Stat Inference & Modeling · Statistics
- Theory on many statistical models such as generalized regression, different estimators, missing data, multivariate gaussians etc.
I learned how to build statistical models with R to perform statistical inference
Got opportunities to learn 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.
The class taught me a much more statistical perspective on machine learning
A broad range of statistical modelling topics: hypothesis testing, Bayesian inference, time series analysis, regression etc...
I received a wide exposition to many topics in statistical inference and modelling theory; in addition, there were many example applications in R. Topics included maximum likelihood parameter estimation, hypothesis testing, likelihood ratio tests, regression, Markov chains, MCMC methods, Bayesian inference and time series.
I learned many statistical models and how to apply them to real world questions. This course focuses on many problems that we have not considered in previous STAT courses, and I learned how to solve these problems without making non-realistic assumptions.
Learned important Statistical Modeling Techniques which are widely used in the FinTech industries.
The statistics courses for ds are all below expectations and I want my money back. The professor only copies things from his pad and reads it out. His handwriting is hard to recognize and his voice becomes lower and lower as he writes. It is even hard to learn by myself because of the terrible handwriting and he did not specify the materials for each lecture.
This course is friendly to those who lacks statistic background and is willing to do some data science works. 1 Columbia University: Arts & Sciences Spring 2023 Course: STATGR5703_002_2023_1-STATINFERENCE&MODELING : STATGR5703_002_2023_1 - STAT INFERENCE & MODELING Instructor: Franz Rembart