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For further clarification on a course, feel free to contact us at contact@susa.berkeley.edu

*4 Units*

For students with mathematical background who wish to acquire basic concepts. Relative frequencies, discrete probability, random variables, expectation. Testing hypotheses. Estimation. Illustrations from various fields. Teaches the R programming language; topics include: data frames, ggplot2, dplyr, statistical computation through code.

**Rules & Requirements**

**Prerequisites:** One semester of calculus

**Credit Restrictions:** Students who have taken 2, 2X, 5, 21, 21X, or 25 will receive no credit for 20.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 2 hours of laboratory per week

**Summer:** 8 weeks - 6 hours of lecture and 3 hours of laboratory per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

**From SUSA: Between Stat 20 and C8**

Stat 20 goes into just very general applications of stats and doesn't teach much theory; mainly just a computational class like high school math (in fact it basically covers AP Statistics but the material is just a bit harder). You'll also get to see how you can utilize R (no strictly "how" R/programming works) with data sets provided by the professor. In addition, the class is mainly just a requirement so that you experience on hand a very basic understanding of what happens in stats (it became a requirement very recently in Fall 2018). So what you learn in this class is very basic and won't be much help in understanding the mathematical basis of things in the upper division classes you have to take.

You can also consider Data 8, though it doesn't really match up with the material you learn specifically in the statistics major. Personally, if you do well in Stat 20 or Data 8, you can learn the material in the other easily and you shouldn't consider taking both. If you are thinking about doing data science, I would suggest you take Data 8 so that you have more options (By taking Data 8, you might be interested in other data science courses like Data 100, Data C102, Stat C140). The drawback to taking Data 8 instead of Stat 20 is just that there are some concepts in Stat 135 which you wouldn't be introduced to in terms of the computation (but the concepts are easily buildable without Stat 20).

*2 Units*

Computer Science 36 is a seminar for CS Scholars who are concurrently taking CS61A: The Structure and Interpretation of Computer Programs. CS Scholars is a cohort-model program to provide support in exploring and potentially declaring a CS major for students with little to no computational background prior to coming to the university. CS 36 provides an introduction to the CS curriculum at UC Berkeley, and the overall CS landscape in both industry and academia—through the lens of accessibility and its relevance to diversity. Additionally, CS36 provides technical instruction to review concepts in CS61A, in order to support CS Scholars’ individual learning and success in the CS61A course.

**Objectives & Outcomes**

**Student Learning Outcomes:** Students will know where to find several support services including tutoring, advising, counseling, and career advice.

Students will perform as well as possible in the CS61A prerequisite for the CS major. They will also have customized program plans for completing the major within four years.

**Rules & Requirements**

**Prerequisites:** Prerequisite satisfied Concurrently: Participating in the CS Scholars program, and concurrently taking COMPSCI 61A

**Hours & Format**

**Fall and/or spring:** 15 weeks - 2 hours of seminar per week

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** Offered for pass/not pass grade only. Alternative to final exam.

**Instructor:** Hunn

*4 Units*

An introduction to programming and computer science focused on abstraction techniques as means to manage program complexity. Techniques include procedural abstraction; control abstraction using recursion, higher-order functions, generators, and streams; data abstraction using interfaces, objects, classes, and generic operators; and language abstraction using interpreters and macros. The course exposes students to programming paradigms, including functional, object-oriented, and declarative approaches. It includes an introduction to asymptotic analysis of algorithms. There are several significant programming projects.

**Rules & Requirements**

**Prerequisites:** MATH 1A (may be taken concurrently); programming experience equivalent to that gained from a score of 3 or above on the Advanced Placement Computer Science A exam

**Credit Restrictions:** Students will receive no credit for Computer Science 61A after completing Computer Science 47A or Computer Science 61AS. A deficient grade in Computer Science 61AS may be removed by taking Computer Science 61A.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture, 1.5 hours of discussion, and 1.5 hours of laboratory per week

**Summer:** 8 weeks - 6 hours of lecture, 3 hours of discussion, and 3 hours of laboratory per week

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

**Instructors:** Garcia, Hilfinger

*4 Units*

A project-based introduction to statistical data analysis. Through case studies, computer laboratories, and a term project, students will learn practical techniques and tools for producing statistically sound and appropriate, reproducible, and verifiable computational answers to scientific questions. Examples will be drawn from a variety of fields, including agriculture, health, public policy, and climate. The course will begin with a discussion of why reproducibility is an issue, what is contributing to the lack of reproducibility in science, and why replication is important. Course emphasizes version control, testing, process automation, code review, and collaborative programming. Software tools may include Bash, Git, Python, and LaTeX.

**Rules & Requirements**

**Prerequisites:** Statistics 133, Statistics 134, and Statistics 135 (or equivalent)

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 2 hours of laboratory per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Alternative to final exam.

*4 Units*

This course teaches a broad range of statistical methods that are used to solve data problems. Topics include group comparisons and ANOVA, standard parametric statistical models, multivariate data visualization, multiple linear regression, logistic regression and classification, regression trees and random forests. An important focus of the course is on statistical computing and reproducible statistical analysis. The course and lab include hands-on experience in analyzing real world data from the social, life, and physical sciences. The R statistical language is used.

**Rules & Requirements**

**Prerequisites:** Statistics/Computer Science/Information C8 or Statistics 20; and Mathematics 1A, Mathematics 16A, or Mathematics 10A/10B. Strongly recommended corequisite: Statistics 33A or Statistics 133

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 2 hours of laboratory per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

**Formerly known as:** Statistics 131A

**Also listed as:** DATA C131A

*3 Units*

Continuation of 16A. Application of integration of economics and life sciences. Differential equations. Functions of many variables. Partial derivatives, constrained and unconstrained optimization.

**Rules & Requirements**

**Prerequisites:** Mathematics 16A or N16A

**Credit Restrictions:** Students will receive no credit for Math N16B after Math 16B, 1B or N1B. A deficient grade in N16B may be removed by completing 16B.

**Hours & Format**

**Summer:** 8 weeks - 8 hours of lecture per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*4 Units*

Logic, mathematical induction sets, relations, and functions. Introduction to graphs, elementary number theory, combinatorics, algebraic structures, and discrete probability theory.

**Rules & Requirements**

**Prerequisites:** Mathematical maturity appropriate to a sophomore math class. 1A-1B recommended

**Credit Restrictions:** Students will receive no credit for Math 55 after completion of Math N55 or Computer Science 70. A deficient grade in Math 55 may be removed by completing Math N55.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 2 hours of discussion per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*4 Units*

An introduction to linear algebra for data science. The course will cover introductory topics in linear algebra, starting with the basics; discrete probability and how prob- ability can be used to understand high-dimensional vector spaces; matrices and graphs as popular mathematical structures with which to model data (e.g., as models for term-document corpora, high-dimensional regression problems, ranking/classification of web data, adjacency properties of social network data, etc.); and geometric approaches to eigendecompositions, least-squares, principal components analysis, etc.

**Rules & Requirements**

**Prerequisites:** One year of calculus. Prerequisite or corequisite: Foundations of Data Science (COMPSCI C8 / INFO C8 / STAT C8)

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 2 hours of laboratory per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*4 Units*

An introduction to probability, emphasizing the combined use of mathematics and programming to solve problems. Random variables, discrete and continuous families of distributions. Bounds and approximations. Dependence, conditioning, Bayes methods. Convergence, Markov chains. Least squares prediction. Random permutations, symmetry, order statistics. Use of numerical computation, graphics, simulation, and computer algebra.

