The Master of Information and Data Science (MIDS) is an online, part-time professional degree program that prepares students to work effectively with heterogeneous, real-world data and to extract insights from the data using the latest tools and analytical methods. The program emphasizes the importance of asking good research or business questions as well as the ethical and legal requirements of data privacy and security.
Students attend weekly live ("synchronous") sessions with classmates and instructors via an online platform as well as engaging with online ("asynchronous") videos and assignments on their own time.
The curriculum includes research design and applications for data and analysis, statistics for data science, data engineering, applied machine learning, data visualization, natural language processing, and data ethics. MIDS features a project-based approach to learning and encourages the pragmatic application of a variety of different tools and methods to solve complex problems.
Graduates of the program will be able to:
Imagine new and valuable uses for large datasets;
Retrieve, organize, combine, clean, and store data from multiple sources;
Apply appropriate data mining, statistical analysis, and machine learning techniques to detect patterns and make predictions;
Design visualizations and effectively communicate findings; and
Understand the ethical and legal requirements of data privacy and security.
Thank you for considering UC Berkeley for graduate study! UC Berkeley offers more than 120 graduate programs representing the breadth and depth of interdisciplinary scholarship. The Graduate Division hosts a complete list of graduate academic programs, departments, degrees offered, and application deadlines can be found on the Graduate Division website.
Prospective students must submit an online application to be considered for admission, in addition to any supplemental materials specific to the program for which they are applying. The online application and steps to take to apply can be found on the Graduate Division website.
Admission Requirements
The minimum graduate admission requirements are:
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 to do graduate work in your chosen field.
For a list of requirements to complete your graduate application, please see the Graduate Division’s Admissions Requirements page. It is also important to check with the program or department of interest, as they may have additional requirements specific to their program of study and degree. Department contact information can be found here.
Applications are evaluated holistically on a combination of prior academic performance, work experience, essays, letters of recommendation, and goals that are a good fit for the program.
The UC Berkeley School of Information seeks students with the academic abilities to meet the demands of a rigorous graduate program.
To be eligible to apply to the Master of Information and Data Science program, applicants must meet the following requirements:
A bachelor’s degree or its recognized equivalent from an accredited institution.
Superior scholastic record, normally well above a 3.0 GPA.
A high level of quantitative ability as conveyed by significant work experience that demonstrates your quantitative abilities and/or academic coursework that demonstrates quantitative aptitude
A high level of analytical reasoning ability and a problem-solving mindset as demonstrated in academic and/or professional performance.
A working knowledge of fundamental concepts including: data structures, algorithms and analysis of algorithms, and linear algebra.
Proficiency in programming languages, such as Python or Java, demonstrated by prior work experience or advanced coursework. Applicants who lack this experience in their academic or work background but meet all other admission requirements will be required to take the Introduction to Data Science Programming course in their first term.
The ability to communicate effectively, as demonstrated by academic performance, professional experience, and/or strong essays that demonstrate effective communication skills.
The Master of Information and Data Science is designed to be completed in 20 months, but other options are available to complete the program. You will complete 27 units of course work over an average of five terms, taking a maximum of 9 units each term. Courses are divided into foundation courses (15 units), advanced courses (9 units), and a synthetic capstone (3 units). Students also complete an Immersion Program.
The Immersion Program provides MIDS students with the opportunity to meet faculty and peers in person. Students have the opportunity to gain on-the-ground perspectives from faculty and industry leaders, meet with data science professionals, and soak up more of the School of Information (I School) culture. Offered three times a year, each two- to three-day immersion will be custom-crafted to deliver additional learning, networking, and community-building opportunities.
Please note: DATASCI courses are only available for Information and Data Science (MIDS) students.
Information and Data Science
Terms offered: Summer 2024, Spring 2024, Fall 2023
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. Introduction to Data Science Programming: Read More [+]
Objectives & Outcomes
Student Learning Outcomes: Be able to design, reason about, and implement algorithms for solving computational problems.
