MIS 4470/5470 - Practical Computing for Data Analytics (Fall 2024)

This course is part of our graduate MS in Business Analytics program as well as for our undergraduate Minor in Business Analytics. Both graduate and undergrad students from a variety of programs find their way to this course: MBA, Master of Accounting, MS in Business Analytics, MS in IT Management, MS in Applied Statistics, undergraduates in School of Business, Actuarial Scence, and the School of Engineering and Computer Science. The diverse mix of students make this a very enjoyable class to teach and take.

Most students take my spreadsheet based analytics course, MIS 4460/5460 before doing this course. Highly recommended but not strictly required.

• My OU website: http://www.sba.oakland.edu/faculty/isken/

• My Github site: https://github.com/misken/

• My Bits of Analytics blog: https://bitsofanalytics.org/

Course outline:

• Course logistics and resources
  • Textbooks
  • Software and pcda virtual machine
  • Resource Center
• Getting started with pcda and Linux (Weeks 1-2)
  • Intro to PCDA course
  • Intro to the bash shell
• Intro to data science with R (Weeks 3-8)
  • Intro to R and R Studio
  • EDA with R
  • EDA with R
  • Version control with git and GitHub
  • Group by analysis and data wrangling with R
  • Group by analysis and data wrangling with R (UNDER CONSTRUCTION)
  • Modeling 1: Overview and linear regression in R
  • Modeling 1: Overview and linear regression in R with tidymodels (UNDER CONSTRUCTION)
  • Modeling 2: Intro to classifiers with R
  • Modeling 2: Intro to classifiers with R(UNDER CONSTRUCTION)
• Intro to data science with Python (Weeks 8-13)
  • Intro to Python for data analytics
  • Functions, text files, reading data files with Python
  • Data analysis and plotting in Python
  • Text wrangling with Python
  • Modeling 3: Intro to machine learning with scikit-learn
  • Automated web data gathering (OPTIONAL)
• More topics (teaching Advanced Analytics with Python course in summer 2024)

• Search Page