**Objectives & Outcomes**

**Course Objectives:** The emphasis on simulation and the bootstrap in Data 8 gives students a concrete sense of randomness and sampling variability. Stat 140 will capitalize on this, abstraction and computation complementing each other throughout.

The syllabus has been designed to maintain a mathematical level at least equal to that in Stat 134. So Stat 140 will start faster than Stat 134 (due to the Data 8 prerequisite), avoid approximations that are unnecessary when SciPy is at hand, and replace some of the routine calculus by symbolic math done in SymPy. This will create time for a unit on the convergence and reversibility of Markov Chains as well as added focus on conditioning and Bayes methods.

With about a thousand students a year taking Foundations of Data Science (Stat/CS/Info C8, a.k.a. Data 8), there is considerable demand for follow-on courses that build on the skills acquired in that class. Stat 140 is a probability course for Data 8 graduates who have also had a year of calculus and wish to go deeper into data science.

**Student Learning Outcomes:** Understand the difference between math and simulation, and appreciate the power of both

Use a variety of approaches to problem solving

Work with probability concepts algebraically, numerically, and graphically

**Rules & Requirements**

**Prerequisites:**

- Have taken a year of calculus at the level of Math 1A-1B and preferably higher; Prob 140 involves some double integration and partial derivatives
- Have taken or are concurrently taking linear algebra in Math 54 or EE 16A or Stat 89A or Math 110, or have taken an equivalent linear algebra course at another college
- Have taken Data 8 or Data 100 or
**both**Stat 20 and CS 61A

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture, 2 hours of discussion, and 1 hour of supplement per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

**Formerly known as:** Statistics 140

**Also listed as:** DATA C140

More information: http://prob140.org/about/

*2 Units*

Self-paced course in functional programming, using the Scheme programming language, for students who already know how to program. Recursion; higher-order functions; list processing; implementation of rule-based querying.

**Rules & Requirements**

**Prerequisites:** Programming experience similar to that gained in COMPSCI 10 or ENGIN 7

**Credit Restrictions:** Students will receive no credit for COMPSCI 9D after completing COMPSCI 61A.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 2 hours of self-paced per week

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** Offered for pass/not pass grade only. Final exam required.

**Instructor:** Hilfinger

*1 Unit*

MIPS instruction set simulation. The assembly and linking process. Caches and virtual memory. Pipelined computer organization. Students with sufficient partial credit in 61C may, with consent of instructor, complete the credit in this self-paced course.

**Rules & Requirements**

**Prerequisites:** Experience with assembly language including writing an interrupt handler, COMPSCI 9C, and consent of instructor

**Credit Restrictions:** Students will receive no credit for COMPSCI 47C after completing COMPSCI 61C, or COMPSCI 61CL.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 0 hours of self-paced per week

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

**Instructor:** Garcia

Linear algebra background is required for much of the upper-division statistics courses, and you can satisfy this background by taking mostly any linear algebra course including, but not limited to, Math 54, Stat 89A, and Math 110.

Some things to keep in mind: For the statistics major, Stat 89A will not be accepted in lieu of Math 54. However, Stat 89A and Math 54 are enough (and satisfy the same requisites) for courses that require one or the other. *4 Units*

An introductory course for students with minimal prior exposure to computer science. Prepares students for future computer science courses and empowers them to utilize programming to solve problems in their field of study. Presents an overview of the history, great principles, and transformative applications of computer science, as well as a comprehensive introduction to programming. Topics include abstraction, recursion, algorithmic complexity, higher-order functions, concurrency, social implications of computing (privacy, education, algorithmic bias), and engaging research areas (data science, AI, HCI). Students will program in Snap! (a friendly graphical language) and Python, and will design and implement two projects of their choice.

**Rules & Requirements**

**Credit Restrictions:** Students will receive no credit for 10 after having taken W10, 61A, 61B, or 61C.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 2 hours of lecture, 1 hour of discussion, and 4 hours of laboratory per week

**Summer:** 8 weeks - 4 hours of lecture, 2 hours of discussion, and 8 hours of laboratory per week

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

**Instructors:** Garcia, Hug

*1 Unit*

An introduction to the R statistical software for students with minimal prior experience with programming. This course prepares students for data analysis with R. The focus is on the computational model that underlies the R language with the goal of providing a foundation for coding. Topics include data types and structures, such as vectors, data frames and lists; the REPL evaluation model; function calls, argument matching, and environments; writing simple functions and control flow. Tools for reading, analyzing, and plotting data are covered, such as data input/output, reshaping data, the formula language, and graphics models.

**Rules & Requirements**

**Credit Restrictions:** Students will receive no credit for STAT 33A after completing STAT 33B, or STAT 133. A deficient grade in STAT 33A may be removed by taking STAT 33B, or STAT 133.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 1 hour of lecture and 1 hour of laboratory per week

**Summer:** 6 weeks - 2 hours of lecture and 3 hours of laboratory per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*1.5 - 4 Units*

Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester. Enrollment limits are set by the faculty, but the suggested limit is 25.

**Rules & Requirements**

**Prerequisites:** Priority given to freshmen and sophomores

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

**Hours & Format**

**Fall and/or spring:** 15 weeks - 2-4 hours of seminar per week

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** The grading option will be decided by the instructor when the class is offered. Final exam required.

*1.5 - 4 Units*

Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester. Enrollment limits are set by the faculty, but the suggested limit is 25.

**Rules & Requirements**

**Prerequisites:** Priority given to freshmen and sophomores

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

**Hours & Format**

**Fall and/or spring:** 15 weeks - 2-4 hours of seminar per week

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** The grading option will be decided by the instructor when the class is offered. Final exam required.

*4 Units*

The sequence Math 10A, Math 10B is intended for majors in the life sciences. Elementary combinatorics and discrete and continuous probability theory. Representation of data, statistical models and testing. Sequences and applications of linear algebra.

**Rules & Requirements**

**Prerequisites:** Continuation of 10A

**Credit Restrictions:** Students will receive no credit for Mathematics 10B after completing Mathematics N10B. A deficient grade in Math 10B may be removed by taking Math N10B.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 3 hours of discussion per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*4 Units*

A coordinated treatment of linear and generalized linear models and their application. Linear regression, analysis of variance and covariance, random effects, design and analysis of experiments, quality improvement, log-linear models for discrete multivariate data, model selection, robustness, graphical techniques, productive use of computers, in-depth case studies.

**Rules & Requirements**

**Prerequisites:** STAT 102 or STAT 135. STAT 133 recommended

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 2 hours of laboratory per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*1.5 - 2 Units*

Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester. Enrollment limits are set by the faculty, but the suggested limit is 25.

**Rules & Requirements**

**Prerequisites:** Priority given to freshmen and sophomores

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

**Hours & Format**

**Fall and/or spring:** 15 weeks - 2-3 hours of seminar per week

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** Offered for pass/not pass grade only. Final Exam To be decided by the instructor when the class is offered.

*4 Units*

A comprehensive survey course in statistical theory and methodology. Topics include descriptive statistics, maximum likelihood estimation, non-parametric methods, introduction to optimality, goodness-of-fit tests, analysis of variance, bootstrap and computer-intensive methods and least squares estimation. The laboratory includes computer-based data-analytic applications to science and engineering.

