Practical Computing for Data Analytics

The modules correspond to the weeks in our course Moodle site (with a few twists). Roughly the course is broken up into three sections.

Section 1: Linux and the bash shell

The first two modules set the tone and build a foundation for the rest of the course.

• Week 1: Intro to pcda - overview of business analytics and a preview of things to come
• Week 2: Linux and the bash shell - modeling principles and spreadsheet engineering fundamentals

Section 2: R for data analytics

R is a statistical programming language that has a huge ecosystem for doing data analysis and statistical modeling. The R programming that we’ll do will mostly be sequences of R commands.

• Week 3: Intro to R and R Studio - what is R? what is R Studio? How do we start to use them?
• Week 4: EDA with R - basic descriptive statistics and learning to create data visualizations using ggplot2
• Week 5: Group by analysis and data wrangling with R - use the tidyverse libraries to get comfortable cleaning, reshaping and analyzing data
• Week 6: Intro to regression modeling with R - learn the basics of predictive modeling using regression

Section 3: Python for data analytics

Python is the most widely used language for data science. This part of the course will be more programmatic in nature.

• Week 7/8: Intro to Python for data analytics - programming and Python fundamentals
• Week 8/9: More introductory Python - more on data stuctures, functions and creating more involved programs
• Week 10: Text wrangling and regular expressions - use regular expressions from within Python programs to search and clean data files
• Week 11: EDA with Python - use pandas and matplotlib (and Seaborn)
• Week 12: Classifiers with Python and R - use scikit-learn and tidymodels to build classification models
• Week 13: Unsupervised models with Python and R - use R and Python for PCA and cluster analysis

After this class, if you want to learn more about data science and predictive modeling with Python, you could take MIS 4480/5480 - Advanced Analytics with Python.