INTRO TO STATISTICAL REASONING

A friendly introduction to statistical concepts and reasoning with emphasis on developing statistical intuition rather than on mathematical rigor. Topics include design of experiments, descriptive statistics, correlation and regression, probability, chance variability, sampling, chance models, and tests of significance.

• TR 10:10 a.m.-11:25 a.m.
• MW 6:10 p.m.-7:25 p.m.
• Instructor: Tian Zheng
  CULPA: 13 Wikipedia h-index: 25 Info
• Instructor: Anthony Donoghue
  CULPA: 17 Info
• 3 points

INTRO TO STATISTICAL REASONING - RECITAT

RECITATION - INTRO STAT R

This is only a request to create zero credit recitation for STAT1001UN Intro to Stat Reasoning starting in Fall 2026.

• F 2:10 p.m.-3:25 p.m.
• F 10:10 a.m.-11:25 a.m.
• W 4:40 p.m.-5:55 p.m.
• Instructor: Anthony Donoghue
  CULPA: 17 Info
• 0 points

UNDERGRADUATE SEM/STATISTICS

This is a seminar in probability and statistics taught by faculty in the Department of
Statistics and with guests from other Departments.
The format is that of a weekly Department seminar, but the presentations are geared for
undergraduates.
Meetings are once per week and open to all undergraduates, but undergraduates who
register and attend regularly earn one point of credit and a notation on their transcript.

• F 1:10 p.m.-2:25 p.m.
• Instructor: Daniel Rabinowitz
  CULPA: 19 h-index: 34 Info
• 1 points

INTRODUCTION TO STATISTICS

Prerequisites: intermediate high school algebra. Designed for students in fields that emphasize quantitative methods. Graphical and numerical summaries, probability, theory of sampling distributions, linear regression, analysis of variance, confidence intervals and hypothesis testing. Quantitative reasoning and data analysis. Practical experience with statistical software. Illustrations are taken from a variety of fields. Data-collection/analysis project with emphasis on study designs is part of the coursework requirement.

• MW 10:10 a.m.-11:25 a.m.
• MW 2:40 p.m.-3:55 p.m.
• MW 4:10 p.m.-5:25 p.m.
• Instructor: Alex Pijyan
  Info
• 3 points

RECITATION FOR INTRODUCTION TO STATISTIC

RECITATION FOR INTRO TO S

RECITATION FOR STAT1101UN INTRODUCTION TO STATISTICS

• M 6:10 p.m.-7:25 p.m.
• F 10:10 a.m.-11:25 a.m.
• F 11:40 a.m.-12:55 p.m.
• W 4:10 p.m.-5:25 p.m.
• T 4:10 p.m.-5:25 p.m.
• R 4:10 p.m.-5:25 p.m.
• T 12:10 p.m.-1:25 p.m.
• R 11:40 a.m.-12:30 p.m.
• F 1:10 p.m.-2:25 p.m.
• Instructor: Alex Pijyan
  Info
• 0 points

CALC-BASED INTRO TO STATISTICS

Prerequisites: one semester of calculus. Designed for students who desire a strong grounding in statistical concepts with a greater degree of mathematical rigor than in STAT W1111. Random variables, probability distributions, pdf, cdf, mean, variance, correlation, conditional distribution, conditional mean and conditional variance, law of iterated expectations, normal, chi-square, F and t distributions, law of large numbers, central limit theorem, parameter estimation, unbiasedness, consistency, efficiency, hypothesis testing, p-value, confidence intervals, maximum likelihood estimation. Serves as the pre-requisite for ECON W3412.

• TR 10:10 a.m.-11:25 a.m.
• TR 6:10 p.m.-7:25 p.m.
• MW 1:10 p.m.-2:25 p.m.
• 3 points

RECITATION FOR CALC-BASED INTRODUCTION T

RECITATION CALC-BAS INTRO

RECITATION FOR STAT1201UN CALC-BASED INTRODUCTION TO STATISTICS

• F 10:10 a.m.-11:25 a.m.
• F 12:10 p.m.-1:25 p.m.
• F 2:10 p.m.-3:25 p.m.
• F 10:10 a.m.-11:25 a.m.
• F 2:10 p.m.-3:25 p.m.
• S 10:10 a.m.-11:25 a.m.
• S 2:10 p.m.-3:25 p.m.
• R 6:10 p.m.-7:25 p.m.
• R 12:10 p.m.-1:25 p.m.
• F 12:10 p.m.-1:25 p.m.
• F 2:10 p.m.-3:25 p.m.
• 0 points