**Rules & Requirements**

**Prerequisites:** STAT 134 or STAT 140; and MATH 54, EL ENG 16A, STAT 89A, MATH 110 or equivalent linear algebra. Strongly recommended corerequisite: STAT 133

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 2 hours of laboratory per week

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

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*4 Units*

Parametric equations and polar coordinates. Vectors in 2- and 3-dimensional Euclidean spaces. Partial derivatives. Multiple integrals. Vector calculus. Theorems of Green, Gauss, and Stokes.

**Rules & Requirements**

**Prerequisites:** Mathematics 1B or N1B

**Credit Restrictions:** Students will receive no credit for Mathematics N53 after completing Mathematics 53, H53, or W53; A deficient grade in N53 may be removed by completing Mathematics 53, H53, or W53.

**Hours & Format**

**Summer:** 8 weeks - 10 hours of lecture per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

Upper-division probability background is required for almost every upper-division statistics course that you encounter, and courses that'll satisfy this background include but are not limited to: Stat 134, Stat/Data C140, EECS 126, IND ENG 172.

You can only take either Stat 134 or Stat/Data C140 for credit. At least a B- in one will allow you to declare statistics. If you don't make the B-, you can still declare by getting a B- in Stat 135. It is also highly recommended that you do not take Stat 135 concurrently or before your probability course due to Stat 135 assuming a probability background. *1.5 - 4 Units*

**Rules & Requirements**

**Prerequisites:** Priority given to freshmen and sophomores

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

**Hours & Format**

**Fall and/or spring:** 15 weeks - 2-4 hours of seminar per week

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** The grading option will be decided by the instructor when the class is offered. Final exam required.

These are the lower division math courses needed for understanding most material that you encounter in the statistics major.

For more comprehensible advice, we encourage you to go to MUSA for help! They also have a course map that you can find here: https://musa.berkeley.edu/exposition.html *1 - 3 Units*

Supervised experience relevant to specific aspects of statistics in off-campus settings. Individual and/or group meetings with faculty.

**Rules & Requirements**

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

**Hours & Format**

**Fall and/or spring:** 15 weeks - 1-3 hours of fieldwork per week

**Summer:**

6 weeks - 2.5-7.5 hours of fieldwork per week

8 weeks - 1.5-5.5 hours of fieldwork per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Offered for pass/not pass grade only. Final exam not required.

*4 Units*

Theory and practice of statistical prediction. Contemporary methods as extensions of classical methods. Topics: optimal prediction rules, the curse of dimensionality, empirical risk, linear regression and classification, basis expansions, regularization, splines, the bootstrap, model selection, classification and regression trees, boosting, support vector machines. Computational efficiency versus predictive performance. Emphasis on experience with real data and assessing statistical assumptions.

**Rules & Requirements**

**Prerequisites:** Mathematics 53 or equivalent; Mathematics 54, Electrical Engineering 16A, Statistics 89A, Mathematics 110 or equivalent linear algebra; Statistics 135 or equivalent; experience with some programming language. Recommended prerequisite: Mathematics 55 or equivalent exposure to counting arguments

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 2 hours of laboratory per week

**Summer:** 10 weeks - 4.5 hours of lecture and 3 hours of laboratory per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*4 Units*

Theory and practice of sampling from finite populations. Simple random, stratified, cluster, and double sampling. Sampling with unequal probabilities. Properties of various estimators including ratio, regression, and difference estimators. Error estimation for complex samples.

**Rules & Requirements**

**Prerequisites:** 101 or 134. 133 and 135 recommended

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 2 hours of laboratory per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*3 Units*

Substantial student participation required. The topics to be covered each semester that the course may be offered will be announced by the middle of the preceding semester; see departmental bulletins. Recent topics include: Bayesian statistics, statistics and finance, random matrix theory, high-dimensional statistics.

**Rules & Requirements**

**Prerequisites:** Mathematics 53-54, Statistics 134, 135. Knowledge of scientific computing environment (R or Matlab) often required. Prerequisites might vary with instructor and topics

**Repeat rules:** Course may be repeated for credit with instructor consent.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of seminar per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*1 - 3 Units*

Must be taken at the same time as either Statistics 2 or 21. This course assists lower division statistics students with structured problem solving, interpretation and making conclusions.

**Rules & Requirements**

**Prerequisites:** Consent of instructor

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

**Hours & Format**

**Fall and/or spring:** 15 weeks - 2-3 hours of directed group study per week

**Summer:** 8 weeks - 4-6 hours of directed group study per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Offered for pass/not pass grade only. Final exam not required.

These are statistics courses (crosslisted courses with data science are not highlighted), and these classes, except for 33A, 88, and 89A, could possibly fulfill something in the statistics major.

*4 Units*

This sequence is intended for majors in engineering and the physical sciences. An introduction to differential and integral calculus of functions of one variable, with applications and an introduction to transcendental functions.

**Rules & Requirements**

**Prerequisites:** Three and one-half years of high school math, including trigonometry and analytic geometry. Students with high school exam credits (such as AP credit) should consider choosing a course more advanced than 1A

**Credit Restrictions:** Students will receive no credit for MATH 1A after completing MATH N1A, MATH 16B, Math N16B or XMATH 1A. A deficient grade in MATH 1A may be removed by taking MATH N1A.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 3 hours of discussion per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

**Substitutions/Equivalents:** Articulated CC course, AP/A-Levels/IB exam, or summer version of the class (MATH N1A)

*4 Units*

The sequence Math 10A, Math 10B is intended for majors in the life sciences. Elementary combinatorics and discrete and continuous probability theory. Representation of data, statistical models and testing. Sequences and applications of linear algebra.

**Rules & Requirements**

**Prerequisites:** Math 10A or N10A

**Credit Restrictions:** Students will receive no credit for Math N10B after completing Math 10B. A deficient grade in Math N10B may be removed by completing Math 10B.

**Hours & Format**

**Summer:** 8 weeks - 10 hours of lecture per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

You can see the lower division (declaration) requirements for the statistics major highlighted.

These are:

- Math:
- Math 1A
- Math 1B
- Math 53
- Math 54
- You must earn a 3.2 GPA in at least one of these courses (or their equivalent) in the UC system. If you've already taken all four at non-UC insitutions, you can choose from Math 54 (B+ required) or Math 110 (B required) to pass the math requirement.
- The same AP credit system used by the math system applies to these courses. https://math.berkeley.edu/courses/choosing/high-school-exam-credits
- Lower Div Statistics:
- Minimum C grade in: Stat 20 or Stat/Compsci/Data C8
- Upper Div Statistics:
- Minimum B- grade in Stat 134/C140 or Stat 135

** Between Stat 20 and C8**

Stat 20 goes into just very general applications of stats and doesn't teach much theory; mainly just a computational class like high school math (in fact it basically covers AP Statistics but the material is just a bit harder). You'll also get to see how you can utilize R (no strictly "how" R/programming works) with data sets provided by the professor. In addition, the class is mainly just a requirement so that experience on hand a very basic understanding of what happens in stats (it became a requirement very recently in Fall 2018). So what you learn in this class is very basic and won't be much help in understanding the mathematical basis of things in the upper division classes you have to take.