Be able to generate an exploratory analysis of a data set using Python.
Be able to navigate a file system, manipulate files, and execute programs using a command line interface.
Be able to test and effectively debug programs.
Be fluent in Python syntax and familiar with foundational Python object types.
Be prepared for further programming challenges in more advanced data science courses.
Know how to read, manipulate, describe, and visualize data using the Numpy and Pandas packages.
Know how to use Python to extract data from different type of files and other sources.
Understand how to manage different versions of a project using Git and how to collaborate with others using Github.
Understand the principles of functional programming.
Understand the principles of object-oriented design and the process by which large pieces of software are developed.
Rules & Requirements
Prerequisites: MIDS students only
Hours & Format
Fall and/or spring: 14 weeks - 3 hours of lecture per week
Terms offered: Summer 2024, Spring 2024, Fall 2023
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. Course must be taken for a letter grade to fulfill degree requirements. Research Design and Applications for Data and Analysis: Read More [+]
Rules & Requirements
Prerequisites: MIDS students only
Hours & Format
Fall and/or spring: 14 weeks - 3 hours of lecture per week
Terms offered: Summer 2024, Spring 2024, Fall 2023
This course provides students with a foundational understanding of classical statistics within the broader context of data science. Topics include exploratory analysis and descriptive statistics, probability theory and the foundations of statistical modeling, estimators, hypothesis testing, and classical linear regression. Causal inference and reproducibility issues are treated briefly. Students will learn to apply the most common statistical procedures correctly, checking assumptions and responding appropriately when they appear violated; to evaluate the design of a study and how the variables being measured relate to research questions; and to analyze real-world data using the open-source language R. Statistics for Data Science: Read More [+]
Rules & Requirements
Prerequisites: MIDS students only. Intermediate competency in calculus is required. A college-level linear algebra course is recommended
Hours & Format
Fall and/or spring: 14 weeks - 3 hours of lecture per week
Terms offered: Summer 2024, Spring 2024, Fall 2023
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. Fundamentals of Data Engineering: Read More [+]
Rules & Requirements
Prerequisites: MIDS students only. Intermediate competency in Python, C, or Java, and competency in Linux, GitHub, and relevant Python libraries. Knowledge of database management including SQL is recommended but not required
Credit Restrictions: Students will receive no credit for DATASCI W205 after completing DATASCI 205. A deficient grade in DATASCI W205 may be removed by taking DATASCI 205.
Hours & Format
Fall and/or spring: 14 weeks - 3 hours of lecture per week
Terms offered: Summer 2024, Spring 2024, Fall 2023
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. Course must be taken for a letter grade to fulfill degree requirements. Applied Machine Learning: Read More [+]
Rules & Requirements
Prerequisites: MIDS students only. DATASCI 201 or DATASCI 201A and DATASCI 203. Intermediate competency in Python, C, or Java, and competency in Linux, GitHub, and relevant Python libraries. Linear algebra is recommended
Hours & Format
Fall and/or spring: 14 weeks - 3 hours of lecture per week
Terms offered: Summer 2024, Spring 2024, Fall 2023
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. Data Visualization: Read More [+]
Objectives & Outcomes
Student Learning Outcomes: Analyze data using exploratory visualization.
Build commonly requested types of visualizations as well as more advanced visualizations using ground-up customization.
Constructively critique existing visualizations, identifying issues of integrity as well as excellence.
Create useful, performant visualizations from real-world data sources, including large and complex datasets.
Design aesthetically pleasing static and interactive visualizations with perceptually appropriate forms and encodings.
Improve your own work through usability testing and iteration, with attention to context.
Select appropriate tools for building visualizations, and gain skills to evaluate new tools.
