Hey everyone,
My college does not offer a data science degree but I am looking forward to taking some statistics courses that will be the most relevant for a career in it. How closely related are some of these courses connected with data science?
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Data analysis:
This course focuses on choosing, fitting, assessing and using statistical models. Simple linear regression, multiple regression, analysis of variance, general linear models, logistic regression and discrete data analysis will provide the foundation for the course. Classical interference methods that rely on the normality of the error terms will be thoroughly discussed -
Nonparametric Statistics:
This course will focus on nonparametric and distribution-free statistical procedures. These procedures will rely heavily on counting and ranking techniques. In the one and two sample settings, the sign, signed-rank and Mann-Whitney-Wilcoxon procedures will be discussed. Correlation and one-way analysis of variance techniques also will be investigated. -
Linear Regression Models
Simple linear regression with one predictor variable will serve as the starting point. Models, inferences, diagnostics and remedial measures for dealing with invalid assumptions will be examined. The matrix approach to simple linear regression will be presented and used to develop more general multiple regression models. Building and evaluating models for real data will be the ultimate goal -
Statistical Computing in R
The primary goal of the course is to learn and apply Monte-Carlo simulation techniques to a wide variety of problems. We will focus on solving problems from a numerical point of view, with methods to complete numerical integration, root finding, curve fitting, variance reduction and optimization. Core knowledge of R and basic programming concepts will be introduced.
These are just some specialized courses, and besides them, I’ll have to meet the requirements for linear algebra, elements of stats, calculus series, and some intro cs classes.
Will greatly appreciate any input/advice. Thanks!