You can also consider Data 8, though it doesn't really match up with the material you learn specifically in the statistics major. Personally, if you do well in Stat 20 or Data 8, you can learn the material in the other easily and you shouldn't consider taking both. If you are thinking about doing data science, I would suggest you take Data 8 so that you have more options (By taking Data 8, you might be interested in other data science courses like Data 100, Data C102, Stat C140). The drawback to taking Data 8 instead of Stat 20 is just that there are some concepts in Stat 135 which you wouldn't be introduced to in terms of the computation (but the concepts are easily buildable without Stat 20).

It is also highly recommended that you do not take Stat 135 concurrently or before your probability course due to Stat 135 assuming a probability background. Stat 135 is included in these declaring guidelines mainly because it gives you have a chance to declare if you don't get a B- in Stat 134/C140.

Choosing between Stat 134 and C140, you can view the course websites https://www.stat134.org/ and http://prob140.org/about/

More info and articulated info can be found at: https://statistics.berkeley.edu/programs/undergrad/major

*4 Units*

Population and variables. Standard measures of location, spread and association. Normal approximation. Regression. Probability and sampling. Binomial distribution. Interval estimation. Some standard significance tests.

**Rules & Requirements**

**Credit Restrictions:** Students who have taken 2X, 5, 20, 21, 21X, or 25 will receive no credit for 2.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 2 hours of laboratory per week

**Summer:**

6 weeks - 7.5 hours of lecture and 5 hours of laboratory per week

8 weeks - 5 hours of lecture and 4 hours of laboratory per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*3 Units*

An introduction to computational thinking and quantitative reasoning, preparing students for further coursework, especially Foundations of Data Science (CS/Info/Stat C8). Emphasizes the use of computation to gain insight about quantitative problems with real data. Expressions, data types, collections, and tables in Python. Programming practices, abstraction, and iteration. Visualizing univariate and bivariate data with bar charts, histograms, plots, and maps. Introduction to statistical concepts including averages and distributions, predicting one variable from another, association and causality, probability and probabilistic simulation. Relationship between numerical functions and graphs. Sampling and introduction to inference.

**Objectives & Outcomes**

**Course Objectives:** C6 also includes quantitative reasoning concepts that aren’t covered in Data 8. These include certain topics in: principles of data visualization; simulation of random processes; and understanding numerical functions through their graphs. This will help prepare students for computational and quantitative courses other than Data 8.

C6 takes advantage of the complementarity of computing and quantitative reasoning to enliven abstract ideas and build students’ confidence in their ability to solve real problems with quantitative tools. Students learn computer science concepts and immediately apply them to plot functions, visualize data, and simulate random events.

Foundations of Data Science (CS/Info/Stat C8, a.k.a. Data 8) is an increasingly popular class for entering students at Berkeley. Data 8 builds students’ computing skills in the first month of the semester, and students rely on these skills as the course progresses. For some students, particularly those with little prior exposure to computing, developing these skills benefits from further time and practice. C6 is a rapid introduction to Python programming, visualization, and data analysis, which will prepare students for success in Data 8.

**Student Learning Outcomes:** Students will be able to perform basic computations in Python, including working with tabular data.

Students will be able to understand basic probabilistic simulations.

Students will be able to understand the syntactic structure of Python code.

Students will be able to use good practices in Python programming.

Students will be able to use visualizations to understand univariate data and to identify associations or causal relationships in bivariate data.

**Rules & Requirements**

**Credit Restrictions:** Students will receive no credit for DATA C6\COMPSCI C6\STAT C6 after completing DATA C8, or DATA 6. A deficient grade in DATA C6\COMPSCI C6\STAT C6 may be removed by taking DATA 6.

**Hours & Format**

**Summer:** 6 weeks - 4 hours of lecture, 2 hours of discussion, and 4 hours of laboratory per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

**Formerly known as:** Computer Science C8R/Statistics C8R

**Also listed as:** COMPSCI C6/DATA C6

*2 Units*

Self-paced course in Java for students who already know how to program. Applets; variables and computation; events and flow of control; classes and objects; inheritance; GUI elements; applications; arrays, strings, files, and linked structures; exceptions; threads.

**Rules & Requirements**

**Prerequisites:** COMPSCI 9C, COMPSCI 9F, or COMPSCI 61A plus experience with object-oriented programming or C-based language

**Hours & Format**

**Fall and/or spring:** 15 weeks - 2 hours of self-paced per week

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** Offered for pass/not pass grade only. Final exam required.

**Instructor:** Garcia

You can see the core courses for the statistics major highlighted.

These are:

- Stat 133 OR Stat/Data/Compsci C100 and Stat 33B
- Stat 134 OR Stat C140
- Stat 135

To declare, among these classes, you just need a B- in Stat 134 or Stat C140. If you don't make that B-, you still have a chance by getting a B- in Stat 135.

If you're deciding between Stat 133 OR Stat/Data/Compsci C100 and Stat 33B, consider that Stat 33B + Data 100 option requires more prereqs just because of Data 100 . To iterate more, on choosing between these two, Data 100 requires 61A/88, linear algebra, and Data 8 [which is not really needed since you took Stat 20]. But, It does teach you a lot more skills since you get you learn Python and use the matplotlib, pandas, scikitlearn, etc. packages. If you're looking to go into industry, this option is something to really consider since there is a fair amount of places that want you to know Python to do things. Theory knowledge wise, you might get the shortend and not learn much from Data 100 except how to use Python for data science if you have enough statistics background. As for comparison between 33B and 133, there's not much to say. If you take 33B, then you will already be having the programming experience from 61A/88 and other concepts covered in 133 should come naturally to you. If you decide to go the 133 route, you won't learn Python at all and will have limited programming experience (it doesn't teach much "CS" concepts). If you're minoring/doubling in data science, it's unit efficient to take the Data 100 + Stat 33B option.

Between Stat 134 and C140, you can view the course websites https://www.stat134.org/ and http://prob140.org/about/

*4 Units*

Continuation of 1A. Techniques of integration; applications of integration. Infinite sequences and series. First-order ordinary differential equations. Second-order ordinary differential equations; oscillation and damping; series solutions of ordinary differential equations.

**Rules & Requirements**

**Prerequisites:** 1A or N1A

**Credit Restrictions:** Students will receive no credit for Math 1B after completing Math N1B, H1B, Xmath 1B. A deficient grade in MATH 1B may be removed by taking MATH N1B or MATH H1B.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 3 hours of discussion per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

**Substitutions/Equivalents:** Articulated CC course, AP/A-Levels/IB exam, or summer version of the class (MATH N1B)

*3 Units*

Random walks, discrete time Markov chains, Poisson processes. Further topics such as: continuous time Markov chains, queueing theory, point processes, branching processes, renewal theory, stationary processes, Gaussian processes.

**Rules & Requirements**

**Prerequisites:** 101 or 103A or 134

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*2 Units*

Use of UNIX utilities and scripting facilities for customizing the programming environment, organizing files (possibly in more than one computer account), implementing a personal database, reformatting text, and searching for online resources.

**Rules & Requirements**

**Prerequisites:** Programming experience similar to that gained in COMPSCI 61A or ENGIN 7; DOS or UNIX experience

**Hours & Format**

**Fall and/or spring:** 15 weeks - 2 hours of self-paced per week

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** Offered for pass/not pass grade only. Final exam required.

