3 courses with the subject STAT GR, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
STAT GR5701PROBABILITY ＆ STAT FOR DATA SC3.00
This course covers the following topics: Fundamentals of probability theory and statistical inference used in data science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference; point and confidence interval estimation, hypothesis tests, linear regression
- Subject
- STAT GR
- Credits (min)
- 3
- Credits (max)
- 3
- Credit unit
- points
- Type
- course
- Edition
- 2026
- Source
- bulletin.columbia.edu
STAT GR5702EXPLORATORY DATA ANALYSIS/VISUAL3.00
This course is covers the following topics: fundamentals of data visualization, layered grammer of graphics, perception of discrete and continuous variables, intreoduction to Mondran, mosaic pots, parallel coordinate plots, introduction to ggobi, linked pots, brushing, dynamic graphics, model visualization, clustering and classification
- Subject
- STAT GR
- Credits (min)
- 3
- Credits (max)
- 3
- Credit unit
- points
- Type
- course
- Edition
- 2026
- Source
- bulletin.columbia.edu
STAT GR5703STAT INFERENCE ＆ MODELING3.00
Prerequisites: (STAT GR5701) working knowledge of calculus and linear algebra (vectors and matrices), and STAT GR5203 or the equivalent. Prerequisites: (STAT GR5701) working knowledge of calculus and linear algebra (vectors and matrices), STAT GR5701 or equivalent, and familiarity with a programming language (e.g. R, Python) for statistical data analysis. In this course, we will systematically cover fundamentals of statistical inference and modeling, with special attention to models and methods that address practical data issues. The course will be focused on inference and modeling approaches such as the EM algorithm, MCMC methods and Bayesian modeling, linear regression models, generalized linear regression models, nonparametric regressions, and statistical computing. In addition, the course will provide introduction to statistical methods and modeling that addresses various practical issues such as design of experiments, analysis of time-dependent data, missing values, etc. Throughpout the course, real-data examples will be used in lecture discussion and homework problems. This course lays the statistical foundation for inference and modeling using data, preparing the MS in Data Science students, for other courses in machine learning, data mining and visualization
- Subject
- STAT GR
- Credits (min)
- 3
- Credits (max)
- 3
- Credit unit
- points
- Type
- course
- Edition
- 2026
- Source
- bulletin.columbia.edu
Source: Teachers College at Columbia University's catalog, linked per course · table learning_unit · CourseShelf publish 61
