|Introduction to Python Programming
||This course will introduce you to the basics of programming in Python, on either Windows or Mac. You will use both Jupyter notebooks and standard script editors, and work through simple arithmetic operations, statistical operations, variables, keywords, lists, arrays, and dictionaries. You’ll use Conda to install modules and close with some data visualizations.
|Introduction to R Programming
||This course provides an easy introduction to programming in R for those who have little or no programming experience. Topics include understanding file formats, basic R syntax, and how to use text editors to write code. You will learn to read in files, use symbols and assignments, and iterate simple loops, and the course closes with a discussion of data structures (including vectors and data frames) and subsetting.
|Introductory Statistics for College Credit (Statistics 1 & 2)
||This course is designed to teach sometimes tricky statistical concepts in an easy-to-understand business and real-world context. It relies on the innovative text “Introductory Statistics and Analytics: A Resampling Perspective” and intentionally references the growing field of Data Science. The course is divided into two four-week sections, Part 1 – Probability and Study Design, and Part 2 – Inference and Association.
|Matrix Algebra Review
||Statistics deals with collections of data organized in 1,2,3 or more dimensions. Matrix notation is the best way to compactly represent such data. This course provides the basics of vector and matrix algebra and operations necessary to understand multivariate statistical methods including the matrix inverse, generalized inverse and eigenvalues and eigenvectors.
|R Programming - Introduction 2
||In this course you will continue your introduction to R programming. You will learn how R works with numeric vectors, and how to deal with special values. You will start working with R to handle text data, and learn about regular expressions, dates, classes, and generic functions, as well as matrices, data frames, and lists. Some prior programming experience and some familiarity with an installed R system is required.