RECITATION FOR APPLIED STATISTICAL COMPU

RECITATION FOR APPL STAT

RECITATION FOR STAT2102UN APPLIED STATISTICAL COMPUTING

• F 10:10 a.m.-11:25 a.m.
• F 2:10 p.m.-3:25 p.m.
• 0 points

Applied Statistical Computing

Corequisites: An introductory course in statistic (STAT UN1101 is recommended). This course is an introduction to R programming. After learning basic programming component, such as defining variables and vectors, and learning different data structures in R, students will, via project-based assignments, study more advanced topics, such as conditionals, modular programming, and data visualization. Students will also learn the fundamental concepts in computational complexity, and will practice writing reports based on their data analyses.

• MW 11:40 a.m.-12:55 p.m.
• 3 points

APPLIED LINEAR REG ANALYSIS

Prerequisites: An introductory course in statistics (STAT UN1101 is recommended). Students without programming experience in R might find STAT UN2102 very helpful. Develops critical thinking and data analysis skills for regression analysis in science and policy settings. Simple and multiple linear regression, non-linear and logistic models, random-effects models. Implementation in a statistical package. Emphasis on real-world examples and on planning, proposing, implementing, and reporting.

• MW 1:10 p.m.-2:25 p.m.
• Instructor: Casey Bradshaw
  Info
• 3 points

APPL CATEGORICAL DATA ANALYSIS

Prerequisites: STAT UN2103 is strongly recommended. Students without programming experience in R might find STAT UN2102 very helpful. This course covers statistical models amd methods for analyzing and drawing inferences for problems involving categofical data. The goals are familiarity and understanding of a substantial and integrated body of statistical methods that are used for such problems, experience in anlyzing data using these methods, and profficiency in communicating the results of such methods, and the ability to critically evaluate the use of such methods. Topics include binomial proportions, two-way and three-way contingency tables, logistic regression, log-linear models for large multi-way contingency tables, graphical methods. The statistical package R will be used.

• TR 11:40 a.m.-12:55 p.m.
• Instructor: Casey Bradshaw
  Info
• 3 points

RECITATION FOR APPLIED DEEP LEARN AND AI

REC - APPLIED DEEP LEARNI

RECITATION FOR STAT3108 APPLIED DEEP LEARNING AND AI

• F 10:10 a.m.-11:25 a.m.
• F 2:10 p.m.-3:25 p.m.
• 0 points

APPLIED MACHINE LEARNING

Prerequisites: STAT UN2103. Students without programming experience in R might find STAT UN2102 very helpful. This course is a machine learning class from an application perspective. We will cover topics including data-based prediction, classification, specific classification methods (such as logistic regression and random forests), and basics of neural networks. Programming in homeworks will require R.

• MW 10:10 a.m.-11:25 a.m.
• Instructor: Michael Weylandt Jr.
  Info
• 3 points

Undergraduate Mentored Research

Prerequisites: the project mentors permission. This course provides a mechanism for students who undertake research with a faculty member from the Department of Statistics to receive academic credit. Students seeking research opportunities should be proactive and entrepreneurial: identify congenial faculty whose research is appealing, let them know of your interest and your background and skills.

• Instructor: Arian Maleki
  h-index: 31 Info
• Instructor: Tian Zheng
  CULPA: 13 Wikipedia h-index: 25 Info
• 3 points

APPLIED DEEP LEARNING AND AI

This course provides a non-mathematical introduction to the principles and architectures of deep learning and generative AI models. Designed for undergraduates in the Applied Data Science minor, the curriculum covers the mathematical foundations of neural networks and their application to spatial, temporal, and multimodal data. Students will examine the mechanics of convolutional and recurrent architectures, the self-attention mechanism in Transformers, and the training objectives of Large Language Models (LLMs). The course also addresses optimization strategies, reinforcement learning for model alignment, and generative paradigms, including diffusion and autoregressive models. Emphasis is placed on understanding model internal representations, architectural tradeoffs, and the evaluation of complex AI systems.

