begin by enrolling in Introduction to Risk Management and Financial Derivatives BUS ADM X436.5. 

8 Academic Unit(s)

0 Academic Unit(s)

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Contact Information

To contact the department office, email [email protected] .

Prerequisites

If you have no prior work experience or coursework in finance, we strongly recommend you have a solid academic foundation in the basic areas of finance (i.e., Financial Accounting UGBA XB102A and/or Basic Corporate Finance BUS ADM X430.1).

Proficiency with basic financial mathematics and an HP 12C (or HP 10b) calculator is important to your success. 

There are no admission requirements, but a bachelor's degree is also strongly recommended.

Curriculum Requirements

The curriculum consists of 4 required courses and 8 semester units of electives for a total of 15 semester units (225 hours of instruction). Candidates must pay a nonrefundable certificate registration fee.

All courses must be taken for a letter grade. To receive the certificate, students must complete each course with a grade of C or better. All coursework must be completed within five years. Most students take one-and-a-half to two years to complete the certificate.

How to Register

By registering, you declare your intention to complete the curriculum (domestic students only). To register online for the Certificate Program in Finance, complete these two steps:

• Submit the online registration , and complete your student account profile if you are a new student.
• Pay the nonrefundable registration fee through your shopping cart. Please allow 3–5 business days for the registration to appear in your student account.

If you are an international student, you must apply to Evening Certificate Programs. Learn more .

Estimated Cost

Each course is priced individually, and you pay the course fee at the time of enrollment. The certificate has an estimated cost of $6,245–6,735 (not including course materials or registration fee). Course fees are subject to change.

Certificate and Award Request

Once you have completed the certificate coursework, notify UC Berkeley Extension that you have completed the curriculum and request your certificate. Send an email to [email protected] . After your records have been reviewed, verified and approved (usually within two to three weeks), your certificate will be mailed to you.

Additional Information

Interested in Pursuing a Graduate Degree? If you are interested in pursuing an M.B.A. or other graduate degree, contact the registrar of the graduate school to which you are applying to inquire about course transfer. We cannot guarantee that any courses will be accepted into another institution, including UC Berkeley.

Advisory Board

Maren Amdal

Strategy Consultant

Interpro, Inc.

John Brunello

Financial Adviser

UBS Financial Services

John Kramer

Kensington Investment Group

Gustavo Manso

Finance Associate Professor

UC Berkeley Haas School of Business

Jeff Shen, Ph.D.

Managing Director

Mitchell Smith

MDSCO Financial Advisors

Victor Villagracia

Vice President, Wealth Management

QuantScape Asset Management, LLC

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New PhD course focuses on intersection of climate economics, sustainability

Rising to a critical need for more research and leadership in climate finance, Berkeley Haas has joined a group of top universities worldwide in offering an innovative online PhD course focused on the intersection of climate economics and sustainability.

Professors Adair Morse and Panos Patatoukas , co-faculty directors of the Sustainable & Impact Finance Initiative (SAIF) at Haas , began co-teaching the online class called “Financial Economics of Climate and Sustainability” this semester. 

They join faculty members from more than 10 schools including Stanford, Harvard, Yale, Columbia, and Oxford, who are teaching this course to a global cohort of nearly 1,000 students from 127 schools across 30 different countries. 

The goal is to inspire a new generation of climate leaders to embark on new research that leads to innovative ways of thinking about climate finance, Patatoukas said. “Our job as instructors will be to give them the tools and the frameworks and provide ways for them to start asking interesting questions,” he said. “Overall, it’s a really good time to more formally train our students in this space. It’s rapidly evolving, it’s messy, it’s not perfect, but that makes it interesting and exciting and an area of growth that is full of opportunities.”

The course will help create change in two areas. First, it encourages students to work outside of their academic silos and come together to share ideas. “Sometimes, in a business school, we’re thinking about these problems in isolation, but this is definitely a field where everybody has to work with each other to come up with better solutions,” Patatoukas said. Second, the course will encourage students to publish cutting-edge research. “We feel like our students will have an easier time getting published in an area that is so impactful and new where basic questions remain open,” he said. 

Each week, professors from different institutions will teach topics including climate, sustainability, and economic theory; corporate carbon disclosure; introduction to climate science; climate and asset pricing; and climate and investment management. All students enrolled in the course for credit will be required to submit an idea for a research project or a plan to review a set of sustainability papers from outside of the course by the last class.

“The timing is perfect for this course,” Patatoukas said. “As consensus has grown worldwide over the climate crisis, a transition to net zero isn’t happening fast enough.”

That’s where mobilizing massive amounts of capital to fight climate change comes into play.  An estimated $4 trillion to $5 trillion a year in resources will need to be financed and distributed to address climate global needs, said Terhilda Garrido , interim executive director of SAIF. “Only a fraction will be provided by governments,” she said. “This course addresses our need to mobilize innovative climate finance quickly, train leaders in finance, and learn from each other, globally. Climate is a global issue requiring global collaboration.”

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Film & Media

The graduate program in film & media, about the film & media ph.d. program.

Students in the Film & Media Ph.D. Program are encouraged to situate moving images within the larger theoretical and analytical frameworks of a range of other disciplines. They integrate the traditions of History, Law, Literature, Religion and Political Theory to the newer disciplines of Film Studies and Digital Media, applying the tools of Post-Structuralism, Psychoanalysis, New Historicism, Frankfurt School, Feminist Theory, Queer Theory, Post-Colonialism and Deconstruction. Many combine their degree study with a campus Designated Emphasis (graduate “minor”) in New Media, in Critical Theory, or in Women, Gender and Sexuality.