**Instructor:** Hilfinger

*4 Units*

Reasoning and fallacies, descriptive statistics, probability models and related concepts, combinatorics, sample surveys, estimates, confidence intervals, tests of significance, controlled experiments vs. observational studies, correlation and regression.

**Rules & Requirements**

**Prerequisites:** One semester of calculus

**Credit Restrictions:** Students will receive no credit for Statistics W21 after completing Statistics 2, 20, 21, N21 or 25. A deficient grade in Statistics 21, N21 maybe removed by taking Statistics W21.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of web-based lecture per week

**Summer:** 8 weeks - 7.5 hours of web-based lecture per week

**Online:** This is an online course.

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

**Formerly known as:** N21

*3 Units*

General theory of zero-sum, two-person games, including games in extensive form and continuous games, and illustrated by detailed study of examples.

**Rules & Requirements**

**Prerequisites:** 101 or 134

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture per week

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

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*4 Units*

Honors version of 1B. Continuation of 1A. Techniques of integration; applications of integration. Infinite sequences and series. First-order ordinary differential equations. Second-order ordinary differential equations; oscillation and damping; series solutions of ordinary differential equations.

**Rules & Requirements**

**Prerequisites:** 1A

**Credit Restrictions:** Students will receive no credit for Mathematics H1B after completing Mathematics 1B or N1B.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 2 hours of discussion per week

**Summer:** 8 weeks - 5 hours of lecture and 5 hours of discussion per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*4 Units*

This course will focus on approaches to causal inference using the potential outcomes framework. It will also use causal diagrams at an intuitive level. The main topics are classical randomized experiments, observational studies, instrumental variables, principal stratification and mediation analysis. Applications are drawn from a variety of fields including political science, economics, sociology, public health, and medicine. This course is a mix of statistical theory and data analysis. Students will be exposed to statistical questions that are relevant to decision and policy making.

**Rules & Requirements**

**Prerequisites:** Statistics 135

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 2 hours of laboratory per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*4 Units*

This course develops the probabilistic foundations of inference in data science, and builds a comprehensive view of the modeling and decision-making life cycle in data science including its human, social, and ethical implications. Topics include: frequentist and Bayesian decision-making, permutation testing, false discovery rate, probabilistic interpretations of models, Bayesian hierarchical models, basics of experimental design, confidence intervals, causal inference, Thompson sampling, optimal control, Q-learning, differential privacy, clustering algorithms, recommendation systems and an introduction to machine learning tools including decision trees, neural networks and ensemble methods.

**Rules & Requirements**

**Prerequisites:** Mathematics 54 or Mathematics 110 or Statistics 89A or Physics 89 or both of Electrical Engineering and Computer Science 16A and Electrical Engineering and Computer Science 16B; Statistics/Computer Science C100; and any of Electrical Engineering and Computer Science 126, Statistics 140, Statistics 134, Industrial Engineering and Operations Research 172. Statistics 140 or Electrical Engineering and Computer Science 126 are preferred

**Credit Restrictions:** Students will receive no credit for DATA C102 after completing STAT 102, or DATA 102. A deficient grade in DATA C102 may be removed by taking STAT 102, STAT 102, or DATA 102.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture, 1 hour of discussion, and 1 hour of laboratory per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

**Formerly known as:** Statistics 102

**Also listed as:** DATA C102

These are data science courses (all of which are crosslisted with the statistics department), most commonly taken as part of the data science major/minor.

*3 Units*

This sequence is intended for majors in the life and social sciences. Calculus of one variable; derivatives, definite integrals and applications, maxima and minima, and applications of the exponential and logarithmic functions.

**Rules & Requirements**

**Prerequisites:** Three years of high school math, including trigonometry. Consult the mathematics department for details

**Credit Restrictions:** Students will receive no credit for 16A after taking N16A, 1A, or N1A. A deficient grade in Math 16A may be removed by taking Math N16A.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 1.5 hours of discussion per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*1.5 - 4 Units*

**Rules & Requirements**

**Prerequisites:** Priority given to freshmen and sophomores

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

**Hours & Format**

**Fall and/or spring:** 15 weeks - 2-4 hours of seminar per week

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** The grading option will be decided by the instructor when the class is offered. Final exam required.

*2 Units*

Self-paced course in the C programming language for students who already know how to program. Computation, input and output, flow of control, functions, arrays, and pointers, linked structures, use of dynamic storage, and implementation of abstract data types.

**Rules & Requirements**

**Prerequisites:** Programming experience with pointers (or addresses in assembly language) and linked data structures equivalent to that gained in COMPSCI 9B, COMPSCI 61A or ENGIN 7

**Credit Restrictions:** Students will receive no credit for COMPSCI 9C after completing COMPSCI 61A.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 2 hours of self-paced per week

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** Offered for pass/not pass grade only. Final exam required.

**Instructor:** Hilfinger

*1 Unit*

The course is designed primarily for those who are already familiar with programming in another language, such as python, and want to understand how R works, and for those who already know the basics of R programming and want to gain a more in-depth understanding of the language in order to improve their coding. The focus is on the underlying paradigms in R, such as functional programming, atomic vectors, complex data structures, environments, and object systems. The goal of this course is to better understand programming principles in general and to write better R code that capitalizes on the language's design.

**Rules & Requirements**

**Prerequisites:** Compsci 61A or equivalent programming background

**Credit Restrictions:** Students will receive no credit for STAT 33B after completing STAT 133. A deficient grade in STAT 33B may be removed by taking STAT 133.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 1 hour of lecture and 1 hour of laboratory per week

**Summer:** 6 weeks - 2 hours of lecture and 3 hours of laboratory per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*2 Units*

Introduction to the constructs provided in the Python programming language, aimed at students who already know how to program. Flow of control; strings, tuples, lists, and dictionaries; CGI programming; file input and output; object-oriented programming; GUI elements.

**Rules & Requirements**

**Prerequisites:** Programming experience equivalent to that gained in COMPSCI 10

**Hours & Format**

**Fall and/or spring:** 15 weeks - 1 hour of self-paced per week

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** Offered for pass/not pass grade only. Final exam required.

**Instructor:** Hilfinger

*4 Units*

An introduction to time series analysis in the time domain and spectral domain. Topics will include: estimation of trends and seasonal effects, autoregressive moving average models, forecasting, indicators, harmonic analysis, spectra.

**Rules & Requirements**

**Prerequisites:** 101, 134 or consent of instructor. 133 or 135 recommended

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 2 hours of laboratory per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*4 Units*

Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership.

**Rules & Requirements**

**Prerequisites:** This course may be taken on its own, but students are encouraged to take it concurrently with a data science connector course (numbered 88 in a range of departments)

**Credit Restrictions:** Students will receive no credit for DATA C8\COMPSCI C8\INFO C8\STAT C8 after completing COMPSCI 8, or DATA 8. A deficient grade in DATA C8\COMPSCI C8\INFO C8\STAT C8 may be removed by taking COMPSCI 8, COMPSCI 8, or DATA 8.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3-3 hours of lecture and 2-2 hours of laboratory per week

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

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

**Formerly known as:** Computer Science C8/Statistics C8/Information C8

**Also listed as:** COMPSCI C8/DATA C8/INFO C8

** From SUSA: Between Stat 20 and C8**

Stat 20 goes into just very general applications of stats and doesn't teach much theory; mainly just a computational class like high school math (in fact it basically covers AP Statistics but the material is just a bit harder). You'll also get to see how you can utilize R (no strictly "how" R/programming works) with data sets provided by the professor. In addition, the class is mainly just a requirement so that experience on hand a very basic understanding of what happens in stats (it became a requirement very recently in Fall 2018). So what you learn in this class is very basic and won't be much help in understanding the mathematical basis of things in the upper division classes you have to take.

