Machine Learning, Python, & R | 4 Credits

Analyze and visualize business application data using statistical and visualization tools of R.

Competencies:

1. Extract business data from databases using tools such as SQL and utilize R for statistical tests such as t-Tests, ANOVA, linear regression, multiple regression, logistic regression, principal component Analysis, and time series analysis.
2. Apply R for analyzing and visualizing business data using packages such as ggplot, ggplot2, tidyverse, and related packages; generate web pages for presentation through R markdown code.

Textbook: Tilman M. Davies, The Book of R: A First Course in Programming and Statistics 1st Edition, ISBN-13: 978-1593276515, ISBN-10: 1593276516

Prerequisites

• BAMA 300x Business Analytics I

Apply R for machine learning and AI applications involving business data.

Competencies:

1. Apply R for AI/ML applications such as k-nearest neighbor classification, tree-based classification, neural networks, deep learning, cluster analysis, text mining, sentiment analysis, word frequency and n-grams.

Textbook: Tilman M. Davies, The Book of R: A First Course in Programming and Statistics 1st Edition, ISBN-13: 978-1593276515, ISBN-10: 1593276516

Prerequisites

• BABA 302X R Fundamentals for Business Analytics

Analyze business application data using statistical tools of python.

Competencies:

1. Apply Python for analyzing business data using statistical tests such as t-Tests, ANOVA, linear regression, multiple regression, logistic regression, principal component Analysis, and time series analysis; generate web pages for presentation through R markdown code.
2. Conduct advanced statistical and AI/machine learning techniques using Python for business data; techniques to include k-nearest neighbor classification, tree-based classification, neural networks, deep learning, cluster analysis, text mining, sentiment analysis.

Prerequisites

• BAMA 300x Business Analytics I