The Film & Media Ph.D. emphasizes film and media history and theory, but also includes a digital-media production component that can be interwoven with the student’s other areas of study. Through a cooperative effort with the Department of Art Practice and the Department of Theater, Dance, and Performance Studies, the Department of Film & Media has helped to found the Digital Media Labs Consortium, a humanities-based initiative that pools production resources and coordinates courses of instruction to widen the possibilities in advanced digital-media production. Art Practice also offers an MFA that includes production in digital media and new genres. Other production degrees on campus include the Graduate School of Journalism’s M.A. in documentary film production.

During their first two years, graduate students in the Film & Media Ph.D. take a course in Film Theory, a course in Film Historiography, a Proseminar that orients new students to the professional field of Film & Media Studies, and other seminars chosen from a range of electives (see sample topics in the “Courses” section). Beginning in their fifth semester, students begin to prepare in greater depth for their doctoral Qualifying Examinations and dissertation research. Because the Department of Film & Media at Berkeley is committed to interdisciplinary research, students are required to work in significant ways with faculty in other departments as well as with Film & Media Faculty.

The Film & Media Ph.D. has about 25 graduate students. Students are admitted to the program in the Fall semester only.  The application deadline for admission in Fall 2025 is December 3, 2024, 8:59 p.m. PST. Start the online application at:  Applying for Graduate Admission.  Please note that the Department of Film & Media admits students for a Ph.D. only. Although an M.A. degree is awarded after partial completion of the requirements for the Ph.D., there is no M.A. program as such with its own curriculum.

Graduate Advisors

Kathleen pera jangar, weihong bao.

Weihong Bao is Pamela P. Fong and Family Distinguished Chair in China Studies and an Associate Professor of Film and Media & East Asian Languages and Cultures, UC Berkeley. She has published widely on comparative media history and theory, media and environment, early cinema, war... Read more about Weihong Bao

Graduate Student Handbook 2024-2025

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An international student finds inspirational peers at Berkeley Haas

“I think there is general agreement that the most innovative minds in finance are in the U.S. So, it makes sense to study here, where you can learn from and be exposed to the top talent,” said Xiao Tian (Jason) Zhou, MFE 25.

Berkeley Haas, the number-one ranked Master of Financial Engineering program in the nation, attracts a fair share of international students. Indeed, the class of 25 is 85% international. The U.S. is the third country Xiao Tian has lived in. Born in China, he emigrated to Canada with his family as a child. “But coming to Berkeley is my first time away from my family in a new country,” he said. “And while Canadian culture has a lot in common with American culture, they still are separate cultures. It helps a lot that people at Berkeley Haas are so openminded and welcoming. My classmates in particular have helped me navigate this new environment.“

Two more culture shocks await Xiao Tian. In October, he will start an internship on the “algo trading team” at Morgan Stanley in New York City. “It is exactly the type of internship I wanted, aligned with my interest in market-making and my desire to improve my skills in an area that will become even more important in the years to come. I also am excited about being in New York City. For one thing, it is closer to my family outside Toronto. For another, as much as I am enjoying Berkeley, the energy in New York City is on a completely different level.”

Enjoying his time in Berkeley starts with his MFE classmates, who Xiao Tian describes as diverse and open to other cultures. “There are people with backgrounds in data science and coding. Some come from a business background and have only basic coding skills. But everyone is here to improve their skills in some way and to gain intuition into how math can be used in the real world to improve financial performance and financial markets. That applied knowledge is something that the professors and classes at Berkeley Haas are very good at.”

Two classes in the curriculum stood out for Xiao Tian. Derivatives: Quantitative Methods (MFE230D), taught by Dmitry Livdan , went “deep into the theory driving derivatives. But he also related the theory to how things work in the real world. We learned how to account for volatility in pricing derivates. We practiced Monte Carlos so we could see the results.” The second was Empirical Methods (MFE 230E). Although already familiar with the material covered, “I appreciated the practicality of the approach. We learned how to forecast and create models that will be useful in finance.”

Xiao Tian and his “study buddies” are awake and at the library early, eager to “talk through problems and approaches, to figure out what we’re missing and how to fill in the gaps.” They also know how to have fun outside of the classroom and library. He is part of a regular poker game. An avid rock climber, Xiao Tian was happy to introduce his new friends to the sport, and they convinced him to start playing tennis again. The varied Asian cultures represented in the Bay Area allow Xiao Tian to keep in touch with his Chinese roots. Apart from having an endless selection of Chinese restaurants to provide a taste of home, “I really like San Francisco’s Japantown, where I can buy the latest manga books and enjoy good ramen.”

Now midway through his degree, Xiao Tian looked back on how he prepared for entering the MFE program. He benefited from the Math Foundations and Statistics pre-program classes . “I also am grateful for the encouragement I got to participate in the Investment Portfolio Case Competition at Cornell before starting my studies. It took a lot of effort, but my team drove forward, and we all ended up classmates at Haas.”

One thing Xiao Tian wishes he had done more of before starting classes is talk with MFE alumni. With 1,500 MFEs and 450,000 UC Berkeley grads around the world, he would have had plenty to choose from in the Haas Alumni Network . “As a student, every Haas alum I have spoken with has been very open and willing to talk. They do not hold back their opinions, or their encouragement and they give the best advice,” he said. “I hope prospective students will say the same thing about me when I talk with them.”

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2024-2025 Tech Policy Fellows

Announcing the second cohort of uc berkeley tech policy fellows.

The CITRIS Policy Lab and the Goldman School of Public Policy are pleased to announce the 2024-2025 cohort of UC Berkeley Tech Policy Fellows.  These remarkable technology policy leaders bring diverse expertise from across the globe. Our fellows hail from prestigious organizations and institutions such as UNICEF, WIRED, federal agencies, and the European Commission, and leading industry players such as Google, Meta, Reddit, and Twitch. They represent a wide range of  backgrounds but are united by a commitment to leveraging technology for social good.

Fellows will spend 13 months conducting research, working on responsible technical/policy interventions, and engaging with the extended UC Berkeley academic community. Meet the Tech Policy Fellows below and read their full bios on the CITRIS Policy Lab  tech policy fellows webpage .