You can also consider Data 8, though it doesn't really match up with the material you learn specifically in the statistics major. Personally, if you do well in Stat 20 or Data 8, you can learn the material in the other easily and you shouldn't consider taking both. If you are thinking about doing data science, I would suggest you take Data 8 so that you have more options (By taking Data 8, you might be interested in other data science courses like Data 100, Data C102, Stat C140). The drawback to taking Data 8 instead of Stat 20 is just that there are some concepts in Stat 135 which you wouldn't be introduced to in terms of the computation (but the concepts are easily buildable without Stat 20).

*4 Units*

Continuation of 1A. Techniques of integration; applications of integration. Infinite sequences and series. First-order ordinary differential equations. Second-order ordinary differential equations; oscillation and damping; series solutions of ordinary differential equations.

**Rules & Requirements**

**Prerequisites:** 1A or N1A

**Credit Restrictions:** Students will receive no credit for Math N1B after completing Math 1B, H1B, or Xmath 1B. A deficient grade in N1B may be removed by completing Mathematics 1B or H1B.

**Hours & Format**

**Summer:** 8 weeks - 10 hours of lecture per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*3 Units*

Continuation of 16A. Application of integration of economics and life sciences. Differential equations. Functions of many variables. Partial derivatives, constrained and unconstrained optimization.

**Rules & Requirements**

**Prerequisites:** 16A

**Credit Restrictions:** Students will receive no credit for MATH 16B after completing MATH N16B, 1B, or N1B. A deficient grade in Math 16B may be removed by taking Math N16B.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 1.5 hours of discussion per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*2 - 4 Units*

Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester.

**Rules & Requirements**

**Prerequisites:** Priority given to freshmen and sophomores

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

**Hours & Format**

**Fall and/or spring:** 15 weeks - 2-4 hours of seminar per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final Exam To be decided by the instructor when the class is offered.

*4 Units*

Basic linear algebra; matrix arithmetic and determinants. Vector spaces; inner product spaces. Eigenvalues and eigenvectors; orthogonality, symmetric matrices. Linear second-order differential equations; first-order systems with constant coefficients. Fourier series.

**Rules & Requirements**

**Prerequisites:** 1B, N1B, 10B, or N10B

**Credit Restrictions:** Students will receive no credit for Math 54 after taking Math N54 or H54. A deficient grade in Math 54 may be removed by completing Math N54.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 3 hours of discussion per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

**Substitutions/Equivalents:** Articulated CC course or summer version of the class (MATH N54)

*4 Units*

Parametric equations and polar coordinates. Vectors in 2- and 3-dimensional Euclidean spaces. Partial derivatives. Multiple integrals. Vector calculus. Theorems of Green, Gauss, and Stokes.

**Rules & Requirements**

**Prerequisites:** Mathematics 1B or equivalent

**Credit Restrictions:** Students will receive no credit for Mathematics W53 after completing Mathematics 53 or N53. A deficient grade in Mathematics W53 may be removed by completing Mathematics 53 or N53.

**Hours & Format**

**Summer:** 8 weeks - 5 hours of web-based lecture and 5 hours of web-based discussion per week

**Online:** This is an online course.

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

**Instructor:** Hutchings

*1 Unit*

Iterators. Hashing, applied to strings and multi-dimensional structures. Heaps. Storage management. Design and implementation of a program containing hundreds of lines of code. Students who have completed a portion of the subject matter of College as well as the EECS department for the course to count in place of COMPSCI 61B.

**Rules & Requirements**

**Prerequisites:** A course in data structures, COMPSCI 9G, and consent of instructor

**Credit Restrictions:** Students will receive no credit for 47B after taking 61B.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 0 hours of self-paced per week

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

**Instructor:** Garcia

*1 - 4 Units*

Topics will vary semester to semester.

**Rules & Requirements**

**Prerequisites:** Consent of instructor

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

**Hours & Format**

**Fall and/or spring:** 15 weeks - 1-3 hours of lecture and 0-2 hours of discussion per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*1.5 - 4 Units*

**Rules & Requirements**

**Prerequisites:** Priority given to freshmen and sophomores

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

**Hours & Format**

**Fall and/or spring:** 15 weeks - 2-4 hours of seminar per week

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** The grading option will be decided by the instructor when the class is offered. Final exam required.

*3 Units*

This sequence is intended for majors in the life and social sciences. Calculus of one variable; derivatives, definite integrals and applications, maxima and minima, and applications of the exponential and logarithmic functions.

**Rules & Requirements**

**Prerequisites:** Three years of high school math, including trigonometry

**Credit Restrictions:** Students will receive no credit for 16A after taking N16A, 1A or N1A. A deficient grade in N16A may be removed by completing 16A.

**Hours & Format**

**Summer:** 8 weeks - 8 hours of lecture per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*4 Units*

An introduction to the design and analysis of experiments. This course covers planning, conducting, and analyzing statistically designed experiments with an emphasis on hands-on experience. Standard designs studied include factorial designs, block designs, latin square designs, and repeated measures designs. Other topics covered include the principles of design, randomization, ANOVA, response surface methodoloy, and computer experiments.

**Rules & Requirements**

**Prerequisites:** Statistics 134 and 135 or consent of instructor. Statistics 135 may be taken concurrently. Statistics 133 is recommended

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 2 hours of laboratory per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*2 Units*

Self-paced introduction to the constructs provided in the C++ programming language for procedural and object-oriented programming, aimed at students who already know how to program.

**Rules & Requirements**

**Prerequisites:** Programming experience equivalent to that gained in COMPSCI 61A or ENGIN 7

**Credit Restrictions:** Students will receive no credit for COMPSCI 9F after completing COMPSCI 61A.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 2 hours of self-paced per week

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** Offered for pass/not pass grade only. Final exam required.

**Instructor:** Hilfinger

*1.5 - 4 Units*

**Rules & Requirements**

**Prerequisites:** Priority given to freshmen and sophomores

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

**Hours & Format**

**Fall and/or spring:** 15 weeks - 2-4 hours of seminar per week

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** The grading option will be decided by the instructor when the class is offered. Final exam required.

*4 Units*

Parametric equations and polar coordinates. Vectors in 2- and 3-dimensional Euclidean spaces. Partial derivatives. Multiple integrals. Vector calculus. Theorems of Green, Gauss, and Stokes.

**Rules & Requirements**

**Prerequisites:** Mathematics 1B or N1B

**Credit Restrictions:** Students will receive no credit for Mathematics 53 after completing Mathematics N53 or W53; A deficient grade in 53 may be removed by completing Mathematics N53 or W53.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 3 hours of discussion per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

**Substitutions/Equivalents:** Articulated CC course or summer/web version of the class (MATH N53 or W53)

*1 Unit*

Implementation of generic operations. Streams and iterators. Implementation techniques for supporting functional, object-oriented, and constraint-based programming in the Scheme programming language. Together with 9D, 47A constitutes an abbreviated, self-paced version of 61A for students who have already taken a course equivalent to 61B.