Rules & Requirements
Prerequisites: MIDS students only. DATASCI 200. Intermediate competency in Python, C, or Java, and competency in Linux, GitHub, and relevant Python libraries. Recommended: experience with HTML, CSS, and JavaScript, or ability to learn new programming languages quickly. If Python is the only programming language you know, you will probably benefit from learning the basics of web development with JavaScript in advance
Hours & Format
Fall and/or spring: 14 weeks - 3 hours of lecture per week
Terms offered: Summer 2024, Spring 2024, Fall 2023
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 as well as 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. Capstone: Read More [+]
Credit Restrictions: Students will receive no credit for DATASCI W210 after completing DATASCI 210. A deficient grade in DATASCI W210 may be removed by taking DATASCI 210.
Hours & Format
Fall and/or spring: 14 weeks - 3 hours of lecture per week
Terms offered: Fall 2023
In the Capstone class, students combine technical, analytical, interpretive, problem-solving, and strategic thinking dimensions to design and execute a full end-to-end data science project. Students will develop their technical and non-technical skills as data scientists who focus on real-world and impactful applications and situations. The final project provides a learning opportunity and “sandbox” to integrate all skills and concepts learned throughout the MIDS program and provides experience and hands-on tools in formulating and implementing an impactful and compelling project. Students are evaluated on their ability to work in a dynamic team environment to collaborate, co-develop, and communicate their work. Capstone for Early Career Data Scientists: Read More [+]
Objectives & Outcomes
Student Learning Outcomes: Construct and perform persuasive, informative and understandable written, spoken, or visualized narratives that concisely convey findings, solutions, and applications of data-driven approaches that have been incorporated in project work.
Demonstrate an ability to integrate and synthesize knowledge and skills gained through other courses in the program (critical technical, analytical, strategic thinking, problem-solving, communication, influencing, and management skills) in developing and implementing a capstone project that addresses a key data science problem.
Demonstrate proficiency in applying technical and analytical skills towards the collection, storage, and analysis of data towards problem-solving and project execution. Assess and select data and the data collection methods that best fit the specific outcome or need of a project or problem space.
Demonstrate proficiency in identifying target user audience for the Capstone project and conduct expert and target user interviews to validate problem framing, scoping, and hypothesis.
Demonstrate proficiency in selecting the appropriate data science and machine learning approaches for a specific project and perform model evaluation to demonstrate the efficacy of the model and its value to the target users.
Effectively engage in a process of teamwork, feedback from peers, instructors and experts, and informed iteration that mirrors the challenges and opportunities of applying data science in a realistic organizational setting. Conduct self-assessment of professional development and leadership.
Identify and articulate a problem space to address through application of data driven methods, approaches and practices that include an understanding of stakeholders, social contexts, potential impact, and potential obstacles.
Identify and describe effective teamwork skills, practices, and characteristics of an effective workplace or project team, including distribution of team tasks and duties. Understand and apply successful communication strategies for teams, for various stakeholders within an organization with different contextual requirements and expectations.
Understand, incorporate, and practice integrated understanding of what it takes to imagine, design, and execute a data science project from start to finish.
Terms offered: Summer 2024, Spring 2024
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. Modern Data Applications: Read More [+]
Objectives & Outcomes
Student Learning Outcomes: Anticipate the opportunities and problems likely to be encountered in building and working with any given data application as business and technology requirements as well as secular trends evolve.
Create a strategic business case for a new or emerging data application or data science / machine learning use case.
Develop strategic and business thinking in various data science domains.
Evaluate data science applications and opportunities across a number of situations and domains.
Learn a set of qualitative models and analytical frameworks to evaluate any modern data application and emerging trends in machine learning and AI.
Understand “modern data stacks” and how to manage and use data as an asset in an organization for responsible decision making.
Terms offered: Summer 2024, Spring 2024, Fall 2023
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. Behind the Data: Humans and Values: Read More [+]
Rules & Requirements
Prerequisites: MIDS and MPA students only
Credit Restrictions: Students will receive no credit for DATASCI W231 after completing DATASCI 231. A deficient grade in DATASCI W231 may be removed by taking DATASCI 231.