Joanna Smolinska is Counsellor for Digital and Deputy Head of the EU Office in San Francisco. She focuses on AI policy and regulation, online content moderation, open digital markets and innovation in the context of transatlantic relations. Before coming to SF, she worked for 15 years in the European Commission in Brussels across a wide range of policy areas and she was actively involved in the development of the EU Digital Services Act and Digital Markets Act. Of Polish nationality, Joanna graduated from Warsaw School of Economics, holds a Master’s Degree in Finance from the University of Geneva, Switzerland, and a Master’s Degree in European Law and Economic Analysis from the College of Europe in Bruges, Belgium.

Kambria Dumesnil is a technologist and innovator dedicated to the people-side of tech to ensure meaningful and impactful implementations.  She is the founder of the AI Innovation Lounge and a management analyst with expertise in research, analysis, and facilitation on workplace issues impacting workers. Her research interests include exploring the psychosocial impacts of artificial intelligence (AI) on workers, demonstrating her commitment to integrating technology with human-centric solutions.

Admissions Requirements

Application requirements.

Congratulations on taking the next step towards submitting your graduate application! To help you along the way, we’ve compiled a list of requirements to complete your graduate application.

It’s also important to check with the program to which you’re applying, as they may have additional requirements specific to their program of study and degree not listed on this page.

Minimum Admissions Requirements

The minimum graduate admissions requirements are:

• Expect to or hold a bachelor’s degree or recognized equivalent from an accredited institution.
• A satisfactory scholastic average, usually a minimum grade-point average (GPA) of 3.0 (B) on a 4.0 scale; and
• Enough undergraduate training and/or professional experience to do graduate work in your chosen field.

Academic Records

You must hold or expect to hold a bachelor’s degree prior to the start of classes from a U.S.accredited institution by one of the AACRAO regional accrediting agencies* or a recognized equivalent from an accredited institution outside of the U.S.

* Regionally accredited college or university means an institution of higher education accredited by one of the following regional accreditation associations in the United States:

• Middle States Commission on Higher Education
• New England Association of Schools and Colleges
• The Higher Learning Commission (formerly known as North Central Association of Colleges and Schools)
• Northwest Commission on Colleges and Universities
• Southern Association of Colleges and Schools Commission on Colleges
• WASC Senior College and University Commission

Graduates of accredited academic institutions outside the United States should hold a degree equivalent to a U.S. bachelor’s degree. Please contact Graduate Admissions with questions.

Bachelor’s Degree from a U.S. Institution

Required Records : Upload unofficial transcripts (ie, a scanned copy) from every post-secondary school that you have attended, including your undergraduate institution, community colleges, summer sessions, and extension programs.

Bachelor’s Degree from an International Institution

Required Records : Upload a scanned copy of your transcript and degree certificate for each institution after high school. If your academic records are in a language other than English or Spanish, you may submit translations in one of two ways:

• Submit translations prepared by certified translators from the American Translators Association or the Ministry of Education. Degree names and grades should be transcribed, not converted, into English words or the U.S. grades of A-F. 
• Submit an official World Education Services (WES) International Credential Advantage Package (ICAP) evaluation (opens in a new tab) . To electronically submit your WES ICAP, follow the instructions provided by WES. Select “University of California at Berkeley” as the recipient and “Graduate Admissions” 318 Sproul Hall #5900, Berkeley, CA 94720-5900 as the school/division.

Evidence of English Language Proficiency

All applicants who have completed a basic degree in a country/region in which the official language is not English are required to submit official evidence of English language proficiency. This requirement applies to institutions from Bangladesh, Nepal, India, Pakistan, Latin America, the Middle East, Israel, the People’s Republic of China, Taiwan, Japan, Korea, Southeast Asian countries, most European countries, and countries in Africa in which English is not the official language.

There are two standardized tests you may take: the Test of English as a Foreign Language (TOEFL), and the International English Language Testing System (IELTS).

Test of English as a Foreign Language (TOEFL)

TOEFL is administered by the Educational Testing Service (ETS). You can obtain detailed information from the TOEFL website .

We accept the internet based (iBT) and iBT Home Edition. We do not accept the TOEFL ITP or Duolingo .

For purposes of admission, your TOEFL test score must be at least 90 for the Internet-based test (IBT). Please contact individual academic departments for more information, as they may choose to require a higher score.

For Fall 2025, tests taken before June 1, 2023 will not be accepted even if your score was reported to UC Berkeley. Please send your test score directly from ETS to the institution code for UC Berkeley: 4833 for Graduate Organizations. We do not accept MyBest Scores.

International English Language Testing System (IELTS)

You can also submit scores from the Academic Modules of the International English Language Testing System (IELTS), which is jointly managed by the British Council, IDP:IELTS Australia, and the University of Cambridge ESOL Examinations. Consult the IELTS website to locate the office of the test center where you plan to take the test.

For purposes of admission, your most recent overall band score must be at least 7 on a 9-point scale.

For Fall 2025, tests taken before June 1, 2023 will not be accepted. All IELTS scores must be sent electronically from the testing center, and no institution code is required. Our address for identification purposes is: University of California, Berkeley, Graduate Division, Sproul Hall Rm 318, MC 5900, Berkeley, CA 94720. Please do not mail any score reports to us.

TOEFL/IELTS Exemption

To qualify for a TOEFL/IELTS exemption, you must fulfill one of the following options:

• Have a basic degree from a recognized institution in a country where the official language is English.
• Have completed a basic or advanced degree at an institution, in the United States or a United States institution abroad, where the language of instruction is English and the institution is accredited by one of the United States’ regional accrediting agencies.
• The following courses do not qualify for an exemption: courses in English as a Second Language, courses conducted in a language other than English, courses that will be completed after applicants submit their application, or courses of a nonacademic nature.