**Rules & Requirements**

**Prerequisites:** COMPSCI 61B, COMPSCI 9D, and consent of instructor

**Credit Restrictions:** Students will receive no credit for 47A after taking 61A.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 0 hours of self-paced per week

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

**Instructor:** Garcia

*2 - 4 Units*

Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester.

**Rules & Requirements**

**Prerequisites:** Priority given to freshmen and sophomores

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

**Hours & Format**

**Fall and/or spring:** 15 weeks - 2-4 hours of seminar per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** The grading option will be decided by the instructor when the class is offered. Final exam required.

*4 Units*

Polynomial and rational functions, exponential and logarithmic functions, trigonometry and trigonometric functions. Complex numbers, fundamental theorem of algebra, mathematical induction, binomial theorem, series, and sequences.

**Rules & Requirements**

**Prerequisites:** Three years of high school mathematics

**Credit Restrictions:** Students will receive no credit for MATH N32 after completing MATH 32, 1A-1B (or N1A-N1B) or 16A-16B (or N16A-16B), or XMATH 32. A deficient grade in MATH 32 or XMATH 32 maybe removed by taking MATH N32.

**Hours & Format**

**Summer:** 8 weeks - 10 hours of lecture per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*4 Units*

This course meets the programming prerequisite for 61A. An introduction to the beauty and joy of computing. The history, social implications, great principles, and future of computing. Beautiful applications that have changed the world. How computing empowers discovery and progress in other fields. Relevance of computing to the student and society will be emphasized. Students will learn the joy of programming a computer using a friendly, graphical language, and will complete a substantial team programming project related to their interests.

**Rules & Requirements**

**Credit Restrictions:** Students will receive no credit for W10 after taking 10, 61A, 61B or 61C. A deficient grade in 10 may be removed by taking W10.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 2 hours of web-based lecture and 5 hours of web-based discussion per week

**Summer:** 8 weeks - 4 hours of web-based lecture and 10 hours of web-based discussion per week

**Online:** This is an online course.

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

**Instructors:** Garcia, Hug

*4 Units*

Descriptive statistics, probability models and related concepts, sample surveys, estimates, confidence intervals, tests of significance, controlled experiments vs. observational studies, correlation and regression.

**Rules & Requirements**

**Prerequisites:** One semester of calculus

**Credit Restrictions:** Students will receive no credit for Statistics 21 after completing Statistics 2, 2X, 5, 20, 21X, N21, W21 or 25 . A deficiency in Statistics 21 may be moved by taking W21.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 2 hours of laboratory per week

**Summer:** 8 weeks - 5 hours of lecture and 4 hours of laboratory per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*1 Unit*

The Berkeley Seminar Program has been designed to provide new students with the opportunity to explore an intellectual topic with a faculty member in a small-seminar setting. Berkeley seminars are offered in all campus departments, and topics vary from department to department and semester to semester. Enrollment limited to 15 freshmen.

**Rules & Requirements**

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

**Hours & Format**

**Fall and/or spring:** 15 weeks - 1 hour of seminar per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** The grading option will be decided by the instructor when the class is offered. Final exam required.

*3 Units*

In this connector course we will state precisely and prove results discovered while exploring data in Data 8. Topics include: probability, conditioning, and independence; random variables; distributions and joint distributions; expectation, variance, tail bounds; Central Limit Theorem; symmetries in random permutations; prior and posterior distributions; probabilistic models; bias-variance tradeoff; testing hypotheses; correlation and the regression model.

**Rules & Requirements**

**Prerequisites:** Prerequisite: one semester of calculus at the level of Math 16A, Math 10A, or Math 1A. Corequisite or Prerequisite: Foundations of Data Science (COMPSCI C8 / DATASCI C8 / INFO C8 / STAT C8)

**Credit Restrictions:** Students will receive no credit for DATA C88S after completing STAT 134, STAT 140, STAT 135, or STAT 102.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 2 hours of discussion per week

**Additional Details**

**Subject/Course Level:** Data Science/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

**Formerly known as:** Statistics 88

**Also listed as:** STAT C88S

*4 Units*

In this course, students will explore the data science lifecycle, including question formulation, data collection and cleaning, exploratory data analysis and visualization, statistical inference and prediction, and decision-making. This class will focus on quantitative critical thinking and key principles and techniques needed to carry out this cycle. These include languages for transforming, querying and analyzing data; algorithms for machine learning methods including regression, classification and clustering; principles behind creating informative data visualizations; statistical concepts of measurement error and prediction; and techniques for scalable data processing.

**Rules & Requirements**

**Prerequisites:** COMPSCI C8 / DATA C8 / INFO C8 / STAT C8; and COMPSCI 61A, COMPSCI 88, or ENGIN 7; Corequisite: MATH 54 or EECS 16A

**Credit Restrictions:** Students will receive no credit for DATA C100\STAT C100\COMPSCI C100 after completing DATA 100. A deficient grade in DATA C100\STAT C100\COMPSCI C100 may be removed by taking DATA 100.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture, 1 hour of discussion, and 1 hour of laboratory per week

**Summer:** 8 weeks - 6 hours of lecture, 2 hours of discussion, and 2 hours of laboratory per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

**Formerly known as:** Statistics C100/Computer Science C100

**Also listed as:** COMPSCI C100/DATA C100

*4 Units*

The sequence Math 10A, Math 10B is intended for majors in the life sciences. Introduction to differential and integral calculus of functions of one variable, ordinary differential equations, and matrix algebra and systems of linear equations.

**Rules & Requirements**

**Prerequisites:** Three and one-half years of high school math, including trigonometry and analytic geometry. Students who have not had calculus in high school are strongly advised to take the Student Learning Center's Math 98 adjunct course for Math 10A; contact the SLC for more information

**Credit Restrictions:** Students will receive no credit for Mathematics 10A after completing Mathematics N10A. A deficient grade in Math 10A may be removed by taking Math N10A.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 3 hours of discussion per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*4 Units*

Basic linear algebra; matrix arithmetic and determinants. Vector spaces; inner product spaces. Eigenvalues and eigenvectors; orthogonality, symmetric matrices. Linear second-order differential equations; first-order systems with constant coefficients. Fourier series.

**Rules & Requirements**

**Prerequisites:** 1B, N1B, 10B, or N10B

**Credit Restrictions:** Students will receive no credit for Math N54 after completing Math 54 or Math H54; A deficient grade in N54 may be removed by completing Mathematics 54 or H54.

**Hours & Format**

**Summer:** 8 weeks - 10 hours of lecture per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*1 Unit*

This course assists entering Freshman students with basic statistical concepts and problem solving. Designed for students who do not meet the prerequisites for 2. Offered through the Student Learning Center.

**Rules & Requirements**

**Prerequisites:** Consent of instructor

**Hours & Format**

**Summer:**

6 weeks - 5 hours of lecture and 4.5 hours of workshop per week

8 weeks - 5 hours of lecture and 4.5 hours of workshop per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Offered for pass/not pass grade only. Final exam required.

**Instructor:** Purves

You can see the upper division courses accepted as electives for the statistics major highlighted. As a statistics major, you will have to take at least 3 of these courses and at least one of them must have a lab.