• TR 6:10 p.m.-7:25 p.m.
• 3 points

INTRODUCTION TO PROBABILITY AND STATISTI

INTRODUCTION TO PROB & ST

Prerequisites: Calculus through multiple integration and infinite sums. A calculus-based tour of the fundamentals of probability theory and statistical inference. Probability models, random variables, useful distributions, conditioning, expectations, law of large numbers, central limit theorem, point and confidence interval estimation, hypothesis tests, linear regression. This course replaces SIEO 4150.

• MW 6:10 p.m.-7:25 p.m.
• Instructor: Banu Baydil
  CULPA: 10 Info
• 3 points

RECITATION FOR PROBABILITY THEORY

REC FOR PROBABILITY THEOR

RECITATION FOR STAT4203GU PROBABILITY THEORY

• R 6:10 p.m.-7:25 p.m.
• Instructor: Arian Maleki
  h-index: 31 Info
• 0 points

RECITATION FOR PROBABILITY THEORY

RECITATION FOR PROB THEOR

RECITATION FOR STAT4203GU PROBABILITY THEORY

• F 2:10 p.m.-3:25 p.m.
• F 1:10 p.m.-2:25 p.m.
• F 12:10 p.m.-1:15 p.m.
• R 2:10 p.m.-3:25 p.m.
• Instructor: Arian Maleki
  h-index: 31 Info
• Instructor: Richard Davis
  CULPA: 3 h-index: 60 Info
• Instructor: Banu Baydil
  CULPA: 10 Info
• Instructor: Richard Davis
  CULPA: 3 h-index: 60 Info
• 0 points

RECITATION FOR STATISTICAL INFERENCE

REC FOR STATISTICAL INFER

RECITATION FOR STAT4204GU STATISTICAL INFERENCE

• F 10:10 a.m.-11:25 a.m.
• F 2:10 p.m.-3:25 p.m.
• Instructor: Bodhisattva Sen
  h-index: 24 Info
• 0 points

RECITATION FOR LINEAR REGRESSION MODELS

RECITATION FOR STAT4205GU LINEAR REGRESSION MODELS

• R 6:10 p.m.-7:25 p.m.
• F 1:10 p.m.-2:25 p.m.
• Instructor: Gabriel Young
  CULPA: 7 Info
• 0 points

RECITATION FOR PROGRAMMING FOR DATA SCIE

RECITATION FOR PROGRAM FO

RECITATION FOR STAT4206GU PROGRAMMING FOR DATA SCIENCE

• F 9:40 a.m.-10:55 a.m.
• F 2:40 p.m.-3:55 p.m.
• F 10:40 a.m.-11:55 a.m.
• F 11:40 a.m.-12:55 p.m.
• 0 points

PROBABILITY THEORY

Prerequisites: At least one semester, and preferably two, of calculus. An introductory course (STAT UN1201, preferably) is strongly recommended. A calculus-based introduction to probability theory. A quick review of multivariate calculus is provided. Topics covered include random variables, conditional probability, expectation, independence, Bayes’ rule, important distributions, joint distributions, moment generating functions, central limit theorem, laws of large numbers and Markov’s inequality.

• TR 4:10 p.m.-5:25 p.m.
• MW 11:40 a.m.-12:55 p.m.
• TR 6:10 p.m.-7:25 p.m.
• Instructor: Arian Maleki
  h-index: 31 Info
• Instructor: Richard Davis
  CULPA: 3 h-index: 60 Info
• Instructor: Banu Baydil
  CULPA: 10 Info
• 3 points

STATISTICAL INFERENCE

Prerequisites: STAT GU4203. At least one semester of calculus is required; two or three semesters are strongly recommended. Calculus-based introduction to the theory of statistics. Useful distributions, law of large numbers and central limit theorem, point estimation, hypothesis testing, confidence intervals maximum likelihood, likelihood ratio tests, nonparametric procedures, theory of least squares and analysis of variance.

• MW 2:40 p.m.-3:55 p.m.
• Instructor: Bodhisattva Sen
  h-index: 24 Info
• 3 points

LINEAR REGRESSION MODELS

Prerequisites: STAT GU4204 or the equivalent, and a course in linear algebra. Theory and practice of regression analysis. Simple and multiple regression, testing, estimation, prediction, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. Extensive use of the computer to analyse data.