The TOEFL or IELTS must be submitted by applicants who do not meet the exemption criteria above.

Graduate Record Examination (GRE) and Other Test Scores

Some programs require applicants to take a standardized test such as the GRE General Test, a GRE Subject Test, the GMAT or MCAT. Consult the program to which you are applying for the department’s requirements.

For the GRE, send your test score directly from ETS to the institution code for UC Berkeley: 4833 for Graduate Organizations. No department code is needed unless specified by the specific program. ETS will not report test scores older than 5 years.

For the GMAT or MCAT, please consult your program for details.

Letters of Recommendation

The application requires at least three letters of recommendation. Applicants should check with their prospective program for questions . Your recommenders are asked to give their personal impressions of your intellectual ability, your aptitude in research or professional skills, your character, and the quality of your previous work and potential for future productive scholarship. Be sure to inform your recommenders of the program’s application deadline.

Applicants may waive the right to inspect their letters of recommendation on a voluntary basis. This option can be selected when you fill out the recommendation invite for your recommender.

The Graduate Division may verify the authenticity of academic letters of recommendation with the school or recommender.

Submit a Statement of Purpose and Personal History statement, along with any other essays the program you are applying to requires.

The Statement of Purpose should describe your aptitude and motivation to enter the program. This can include relevant details about your preparation or specialization in the field. This is a good place to share your academic plans, research interests, and future career goals. Read tips on how to craft your Statement of Purpose .

The Personal History statement describes how your own background and experiences influenced your decision to pursue a graduate degree. Read tips on how to write your Personal History statement .

Application Fee

If you are a U.S. citizen or current permanent resident, the non-refundable application fee is $135; for all others, the fee is $155.

Fee Waiver : U.S. citizens or permanent residents who demonstrate financial need or have participated in an eligible program may apply for an application fee waiver. See guidelines for waivers .

Fee Exemption : Some programs do not require the application fee payment. See guidelines for fee exemption .

• UC Berkeley
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The School of Information is UC Berkeley’s newest professional school. Located in the center of campus, the I School is a graduate research and education community committed to expanding access to information and to improving its usability, reliability, and credibility while preserving security and privacy.

• Career Outcomes
• Degree Requirements
• Paths Through the MIMS Degree
• Final Project
• Funding Your Education
• Admissions Events
• Request Information
• Capstone Project
• Jack Larson Data for Good Fellowship
• Tuition & Fees
• Women in MIDS
• MIDS Curriculum News
• MICS Student News
• Dissertations
• Applied Data Science Certificate
• ICTD Certificate
• Cybersecurity Clinic

The School of Information offers four degrees:

The Master of Information Management and Systems (MIMS) program educates information professionals to provide leadership for an information-driven world.

The Master of Information and Data Science (MIDS) is an online degree preparing data science professionals to solve real-world problems. The 5th Year MIDS program is a streamlined path to a MIDS degree for Cal undergraduates.

The Master of Information and Cybersecurity (MICS) is an online degree preparing cybersecurity leaders for complex cybersecurity challenges.

Our Ph.D. in Information Science is a research program for next-generation scholars of the information age.

• Fall 2024 Course Schedule
• Summer 2024 Course Schedule

The School of Information's courses bridge the disciplines of information and computer science, design, social sciences, management, law, and policy. We welcome interest in our graduate-level Information classes from current UC Berkeley graduate and undergraduate students and community members.  More information about signing up for classes.

• Ladder & Adjunct Faculty
• MIMS Students
• MIDS Students
• 5th Year MIDS Students
• MICS Students
• Ph.D. Students

• Publications
• Centers & Labs
• Computer-mediated Communication
• Data Science
• Entrepreneurship
• Human-computer Interaction (HCI)
• Information Economics
• Information Organization
• Information Policy
• Information Retrieval & Search
• Information Visualization
• Social & Cultural Studies
• Technology for Developing Regions
• User Experience Research

Research by faculty members and doctoral students keeps the I School on the vanguard of contemporary information needs and solutions.

The I School is also home to several active centers and labs, including the Center for Long-Term Cybersecurity (CLTC) , the Center for Technology, Society & Policy , and the BioSENSE Lab .

• Why Hire I School?
• Request a Resume Book
• For Nonprofit and Government Employers
• Leadership Development Program
• Mailing List
• Jobscan & Applicant Tracking Systems
• Resume & LinkedIn Review

I School graduate students and alumni have expertise in data science, user experience design & research, product management, engineering, information policy, cybersecurity, and more — learn more about hiring I School students and alumni .

• Press Coverage
• I School Voices

• Distinguished Lecture Series
• I School Lectures
• Information Access Seminars
• CLTC Events
• Women in MIDS Events

Data Science Course Schedule Summer 2024

Data Science courses are restricted to students enrolled in the MIDS degree program only. 

All times are listed in the Pacific Time Zone (America/Los_Angeles).

This fast-paced course gives students fundamental Python knowledge necessary for advanced work in data science. Students gain frequent practice writing code, building to advanced skills focused on data science applications. We introduce a range of Python objects and control structures, then build on these with classes on object-oriented programming. A major programming project reinforces these concepts, giving students insight into how a large piece of software is built and experience managing a full-cycle development project. The last section covers two popular Python packages for data analysis, NumPy and pandas, and includes an exploratory data analysis.

Introduces the data sciences landscape, with a particular focus on learning data science techniques to uncover and answer the questions students will encounter in industry. Lectures, readings, discussions, and assignments will teach how to apply disciplined, creative methods to ask better questions, gather data, interpret results, and convey findings to various audiences. The emphasis throughout is on making practical contributions to real decisions that organizations will and should make.

An introduction to many different types of quantitative research methods and statistical techniques for analyzing data. We begin with a focus on measurement, inferential statistics and causal inference using the open-source statistics language, R. Topics in quantitative techniques include: descriptive and inferential statistics, sampling, experimental design, tests of difference, ordinary least squares regression, general linear models.