Hours & Format
Fall and/or spring: 14 weeks - 3 hours of lecture per week
Terms offered: Spring 2024, Fall 2023, Spring 2023
This course surveys privacy mechanisms applicable to systems engineering, with a particular focus on the inference threat arising due to advancements in artificial intelligence and machine learning. We will briefly discuss the history of privacy and compare two major examples of general legal frameworks for privacy from the United States and the European Union. We then survey three design frameworks of privacy that may be used to guide the design of privacy-aware information systems. Finally, we survey threat-specific technical privacy frameworks and discuss their applicability in different settings, including statistical privacy with randomized responses, anonymization techniques, semantic privacy models, and technical privacy mechanisms. Privacy Engineering: Read More [+]
Rules & Requirements
Prerequisites: MIDS students only
Credit Restrictions: Students will receive no credit for DATASCI W233 after completing DATASCI 233. A deficient grade in DATASCI W233 may be removed by taking DATASCI 233.
Hours & Format
Fall and/or spring: 14 weeks - 3 hours of lecture per week
Terms offered: Summer 2024, Spring 2024, Fall 2023
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. Experiments and Causal Inference: Read More [+]
Credit Restrictions: Students will receive no credit for DATASCI W241 after completing DATASCI 241. A deficient grade in DATASCI W241 may be removed by taking DATASCI 241.
Hours & Format
Fall and/or spring: 14 weeks - 3 hours of lecture per week
Terms offered: Summer 2024, Spring 2024, Fall 2023
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.
Student Learning Outcomes: Construct, measure, and identify metrics relating to performance of a system in order to optimize costs and latency of serving inferences for machine learning models.
Demonstrate understanding of Kubernetes for management of machine learning models.
Describe the difference between a monolithic and microservice architecture, assess and select appropriate use cases for each.
Describe the differences between a development and production system particularly for Machine Learning where the boundaries are blurry.
Know when to leverage a cache for serving machine learning models to reduce load on production systems.
Understand continuous integration and continuous delivery (CI/CD) pipeline for automated code deployment, particularly for ML models.
Understand how stateful systems add complexities to systems engineering.
Understand how to serve machine learning models over an API in real-time.
Rules & Requirements
Prerequisites: MIDS students only. DATASCI 205 and DATASCI 207. We assume you are familiar with generating predictions from a trained machine learning model. Familiarity with command line (Bash), Python, and Git. We assume you have a working knowledge of SSH, Ports, and familiarity with networking concepts such as DNS
Hours & Format
Fall and/or spring: 14 weeks - 3 hours of lecture per week
Terms offered: Summer 2024, Spring 2024, Fall 2023
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. Machine Learning at Scale: Read More [+]
Rules & Requirements
Prerequisites: MIDS students only. DATASCI 205 and DATASCI 207. Intermediate programming skills in an object-oriented language (e.g., Python)
Credit Restrictions: Students will receive no credit for DATASCI W261 after completing DATASCI 261. A deficient grade in DATASCI W261 may be removed by taking DATASCI 261.
Hours & Format
Fall and/or spring: 14 weeks - 3 hours of lecture per week
Terms offered: Summer 2024, Spring 2024, Fall 2023
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. Natural Language Processing with Deep Learning: Read More [+]
Credit Restrictions: Students will receive no credit for DATASCI W266 after completing DATASCI 266. A deficient grade in DATASCI W266 may be removed by taking DATASCI 266.