• MW 4:10 p.m.-5:25 p.m.
• MW 6:10 p.m.-7:25 p.m.
• Instructor: Gabriel Young
  CULPA: 7 Info
• 3 points

PROGRAMMING FOR DATA SCIENCE

Prerequisites: STAT GU4204 and GU4205 or the equivalent. Introduction to programming in the R statistical package: functions, objects, data structures, flow control, input and output, debugging, logical design, and abstraction. Writing code for numerical and graphical statistical analyses. Writing maintainable code and testing, stochastic simulations, paralleizing data analyses, and working with large data sets. Examples from data science will be used for demonstration.

• MW 7:40 p.m.-8:55 p.m.
• TR 8:40 a.m.-9:55 a.m.
• Instructor: Haiyuan Wang
  Info
• Instructor: Benjamin K Goodrich
  Info
• 3 points

ELEMENTARY STOCHASTIC PROCESS

Elementary Stochastic Processe

Prerequisites: STAT GU4203 and two, preferably three, semesters of calculus. Review of elements of probability theory. Poisson processes. Renewal theory. Walds equation. Introduction to discrete and continuous time Markov chains. Applications to queueing theory, inventory models, branching processes.

• MW 1:10 p.m.-2:25 p.m.
• Instructor: Regina Dolgoarshinnykh
  CULPA: 2 Info
• 3 points

TIME SERIES ANALYSIS

Prerequisites: STAT GU4205 or the equivalent. Least squares smoothing and prediction, linear systems, Fourier analysis, and spectral estimation. Impulse response and transfer function. Fourier series, the fast Fourier transform, autocorrelation function, and spectral density. Univariate Box-Jenkins modeling and forecasting. Emphasis on applications. Examples from the physical sciences, social sciences, and business. Computing is an integral part of the course.

• TR 2:40 p.m.-3:55 p.m.
• Instructor: Rongning Wu
  CULPA: 1 Info
• 3 points

Causal Inference

This course is an introduction to Causal Inference at the masters and advanced undergraduate
level. Students will be introduced to a broad range of causal inference methods including randomized
experiments, observational studies, instrumental variables, di?erence-in-di?erences,
regression discontinuity design, and synthetic controls. In addition, the course will cover modern,
controversial debates regarding the foundations and limitations of causal inference.

The primary learning goal of this course will be to familiarize students with a variety of the
most popular causal inference methods: which causal e?ects they seek to estimate, basic assumptions
required for identi?cation and estimation, and their practical implementation. To
this end, the course will focus both on developing the pre-requisite statistical / methodological
theory and as well as gaining hands-on experience through implementation exercises with
real datasets. By the end of the course, students should have deep familiarity of various causal
inference methods and—more importantly—be able to determine which method is most appropriate
for a given applied problem and to judge whether the pre-requisite identifying conditions
are appropriate.

• W 2:40 p.m.-5:25 p.m.
• Instructor: Christopher Harshaw
  Info
• 3 points

STATISTICAL MACHINE LEARNING

Prerequisites: STAT GU4206. The course will provide an introduction to Machine Learning and its core models and algorithms. The aim of the course is to provide students of statistics with detailed knowledge of how Machine Learning methods work and how statistical models can be brought to bear in computer systems - not only to analyze large data sets, but to let computers perform tasks that traditional methods of computer science are unable to address. Examples range from speech recognition and text analysis through bioinformatics and medical diagnosis. This course provides a first introduction to the statistical methods and mathematical concepts which make such technologies possible.

• MW 10:10 a.m.-11:25 a.m.
• Instructor: Genevera Allen
  Info
• 3 points

ADVANCED MACHINE LEARNING

This course covers various topics in advanced machine learning. Topics may include optimization algorithms, Python libraries for ML, principles for applied supervised and unsupervised learning, hyperparameter selection, computational trade-offs, modern neural network architectures such as ConvNets, LSTMs, and transformers for computer vision and natural language processing, and deep learning for LLMs.