Storing, managing, and processing datasets are foundational processes in data science. This course introduces the fundamental knowledge and skills of data engineering that are required to be effective as a data scientist. This course focuses on the basics of data pipelines, data pipeline flows and associated business use cases, and how organizations derive value from data and data engineering. As these fundamentals of data engineering are introduced, learners will interact with data and data processes at various stages in the pipeline, understand key data engineering tools and platforms, and use and connect critical technologies through which one can construct storage and processing architectures that underpin data science applications.

Machine learning is a rapidly growing field at the intersection of computer science and statistics concerned with finding patterns in data. It is responsible for tremendous advances in technology, from personalized product recommendations to speech recognition in cell phones. This course provides a broad introduction to the key ideas in machine learning. The emphasis will be on intuition and practical examples rather than theoretical results, though some experience with probability, statistics, and linear algebra will be important.

Visualization enhances exploratory analysis as well as efficient communication of data results. This course focuses on the design of visual representations of data in order to discover patterns, answer questions, convey findings, drive decisions, and provide persuasive evidence. The goal is to give you the practical knowledge you need to create effective tools for both exploring and explaining your data. Exercises throughout the course provide a hands-on experience using relevant programming libraries and software tools to apply research and design concepts learned.

The capstone course will cement skills learned throughout the MIDS program — both core data science skills and “soft skills” like problem-solving, communication, influencing, and management — preparing students for success in the field. The centerpiece is a semester-long group project in which teams of students propose and select project ideas, conduct and communicate their work, receive and provide feedback (in informal group discussions and formal class presentations), and deliver compelling presentations along with a web-based final deliverable. Includes relevant readings, case discussions, and real-world examples and perspectives from panel discussions with leading data science experts and industry practitioners.

This is a multidisciplinary graduate course that synthesizes data management, data economy, and machine learning & AI strategy and research, product innovation, business and enterprise technology strategy, industry analysis, organizational decision-making and data-driven leadership into one course offering. The course provides strategic thinking tools, analytical frameworks, and real-world case examples to help students explore and investigate modern data applications and opportunities in multiple domains and industries. Students are required to participate in weekly sessions and write response pieces as well as a final paper and presentation evaluating one defining application or emerging technology in machine learning/AI end-to-end.

Intro to the legal, policy, and ethical implications of data, including privacy, surveillance, security, classification, discrimination, decisional-autonomy, and duties to warn or act. Examines legal, policy, and ethical issues throughout the full data-science life cycle collection, storage, processing, analysis, and use with case studies from criminal justice, national security, health, marketing, politics, education, employment, athletics, and development. Includes legal and policy constraints and considerations for specific domains and data-types, collection methods, and institutions; technical, legal, and market approaches to mitigating and managing concerns; and the strengths and benefits of competing and complementary approaches.

This course introduces students to experimentation in the social sciences. This topic has increased considerably in importance since 1995, as researchers have learned to think creatively about how to generate data in more scientific ways, and developments in information technology have facilitated the development of better data gathering. Key to this area of inquiry is the insight that correlation does not necessarily imply causality. In this course, we learn how to use experiments to establish causal effects and how to be appropriately skeptical of findings from observational data.

This course provides learners hands-on data management and systems engineering experience using containers, cloud, and Kubernetes ecosystems based on current industry practice. The course will be project-based with an emphasis on how production systems are used at leading technology-focused companies and organizations. During the course, learners will build a body of knowledge around data management, architectural design, developing batch and streaming data pipelines, scheduling, and security around data including access management and auditability. We’ll also cover how these tools are changing the technology landscape.

This course teaches the underlying principles required to develop scalable machine learning pipelines for structured and unstructured data at the petabyte scale. Students will gain hands-on experience in Apache Hadoop and Apache Spark.

Understanding language is fundamental to human interaction. Our brains have evolved language-specific circuitry that helps us learn it very quickly; however, this also means that we have great difficulty explaining how exactly meaning arises from sounds and symbols. This course is a broad introduction to linguistic phenomena and our attempts to analyze them with machine learning. We will cover a wide range of concepts with a focus on practical applications such as information extraction, machine translation, sentiment analysis, and summarization.

A continuation of Data Science 203 (Statistics for Data Science), this course trains data science students to apply more advanced methods from regression analysis and time series models. Central topics include linear regression, causal inference, identification strategies, and a wide-range of time series models that are frequently used by industry professionals. Throughout the course, we emphasize choosing, applying, and implementing statistical techniques to capture key patterns and generate insight from data. Students who successfully complete this course will be able to distinguish between appropriate and inappropriate techniques given the problem under consideration, the data available, and the given timeframe.

This course introduces the theoretical and practical aspects of computer vision, covering both classical and state of the art deep-learning based approaches. This course covers everything from the basics of the image formation process in digital cameras and biological systems, through a mathematical and practical treatment of basic image processing, space/frequency representations, classical computer vision techniques for making 3-D measurements from images, and modern deep-learning based techniques for image classification and recognition.

Recent developments in neural network architectures, algorithms, and computing hardware have led to a revolutionary development usually referred to as generative AI nowadays. Large language models (LLMs) are now able to generate seemingly human-like text in response to tasks like summarization, question answering, etc. Leveraging similar strategies, comparable advances have been made with images as well as audio. With today’s (and anticipated future) capabilities, Generative AI is poised to be a tool used comprehensively in a wide variety of ways, and therefore to have a profound set of effects on our lives and society as a whole.

This course is a broad introduction to these new technologies. It is split conceptually into three parts. In the introduction section we will cover the historical aspects, key ideas and learnings all the way to Transformer architectures and training aspects. In the practical aspects and techniques section, we will learn how to deploy, use, and train LLMs. We will discuss core concepts like prompt tuning, quantization, and parameter efficient fine-tuning, and we will also explore use case patterns. Finally, we will discuss challenges & opportunities offered by Generative AI, where we will highlight critical issues like bias and inclusivity, fake information, and safety, as well as some IP issues.