Hours & Format
Fall and/or spring: 14 weeks - 3 hours of lecture per week
Terms offered: Not yet offered
This course focuses on the practical aspects of LLMs to enable students to be effective and responsible users of generative AI technologies. The course has three parts. Introduction section covers the historical aspects, key technical ideas and learnings all the way to Transformer architectures and various LLM training aspects. The Practical Aspects and Techniques section, students learn how to train, deploy, and use LLMs; and discuss core concepts like prompt tuning, quantization, and parameter efficient fine-tuning, and explore use case patterns. Finally, a discussion of challenges & opportunities offered by Generative AI, which includes highlighting critical issues like bias and inclusivity, fake information, safety, and some IP issue Generative AI: Read More [+]
Objectives & Outcomes
Student Learning Outcomes: Appreciate the history of the path towards Large Language Models (LLMs) and Generative AI approaches.
Be able to understand key use case patterns of Generative AI approaches and know how to think about incorporating them into applications.
Become aware of critical issues such as bias, inclusivity problems, hallucinations, and IP questions
Become conversant in PyTorch and key neural net coding strategies.
Know how to approach improving the results obtained from LLMs through prompt-tuning, instruction-based fine-tuning, and Reinforcement Learning with Human Feedback.
Understand the foundations of LLMs, how they are trained, and how to deploy and use them, for and beyond text-focused problems.
Rules & Requirements
Prerequisites: MIDS students only. DATASCI 207. Students need to know what gradient descent is. Simple linear classifiers and softmax are reviewed in the course at a high level, but students should have at least heard of these terms. Linear algebra required, which is used for vector representations and deep learning in the course. Intermediate competency in Python required. Experience in PyTorch recommended
Hours & Format
Fall and/or spring: 14 weeks - 3 hours of lecture per week
Terms offered: Summer 2024, Spring 2024, Fall 2023
A continuation of DATASCI 203, 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. Statistical Methods for Discrete Response, Time Series, and Panel Data: Read More [+]
Rules & Requirements
Prerequisites: MIDS students only. DATASCI 203 taken in Fall 2016 or later and completed with a grade of B+ or above. Strong familiarity with classical linear regression modeling; strong hands-on experience in R; working knowledge of calculus and linear algebra; familiarity with differential calculus, integral calculus and matrix notations
Credit Restrictions: Students will receive no credit for DATASCI W271 after completing DATASCI 271. A deficient grade in DATASCI W271 may be removed by taking DATASCI 271.
Hours & Format
Fall and/or spring: 14 weeks - 3 hours of lecture per week
Terms offered: Summer 2024, Spring 2024, Fall 2023
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.
Student Learning Outcomes: Be able to read and understand research papers in the computer-vision literature.
Build computer vision systems to solve real-world problems.
Properly formulate problems with the appropriate mathematical and computational tools.
Understand the building blocks of classical computer vision techniques.
Understand the building blocks of modern computer vision techniques (primarily artificial neural networks).
Understand the process by which images are formed and represented.
Rules & Requirements
Prerequisites: MIDS students only. DATASCI 207. We assume you are familiar with machine learning techniques. You should also be comfortable with linear algebra, which we'll use for vector representations and when we discuss deep learning. Intermediate programming skills in an object-oriented language (e.g., Python). This course will use Python for all examples, exercises, and assignments
Hours & Format
Fall and/or spring: 14 weeks - 3 hours of lecture per week
Terms offered: Summer 2024, Summer 2023
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. Data Science Professional Practicum: Read More [+]
Objectives & Outcomes
Student Learning Outcomes: Assess the organizational culture of a company, governmental body, or non-governmental organization, especially as it relates to data-driven decision making and ability to adopt or employ data science methods
Connect academic knowledge about data science to real-world professional contexts
Evaluate the effectiveness of a variety of data science methods when deployed in organizational situations
Integrate the student's own individual professional goals with the organization's needs relevant to the internship or practicum
Reflect critically on the internship or practicum experience
Rules & Requirements
Prerequisites: 5th Year MIDS students only
Hours & Format
Fall and/or spring: 14 weeks - 1 hour of lecture per week
Summer: 14 weeks - 1 hour of lecture per week
Additional Details
Subject/Course Level: Data Science/Graduate
Grading: Offered for satisfactory/unsatisfactory grade only.
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