• R 6:10 p.m.-8:55 p.m.
• W 6:20 p.m.-9:05 p.m.
• Instructor: Parijat Dube
  Info
• Instructor: Kamiar Rahnama Rad
  Info
• 3 points

ADVANCED DATA SCIENCE AND ARTIFICIAL INT

AI: ALGORITHMS TO APPS

Project-based topics course in data science and artificial intelligence. Students build a portfolio by implementing and applying modern data science methods.

• TR 4:10 p.m.-5:25 p.m.
• Instructor: Bianca Dumitrascu
  Info
• 3 points

Unsupervised Learning

Description.
Unsupervised Learning is a masters level course on foundations, methods, practice, and applications in machine learning from data without associated labels or outcomes.  This course will focus on dimension reduction and clustering techniques while also covering graphical models, missing data imputation, anomaly detection, generative models, and others. The course will also emphasize conceptual understanding and practical applications of unsupervised learning in data visualization, exploratory data analysis, data pre-processing, and data-driven discovery.

• TR 8:40 a.m.-9:55 a.m.
• Instructor: Yoonhaeng Hur
  Info
• 3 points

STATISTICAL METHODS IN FINANCE

Prerequisites: STAT GU4205 or the equivalent. A fast-paced introduction to statistical methods used in quantitative finance. Financial applications and statistical methodologies are intertwined in all lectures. Topics include regression analysis and applications to the Capital Asset Pricing Model and multifactor pricing models, principal components and multivariate analysis, smoothing techniques and estimation of yield curves statistical methods for financial time series, value at risk, term structure models and fixed income research, and estimation and modeling of volatilities. Hands-on experience with financial data.

• F 10:10 a.m.-12:40 p.m.
• Instructor: Hammou El Barmi
  CULPA: 8 Info
• 3 points

STAT INF/TIME-SERIES MODELLING

Prerequisites: STAT GU4204 or the equivalent. STAT GU4205 is recommended. Modeling and inference for random processes, from natural sciences to finance and economics. ARMA, ARCH, GARCH and nonlinear models, parameter estimation, prediction and filtering. This is a core course in the MS program in mathematical finance.

• S 10:10 a.m.-12:40 p.m.
• Instructor: Franz Rembart
  Info
• 3 points

STOCHASTC PROCSSES-APPLICTNS I

Prerequisites: STAT GU4203. STAT GU4207 is recommended. Basics of continuous-time stochastic processes. Wiener processes. Stochastic integrals. Ito's formula, stochastic calculus. Stochastic exponentials and Girsanov's theorem. Gaussian processes. Stochastic differential equations. Additional topics as time permits.

• MW 1:10 p.m.-2:25 p.m.
• MW 4:10 p.m.-5:25 p.m.
• Instructor: Steven Campbell
  Info
• 3 points

STOCHASTIC METHODS IN FINANCE

Stochastics Methods in Finance

Prerequisites: STAT GU4264. Mathematical theory and probabilistic tools for modeling and analyzing security markets are developed. Pricing options in complete and incomplete markets, equivalent martingale measures, utility maximization, term structure of interest rates. This is a core course in the MS program in mathematical finance.

• MW 4:10 p.m.-5:25 p.m.
• Instructor: Graeme Baker
  Info
• 3 points

ADVANCED DATA ANALYSIS

Prerequisites: STAT GU4205 and at least one statistics course numbered between GU4221 and GU4261. This is a course on getting the most out of data. The emphasis will be on hands-on experience, involving case studies with real data and using common statistical packages. The course covers, at a very high level, exploratory data analysis, model formulation, goodness of fit testing, and other standard and non-standard statistical procedures, including linear regression, analysis of variance, nonlinear regression, generalized linear models, survival analysis, time series analysis, and modern regression methods. Students will be expected to propose a data set of their choice for use as case study material.

• F 5:10 p.m.-7:40 p.m.
• Instructor: Demissie Alemayehu
  h-index: 14 Info
• 3 points

TOPICS IN MODERN STATISTICS

ML ENGINEERING FOR STATS

Topics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.

• M 6:10 p.m.-8:40 p.m.
• Instructor: Iordan I Slavov
  Info
• 3 points

TOPICS IN MODERN STATISTICS

STAT ANALYSIS-NEURAL DATA

Topics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.

• R 1:10 p.m.-3 p.m.
• Instructor: Liam M Paninski
  CULPA: 4 Info
• 3 points

TOPICS IN MODERN STATISTICS

INFO THEORY FOR DS & AI

Topics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.