Our focus will be on practical aspects of LLMs to enable students to be both effective and responsible users of generative AI technologies.

This course provides academic scaffolding for graduate students in data science who are engaged in internships, practicums, or relevant types of independent research while progressing toward a master’s degree. We focus on developing skills in project management, organizational navigation, and professional communication related to data science. In addition, the practicum explores various applications of data science methods in industrial, academic, governmental, and nonprofit settings. We discuss common challenges facing data scientists at work and possible approaches to addressing these challenges.

Celebrating UC Berkeley’s record-breaking fundraising year

"Every gift, regardless of size, supports Berkeley's mission and helps us forge an ever-stronger culture of philanthropy," says Chancellor Rich Lyons, UC Berkeley's new leader.

By Public Affairs

Keegan Houser/UC Berkeley

August 15, 2024

Chancellor Rich Lyons sent the following message to the campus community on Aug. 15:

I am thrilled to share with you, our faculty and staff, the remarkable achievements we reached in fundraising for the 2023–24 fiscal year thanks to the generous support of Berkeley’s community of alumni, parents, and friends. Together, we raised an impressive $1.31 billion in philanthropic support, marking the highest annual fundraising total in Berkeley’s history.

Contributions from donors make a significant impact on our students, faculty, and programs. Thanks to this year’s success, students will benefit from scholarship and fellowship support, faculty positions will be strengthened and sustained, and Berkeley’s extraordinary research enterprise will be enhanced with new resources for work in many areas. Our donors recognize the positive impact their philanthropy has on our campus and beyond. Their shared values with Berkeley’s mission drives them to partner with us on realizing a united vision of a better world.

Notable achievements enabled by the generosity of the Berkeley community include the opening of Helen Diller Anchor House for transfer students, the forthcoming Bakar ClimatEnginuity Hub , and significant programmatic investments that will enhance the student experience and further our research excellence. Last year, the Graduate School of Journalism received a transformative $10 million pledge , enabling the school to substantially increase its support for students, including doubling the financial aid offered to incoming students in the fall of 2024. While undergraduates benefitted from scholarships and programmatic support, including an $11 million bequest providing flexible student support and new resources for the Department of Music and the Center for African Studies. Investments in health and environmental research, such as the Innovative Genomics Institute’s Audacious Project and the Molecular Therapeutics Initiative , reveal the impact of our donors on cutting-edge research.  

We are also grateful for the contributions of other donors, including the Charter Hill Society (CHS) members and Big Give donors. CHS members provided more than $750 million in gifts last year to a range of departments and programs, while Big Give has raised more than $115 million in gifts over its 10 years, and brought 25,000 new donors to campus. These efforts underscore the importance of every gift, regardless of size, in supporting Berkeley’s mission and help us forge an ever-stronger culture of philanthropy.

While a small portion of gifts are directed to be used at the discretion of campus leaders, the vast majority of donations are designated for specific purposes per the donors’ intentions, such as endowed chairs or capital projects. This means that, despite this year’s record-breaking fundraising total, we still face budget challenges insofar as philanthropy—as critical as it is—cannot address our rising costs nor fully make up for reductions in other sources of support. All this being said, the generous partnership of our donors remains one of Berkeley’s most important resources.

Every donation to Berkeley tells a story of our strong and vibrant community, one that continues to respond to the challenges of a changing world. Thank you for your dedication and hard work in making these achievements possible. Let us continue to build on this momentum, ensuring Berkeley’s continued excellence for generations to come.

• UC Berkeley
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The School of Information is UC Berkeley’s newest professional school. Located in the center of campus, the I School is a graduate research and education community committed to expanding access to information and to improving its usability, reliability, and credibility while preserving security and privacy.

• Career Outcomes
• Degree Requirements
• Paths Through the MIMS Degree
• Final Project
• Funding Your Education
• Admissions Events
• Request Information
• Capstone Project
• Jack Larson Data for Good Fellowship
• Tuition & Fees
• Women in MIDS
• MIDS Curriculum News
• MICS Student News
• Dissertations
• Applied Data Science Certificate
• ICTD Certificate
• Cybersecurity Clinic

The School of Information offers four degrees:

The Master of Information Management and Systems (MIMS) program educates information professionals to provide leadership for an information-driven world.

The Master of Information and Data Science (MIDS) is an online degree preparing data science professionals to solve real-world problems. The 5th Year MIDS program is a streamlined path to a MIDS degree for Cal undergraduates.

The Master of Information and Cybersecurity (MICS) is an online degree preparing cybersecurity leaders for complex cybersecurity challenges.

Our Ph.D. in Information Science is a research program for next-generation scholars of the information age.

• Fall 2024 Course Schedule
• Summer 2024 Course Schedule

The School of Information's courses bridge the disciplines of information and computer science, design, social sciences, management, law, and policy. We welcome interest in our graduate-level Information classes from current UC Berkeley graduate and undergraduate students and community members.  More information about signing up for classes.

• Ladder & Adjunct Faculty
• MIMS Students
• MIDS Students
• 5th Year MIDS Students
• MICS Students
• Ph.D. Students

• Publications
• Centers & Labs
• Computer-mediated Communication
• Data Science
• Entrepreneurship
• Human-computer Interaction (HCI)
• Information Economics
• Information Organization
• Information Policy
• Information Retrieval & Search
• Information Visualization
• Social & Cultural Studies
• Technology for Developing Regions
• User Experience Research

Research by faculty members and doctoral students keeps the I School on the vanguard of contemporary information needs and solutions.

The I School is also home to several active centers and labs, including the Center for Long-Term Cybersecurity (CLTC) , the Center for Technology, Society & Policy , and the BioSENSE Lab .

