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|STAT 35500 - Statistics For Data Science|
Credit Hours: 3.00. An introduction to methodologies for data analysis and simulation. Populations and sampling. Distributions and summaries of distributions. Algorithms for sampling and resampling. Foundational statistical concepts including confidence intervals, hypothesis testing, correlation. Introduction to classification and regression. Essential use is made of statistical software throughout. Typically offered Fall Spring.
0.000 OR 3.000 Credit hours
Levels: Graduate, Professional, Undergraduate
Schedule Types: Distance Learning, Laboratory, Lecture
Offered By: College of Science
May be offered at any of the following campuses:
Learning Outcomes: 1. Utilize simulations to generate data (statistical simulations). 2. Explain sampling distributions, their properties, and methods for resampling (Statistical Reasoning). 3. Explain foundational concepts such as confidence intervals, hypothesis testing, correlations, etc., and apply these concepts in statistical process (Statistical Concepts). 4. Assess statistical problems, and justify and apply statistical methods used to solve the problem (Statistical Thinking).