• M 4:10 p.m.-6:40 p.m.
• Instructor: Ori Shental
  Info
• 3 points

TOPICS IN MODERN STATISTICS

ONLINE EXPERIMENTS

Topics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.

• W 6:10 p.m.-8:40 p.m.
• Instructor: Lei Kang
  Info
• 3 points

TOPICS IN MODERN STATISTICS

STATS IN DRUG DEVELOPMENT

Topics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.

• MW 6:10 p.m.-7:25 p.m.
• Instructor: Paula C Abreu
  Info
• 3 points

RECITATION FOR PROBABILITY

RECITATION FOR STAT5203GR PROBABILITY

• F 12:10 p.m.-1:15 p.m.
• Instructor: Banu Baydil
  CULPA: 10 Info
• 0 points

RECITATION FOR LINEAR REGRESSION MODELS

RECITATION LINEAR REG MOD

RECITATION FOR STAT5205GR LINEAR REGRESSION MODELS

• F 10:10 a.m.-11:25 a.m.
• F 11:40 a.m.-12:55 p.m.
• R 6:10 p.m.-7:25 p.m.
• Instructor: Gabriel Young
  CULPA: 7 Info
• 0 points

RECITATION FOR PROGRAMMING FOR DATA SCIE

RECITATION FOR PROGAM FOR

RECITATION FOR STAT5206 PROGRAMMING FOR DATA SCIENCE

• F 9:40 a.m.-10:55 a.m.
• F 10:40 a.m.-11:55 a.m.
• F 11:40 a.m.-12:55 p.m.
• 0 points

RECITATION FOR PROGRAMMING FOR DATA SCIE

Prog for Data Sci Recitat

RECITATION FOR STAT5206 PROGRAMMING FOR DATA SCIENCE

• F 2:40 p.m.-3:55 p.m.
• 0 points

PROBABILITY

Prerequisites: At least one semester of calculus. A calculus-based introduction to probability theory. Topics covered include random variables, conditional probability, expectation, independence, Bayes rule, important distributions, joint distributions, moment generating functions, central limit theorem, laws of large numbers and Markovs inequality.

• TR 6:10 p.m.-7:25 p.m.
• MW 8:40 a.m.-11:25 a.m.
• TR 1:10 p.m.-3:40 p.m.
• Instructor: Banu Baydil
  CULPA: 10 Info
• Instructor: Regina Dolgoarshinnykh
  CULPA: 2 Info
• Instructor: Alberto Gonzalez Sanz
  Info
• Instructor: Ruchira Ray
  Info
• 3 points

STATISTICAL INFERENCE

Prerequisites: STAT GR5203 or the equivalent, and two semesters of calculus. Calculus-based introduction to the theory of statistics. Useful distributions, law of large numbers and central limit theorem, point estimation, hypothesis testing, confidence intervals, maximum likelihood, likelihood ratio tests, nonparametric procedures, theory of least squares and analysis of variance.

• MW 8:40 a.m.-11:25 a.m.
• TR 1:10 p.m.-3:40 p.m.
• Instructor: Regina Dolgoarshinnykh
  CULPA: 2 Info
• Instructor: Victor H de la Pena
  h-index: 20 Info
• 3 points

LINEAR REGRESSION MODELS

Prerequisites: STAT GR5203 and GR5204 or the equivalent. Theory and practice of regression analysis, Simple and multiple regression, including testing, estimation, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. Extensive use of the computer to analyse data.

• MW 6:10 p.m.-7:25 p.m.
• Instructor: Gabriel Young
  CULPA: 7 Info
• 3 points

PROGRAMMING FOR DATA SCIENCE

PROGRAMMING FOR DS

Course Overview: 
This course introduces Python programming, covering data structures, control-flow, objects, and functions, along with libraries like re, requests, numpy, pandas, scikit-learn, scipy, and more. These skills are applied to real-world data science tasks, including AB testing, data manipulation, modeling, optimization, simulations, and data visualization.

Students will develop computational thinking abilities, including problem decomposition, pattern recognition, data representation, abstraction, and algorithm design, through practical exercises.

• MW 7:40 p.m.-8:55 p.m.
• Instructor: Haiyuan Wang
  Info
• 3 points