• Why Hire I School?
• Request a Resume Book
• For Nonprofit and Government Employers
• Leadership Development Program
• Mailing List
• Jobscan & Applicant Tracking Systems
• Resume & LinkedIn Review

I School graduate students and alumni have expertise in data science, user experience design & research, product management, engineering, information policy, cybersecurity, and more — learn more about hiring I School students and alumni .

• Press Coverage
• I School Voices

• Distinguished Lecture Series
• I School Lectures
• Information Access Seminars
• CLTC Events
• Women in MIDS Events

Master of Information and Cybersecurity

Master’s in cybersecurity online from uc berkeley.

The Master of Information and Cybersecurity (MICS) online program prepares students with the cybersecurity skills needed to assume leadership positions and drive innovation in the field.

Through a holistic approach to cybersecurity, students develop a thorough understanding of information security technologies as well as the economic, legal, behavioral, and ethical impacts of cybersecurity. Students graduate as competitive candidates in the job market with connections to UC Berkeley alumni and professionals in the San Francisco Bay Area.

Request more info

A Leadership-Focused Curriculum 

The interdisciplinary MICS curriculum provides students with hands-on practical experience with secure systems and applications, and the ability to lead, manage, and contribute to building cybersecurity solutions.

Students master core technical skills and become fluent in the business, political, and legal context of cybersecurity. They can expect to graduate with a comprehensive understanding of the following:

• Secure coding
• Government and national security
• Network and web security
• Usable privacy and security
• Cryptography
• Operating system security
• Security in context: legal, behavioral, and ethical issues
• Privacy engineering

The online MICS program is 9 courses (27 units) and can be completed in 20 months. Students complete foundation courses, electives, and a capstone project. They also attend one in-person immersion in the San Francisco Bay Area. The curriculum includes the following courses:

• Beyond the Code: Cybersecurity in Context
• Network Security
• Software Security

Cryptography for Cyber and Network Security

Operating system security.

• Managing Cyber Risk
• Government, National Security, and the Fifth Domain
• Usable Privacy and Security

Privacy Engineering

Learn more about about upcoming webinars, deadlines, and more, why a master’s in cybersecurity from uc berkeley.

Cybersecurity permeates almost all aspects of the human experience. Individuals, organizations, and societies are protected from cyber threats constantly without even realizing they occur. This is thanks to professionals who understand the complex relationship between human behaviors and advanced technology in securing information.

UC Berkeley is where the brightest minds from across the globe come together to explore, ask questions, and improve the world. Our MICS program does more than create experts in information security: it shapes leaders who understand the implications of cybersecurity across all industries and want to drive the future of innovation in cybersecurity.

Developed by faculty from the School of Information and the College of Engineering, the interdisciplinary MICS curriculum focuses on the intersection of technical, social, political, economic, legal, national security, and ethical components of cybersecurity. Students graduate with versatile knowledge needed to lead cybersecurity in private, public, or philanthropic sectors and with connections to the nearby technology hub Silicon Valley.

What Can You Do with a Master’s in Cybersecurity?

Cybersecurity’s growth is turning it into a massive market with an increased demand for skilled cybersecurity labor. Due to constant evolution and escalation of threats, cybersecurity has become one of the most important and exciting career fields of the 21st century.

The United States Bureau of Labor Statistics (BLS) projects employment of information security analysts to grow 35% from 2021 to 2031, equating to an average of 19,500 new positions each year.¹

The cybersecurity industry is met with the challenge of not only filling the growing demand and talent shortage but with finding highly skilled professionals who can be trusted to effectively protect information systems. This creates an opportunity for graduates of the MICS program to enter the in-demand market as competitive cybersecurity experts.

Career Opportunities in Cybersecurity

No organization that operates online can ignore cybersecurity. With cyber threats growing and constantly changing, businesses, governments, and nonprofit organizations are making significant investments in cyber defense and hiring increasingly large numbers of cybersecurity professionals.

MICS graduates are poised to enter this market in leadership roles with the requisite multifunctional skill sets and a strong network provided by the program.

Cybersecurity Salaries

Salaries for cybersecurity professionals range from $85,000 to $151,547 per year.¹

Featured Courses

Students learn about the mathematical and practical foundations of cryptography and the many different cryptosystems.

Students compare approaches to security taken among several modern operating systems and learn techniques for establishing trust across a set of interoperating systems.

Students survey privacy mechanisms applicable to systems engineering, with a particular focus on the inference threat arising due to advancements in artificial intelligence and machine learning.

Admissions Requirements for the Master’s in Cybersecurity Online Degree

When evaluating applicants, we consider both qualitative and quantitative factors to determine if they can meet the demands of the rigorous MICS program. These include:

• Academic coursework and performance
• Professional experience
• Communication skills
• Statement of purpose
• Letters of recommendation

¹ Cybersecurity Career Pathway, CyberSeek , 2023

Deal reached in feud between California news outlets and Google: $250 million to support journalism but no new law

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California lawmakers intend to shelve legislation that would have required Google to pay news outlets for distributing their content, and in its place announced on Wednesday a new public-private partnership between the state and the tech giant that will fund programs to research artificial intelligence and bolster local journalism.

The plan lays out a commitment of nearly $250 million over the next five years, with just over one-fourth of the money coming from state taxpayers and the remainder coming from Google and possibly other private donors. The money will go toward two new initiatives administered by UC Berkeley’s Graduate School of Journalism: a fund to distribute millions of dollars to California news outlets, and an “AI accelerator” to develop ways for journalists to use the powerful technology.

“This agreement represents a major breakthrough in ensuring the survival of newsrooms and bolstering local journalism across California — leveraging substantial tech industry resources without imposing new taxes on Californians,” Gov. Gavin Newsom said in a statement. “The deal not only provides funding to support hundreds of new journalists, but helps rebuild a robust and dynamic California press corps for years to come, reinforcing the vital role of journalism in our democracy.”

