# University of California, Berkeley Department of Statistics Statistics Undergraduate Major Information 2018

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2 UC BERKELEY STATISTICS MAJOR COURSE DESCRIPTIONS Students who have NOT taken prerequsites are subject to Instructor Drop. Core Statistics Courses Statistics 133: Concepts in Computing with Data (3 units). This course focuses on statistical computing for data analysis, including how to acquire, clean and organize data, analyze data using computationally intensive statistical methods, and report findings. Students gain experience in computing as a supporting skill for statistical practice and research. They learn how to use existing high-level general purpose software, to implement algorithms from scratch, to express statistical ideas and computations, and they learn about different data technologies and tools, when to use them, and their trade-offs. Students acquire skills in basic numeracy, graphics, modern computationally intensive methods, and simulation. Programming concepts include variables, data types, trees, control flow. Data technology topics include the digital representation of data, regular expressions, relational database management systems, extensible Markup Language (XML), Web services for distributed functionality and methods, and Web publication. Extensive written reports are an integral part of the course. Statistics 134: Concepts of Probability (4 units). Prerequisites: One year of calculus. This is an introduction to probability theory, aimed at students who have had at least one year of calculus. The course covers the laws of probability, expectation, conditioning, covariance and correlation, as well as all the standard distributions of discrete and continuous random variables. Functions of random variables - sums, order statistics, and so on - are studied thoroughly, as are limit laws such as the law of large numbers and the central limit theorem, and the standard models: Bernoulli trials, sampling with and without replacement, Poisson process, univariate and bivariate normal. The course serves as preparation for more systematic study of mathematical statistics and stochastic processes. Credit restriction: Students who have earned credit for Stat 140 will not receive credit for Stat 134. Statistics 135: Concepts of Statistics (4 units). Prerequisites: and Stat 134 or Stat 140. recommended. This is a comprehensive survey course in statistical theory and methodology, aimed at understanding the fundamental principles of statistical reasoning, achieving proficiency in data analysis, and developing written communications skills. Topics include descriptive statistics and data analysis, fundamental concepts of the theory of estimation and hypothesis testing, and methodology such as sampling, goodness-of-fit testing, analysis of variance, and least squares estimation. The laboratory includes computer-based analysis of data from a variety of fields and requires written reports. Statistics 140: Probability for Data Science (4 units). Prerequisites: Stat/CS/Info C8 and one year of calculus. 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. Credit restriction: Students who have earned credit for Stat 134 will not receive credit for Stat 140. Upper-division Statistics Elective Courses Statistics 150: Stochastic Processes (3 units). Prerequisites: Stat 134 or Stat 140. This course is especially recommended for students with a strong interest in probability theory or stochastic models, including models in finance, ecology, epidemiology, geophysics and other fields. Topics include random walks, discrete time Markov chains, Poisson processes, continuous time Markov chains, queueing theory, point processes, branching processes, renewal theory, stationary processes, Gaussian processes. Statistics 151A: Linear Modelling: Theory and Applications (4 units). Prerequisites:. Math 110 and recommended. This course is especially recommended for students with an interest in economics, social science, or statistical models and data analysis more generally. 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. Statistics 152: Sampling Surveys (4 units). Prerequisites: Stat 134 or Stat 140. and 135 recommended. This course is especially recommended for students with an interest in social science, marketing, and data collection more generally. Topics include 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; and error estimation for complex samples. Statistics 153: Introduction to Time Series (4 units). Prerequisites: Stat 134 or Stat 140 (or consent of instructor). or 135 recommended. This course is especially recommended for students with an interest in physical science, communication and information theory, economics, finance, or actuarial work. An introduction to time series analysis in the time domain and spectral domain. Topics include estimation of trends and seasonal effects, autoregressive moving average models, forecasting, indicators, harmonic analysis, spectra. Statistics 154: Modern Statistical Prediction and Machine Learning (4 units). Prerequisites: and or equivalents; or equivalent; experience with some programming language. Math 55 (or equivalent exposure to counting arguments) and Math 110 recommended but not required. 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. Statistics 155: Game Theory (3 units). Prerequisites: Stat 134 or Stat 140. This course is especially recommended for students with an interest in mathematics, optimization or strategy, including business decisions. General theory of zero-sum, two-person games, including games in extensive form and continuous games, and illustrated by detailed study of examples. Statistics 157: Seminar on Topics in Probability and Statistics (3 units). Prerequisites: and, Stat 134 or Stat 140, and consent of instructor. The topic varies considerably from semester to semester. Check with instructor to determine if you have the appropriate foundational knowledge of the topic discussed in that semester. Some Stat 157 courses will require Stat 134 or Stat 140 and 135. Some may have additional prerequisites. Topics that have been taught in recent years include, Bayesian Statistics; Probability in the Real World; Statistics and Finance; High-Dimensional Phenomena and Regularization in Statistics; Topics in Stochastic Processes; and Computational Biology and Statistics. Statistics 158: The Design and Analysis of Experiments (4 units). Prerequisites: Stat 134 or Stat 140 and (or consent of instructor). may be taken concurrently. recommended. 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. Stat 159: Reproducible and Collaborative Statistical Data Science (4 units). Prerequisites:, Stat 134 or Stat 140, and (or equivalent). 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. Course emphasizes version control, testing, process automation, code review, and collaborative programming. Software tools may include Bash, Git, Python, and LaTeX. Lower division and graduate course descriptions can be found in the online Berkeley Academic Guide: guide.berkeley.edu/courses.

4 Sample Program Plans for the Statistics Major Sample Statistics Major 4-YEAR Program Plan (no prerequisites completed) YR 2 YR 1 Math 1A Cluster prerequisite, if applicable Math 1B Intro Stat course such as Stat C8, Stat 20 or W21 (select Stat C8 if planning to take Stat 140) Stat 134 or Stat 140 (lab) Sample Statistics Major 4-YEAR Program Plan (Math 1A & 1B waived due to AP credit) YR 1 Stat C8 (if planning to take Stat 140) Cluster prerequisite course, if applicable YR 2 Stat 134 or Stat 140 (lab) Sample Statistics Major 2-YEAR Program Plan for Transfer Students who have completed all math prerequisites at a non-uc or Math 110 Stat 134 Alternative major course (lab) Sample Statistics Major 2-YEAR Program Plan for Transfer Students who have completed only a year of calculus (Math 1A, 1B) Alternative major course Stat 134 (non-lab) (lab) The sample Program Plans above only include courses required for the Statistics major. A full-time course load in the College of Letters & Science is 13 units, so you will have space each semester to round out your schedule with courses that can fulfill other requirements or simply satisfy your intellectual curiosity. You will need to account for all university and college degree requirements, including but not limited to: Reading & Composition, 7 Breadth, minimum 120 units, etc. See degree requirements on the College of Letters & Science website: Still have questions? Meet with the Statistics Undergraduate Faculty or Staff Advisor to develop a Program Plan that is both feasible and meets your needs. Consider studying abroad or engaging in undergraduate research.

6 For ADVISOR use only Cluster Course Change #1 Cluster Course Change #2 Cluster Course Change #3 Cluster Course Change #4 DATE: ADVISING NOTES: DATE: ADVISING NOTES: Return completed application to Undergraduate Advising Staff by scheduling an appointment online (see below) OR come during Drop-in Hours: Denise Yee Appointments: Majabeen Samadi Appointments: Statistics Department, University of California Undergraduate Advising 367 Evans Hall, Mail Code #3860 Berkeley, CA

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