- Stat/Data C102 (lab) Data, Inference, and Decisions
- Stat 150 Stochastic Processes
- Stat 151A (lab) Linear Modeling: Theory and Applications
- Stat 152 (lab) Sampling Surveys
- Stat 153 (lab) Introduction to Time Series
- Stat 154 (lab) Modern Statistical Prediction and Machine Learning
- Stat 155 Game Theory
- Stat 156 Causal Inference
- Stat 157 Seminar on Topics in Probability and Statistics
- Stat 158 (lab) The Design and Analysis of Experiments
- Stat 159 (lab) Reproducible and Collaborative Statistical Data Science

You can find notes about some of these classes: https://piazza.com/class/jua820aaxcq1o6?cid=7> on the STAT 001 Piazza , though these notes have also been included when you click on one of these classes on the map

*1 - 3 Units*

Students with partial credit in lower division mathematics courses may, with consent of instructor, complete the credit under this heading.

**Rules & Requirements**

**Prerequisites:** Some units in a lower division Mathematics class

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

**Hours & Format**

**Fall and/or spring:** 15 weeks - 0 hours of independent study per week

**Summer:**

6 weeks - 1-5 hours of independent study per week

8 weeks - 1-4 hours of independent study per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam not required.

*1.5 - 4 Units*

**Rules & Requirements**

**Prerequisites:** Priority given to freshmen and sophomores

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

**Hours & Format**

**Fall and/or spring:** 15 weeks - 2-4 hours of seminar per week

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** The grading option will be decided by the instructor when the class is offered. Final exam required.

*3 Units*

Defining, perceiving, quantifying and measuring risk; identifying risks and estimating their importance; determining whether laws and regulations can protect us from these risks; examining how well existing laws work and how they could be improved; evaluting costs and benefits. Applications may vary by term. This course cannot be used to complete engineering unit or technical elective requirements for students in the College of Engineering.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 1 hour of discussion per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam not required.

**Also listed as:** COMPSCI C79/POL SCI C79

*4 Units*

The sequence Math 10A, Math 10B is intended for majors in the life sciences. Introduction to differential and integral calculus of functions of one variable, ordinary differential equations, and matrix algebra and systems of linear equations.

**Rules & Requirements**

**Prerequisites:** Three and one-half years of high school math, including trigonometry and analytic geometry. Students who have not had calculus in high school are strongly advised to take the Student Learning Center's Math 98 adjunct course for Math 10A; contact the SLC for more information

**Credit Restrictions:** Students will receive no credit for Math N10A after completing Math 10A. A deficient grade in Math N10A may be removed by completing Math 10A.

**Hours & Format**

**Summer:** 8 weeks - 10 hours of lecture per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*4 Units*

Honors version of 54. Basic linear algebra: matrix arithmetic and determinants. Vectors spaces; inner product spaces. Eigenvalues and eigenvectors; linear transformations. Homogeneous ordinary differential equations; first-order differential equations with constant coefficients. Fourier series and partial differential equations.

**Rules & Requirements**

**Prerequisites:** 1B

**Credit Restrictions:** Students will receive no credit for Math H54 after completion of Math 54 or N54.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 3 hours of discussion per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*3 Units*

An introduction to computationally intensive applied statistics. Topics will include organization and use of databases, visualization and graphics, statistical learning and data mining, model validation procedures, and the presentation of results.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 2 hours of laboratory per week

**Summer:** 10 weeks - 4 hours of lecture and 3 hours of laboratory per week

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

**From SUSA:**

If you're only majoring in statistics and have no prior programming experience, we urge you to take this concurrently with Stat 135 since courses like 151A, 153, 154, will be much easier to manage if you know computing concepts and R.

*4 Units*

Polynomial and rational functions, exponential and logarithmic functions, trigonometry and trigonometric functions. Complex numbers, fundamental theorem of algebra, mathematical induction, binomial theorem, series, and sequences.

**Rules & Requirements**

**Prerequisites:** Three years of high school mathematics

**Credit Restrictions:** Students will receive no credit for Math 32 after taking N32, 1A or N1A, 1B or N1B, 16A or N16A, 16B or N16B. A deficient grade in Math 32 may be removed by taking Math N32.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 2 hours of discussion per week

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

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*4 Units*

This sequence is intended for majors in engineering and the physical sciences. An introduction to differential and integral calculus of functions of one variable, with applications and an introduction to transcendental functions.

**Rules & Requirements**

**Prerequisites:** Three and one-half years of high school math, including trigonometry and analytic geometry. Students with high school exam credits (such as AP credit) should consider choosing a course more advanced than 1A

**Credit Restrictions:** Students will receive no credit for MATH N1A after completing MATH 1A, MATH 16B or MATH N16B. A deficient grade in MATH N1A may be removed by taking MATH 1A.

**Hours & Format**

**Summer:** 8 weeks - 10 hours of lecture per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

You can see programming courses related to statistics (or at least useful for statistics) highlighted. These courses do not require any statistics knowledge and are just programming classes.

- Stat 33A/B
- Stat 133
- CS 61A

*4 Units*

Honors version of 53. Parametric equations and polar coordinates. Vectors in 2- and 3-dimensional Euclidean spaces. Partial derivatives. Multiple integrals. Vector calculus. Theorems of Green, Gauss, and Stokes.

**Rules & Requirements**

**Prerequisites:** 1B

**Credit Restrictions:** Students will receive no credit for Mathematics H53 after completing Math 53, Math N53, or Math W53.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 3 hours of discussion per week

**Additional Details**

**Subject/Course Level:** Mathematics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*4 Units*

An introduction to probability, emphasizing concepts and applications. Conditional expectation, independence, laws of large numbers. Discrete and continuous random variables. Central limit theorem. Selected topics such as the Poisson process, Markov chains, characteristic functions.

**Rules & Requirements**

**Prerequisites:** One year of calculus

**Credit Restrictions:** Students will not receive credit for 134 after taking 140 or 201A.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 3 hours of lecture and 2 hours of discussion per week

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

**Additional Details**

**Subject/Course Level:** Statistics/Undergraduate

**Grading/Final exam status:** Letter grade. Final exam required.

*2 Units*

Introduction to the constructs in the Matlab programming language, aimed at students who already know how to program. Array and matrix operations, functions and function handles, control flow, plotting and image manipulation, cell arrays and structures, and the Symbolic Mathematics toolbox.

**Rules & Requirements**

**Prerequisites:** Programming experience equivalent to that gained in COMPSCI 10; familiarity with applications of matrix processing

**Repeat rules:** Course may be repeated for credit up to a total of 4 units.

**Hours & Format**

**Fall and/or spring:** 15 weeks - 2 hours of self-paced per week

**Additional Details**

**Subject/Course Level:** Computer Science/Undergraduate

**Grading/Final exam status:** Offered for pass/not pass grade only. Final exam required.

**Instructor:** Hilfinger

Disclaimer: The course map does not accurately reflect the classes/background you need to take a course; all and only possible prerequisites in Statistics/Data Science will be highlighted when using this course map. For final clarifications on courses, please consult with our advisors!

Special thanks to Prof. Fithian and our undergrad advisors Denise and Natalie for feedback!

Most course information scraped from from the Berkeley course catalog.

Designed and implemented by Edward Chang.