The agreement marks the culmination of a two-year battle between the news industry and the tech sector over how to resuscitate local journalism amid massive upheavals in how people consume news and how advertisers reach consumers.

Assemblymember Buffy Wicks (D-Oakland) led the effort, arguing that supporting California journalism is essential to maintaining democracy because local news outlets play a vital role informing the public about their government. Dwindling advertising revenue has caused many media companies to lay off journalists, or shutter entirely, leaving some communities without the independent watchdogs that local news outlets provide.

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“This partnership represents a cross-sector commitment to supporting a free and vibrant press, empowering local news outlets up and down the state to continue in their essential work,” Wicks said in a statement. “This is just the beginning. I remain committed to finding even more ways to support journalism in our state for years to come.”

The debate over how to solve the problem fractured the news industry, with traditional publishers getting behind a bill that digital news outlets initially did not support, and the union that represents reporters turning against the negotiated compromise. Politicians found themselves squeezed between the divided journalism community and the powerful tech sector, a major contributor to California’s economy.

And Democratic lawmakers themselves were divided, with state Senate leader Mike McGuire (D-Healdsburg) saying the agreement “doesn’t fully address the inequities facing the [news] industry.”

As part of the agreement, the state will put $30 million from next year’s budget into the fund, and contribute $10 million in each of the next four years. Google will put $15 million into the fund next year, and pay another $15 million next year to support other journalism initiatives: $5 million for the AI accelerator and $10 million in direct donations to digital news outlets. In each of the next four years, Google commitments include putting $10 million into the new fund and continuing $10 million in direct donations to news outlets. Money in the fund will be distributed to California newsrooms based on how many full-time journalists they employ. A new nonprofit organization will be formed to administer the programs at UC Berkeley, with a board made up of representatives from across California’s industry.

“This public-private partnership builds on our long history of working with journalism and the local news ecosystem in our home state, while developing a national center of excellence on AI policy,” said a statement from Kent Walker, chief legal officer for Alphabet, the parent company of Google.

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The agreement has support from several professional associations, including the California News Publishers Assn. that the Los Angeles Times belongs to and the Local Independent Online News Publishers group that initially opposed Wicks’ bill. The labor union that represents journalists supported her bill but opposes the deal.

Matt Pearce, president of the Media Guild of the West, criticized the plan in emails with union members in recent days, calling it a “total rout of the state’s attempts to check Google’s stranglehold over our newsrooms.”

The union had lobbied for the deal to include a provision requiring media companies that receive the funds to have a non-expired collective bargaining agreement, and for Google to contribute more than the $74 million it pays annually to newsrooms in Canada. The agreement does not include a labor provision and Google comes out paying less to California newsrooms than it does in Canada. Pearce criticized it as a “secret deal” that was worked out behind closed doors.

As part of the agreement, Wicks agreed to set aside legislation she’s championed for the last two years.

Assembly Bill 886 also, known as the California Journalism Preservation Act, sought to blunt the financial hardships that have hit the news business as Google and Meta grew to dominate digital advertising, and technology radically changed the way people consume news. Sponsored by the California News Publishers Assn., of which the Los Angeles Times is a member, the bill set off a fierce battle between news organizations and Big Tech.

Publishers argue that online search and social media platforms are unfairly gobbling up advertising revenue while publishing content they don’t pay for. The bill would have required Google to pay into a fund that would in turn distribute millions of dollars to California news outlets based on how many journalists they employ. The amount of the payment was never spelled out in legislation, though lawmakers were hopeful that Google and the news industry could negotiate an agreement.

But Google argued in testimony before the Legislature earlier this summer that the bill would “break the fundamental and foundational principles of the open internet, forcing platforms to pay publishers for sending valuable free traffic to them.”

Google threatened to remove California news content from its platform if the bill passed, and then ran ads saying the legislation would reduce Californians’ access to news.

Lobbying over the bill grew intense, with a trade association Google belongs to launching an ad campaign aimed at lawmakers that cast the legislation as a giveaway to large media corporations. Records show the Computer and Communications Industry Assn. spent $5 million on ads against AB 886 over the last two years as the bill made its way through the Legislature.

The most recent version of the bill was modeled after similar legislation in Canada , where Google is paying $74 million annually into a fund. California’s bill would have been the first such law in the United States.

World & Nation

California is trying to force Big Tech to pay for news. What can we learn from Australia and Canada?

Three years ago, when Australian politicians took on the titans of Big Tech — crafting legislation to force Google and Facebook to pay for news articles they share on their sites — Google threatened to pull out of the country.

Lawmakers this year also considered a different bill that sought to aid the news industry by providing a tax credit for employing full-time journalists. Senate Bill 1327 would impose a new tax on Amazon, Meta and Google for the data they take from users and pump the money from this “data extraction mitigation fee” into tax credits for news outlets.

As a tax measure, it required approval from two-thirds of the Legislature, presenting a political challenge in an election year.

Its author, Sen. Steve Glazer (D-Orinda), supported Wicks’ legislation as it moved through the Capitol but said he is against the new plan that’s taken its place.

“Despite the good intentions of the parties involved, this proposal does not provide sufficient resources,” Glazer said, criticizing the lack of involvement of other companies, such as Meta.

“This agreement, unfortunately, seriously undercuts our work towards a long-term solution to rescue independent journalism.”

More to Read

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July 31, 2024

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Laurel Rosenhall is Sacramento bureau chief, overseeing the Los Angeles Times’ coverage of the California Capitol, state government and state politics. She joined the company in 2021 and spent a year as a member of the editorial board writing about California politics, policy and power. Before joining The Times, Rosenhall covered state politics for CalMatters and the Sacramento Bee. A lifelong Californian, she grew up in San Francisco and graduated from the UC Berkeley Graduate School of Journalism.

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