fmegahed/isa401

This repo contains my code, lecture notes, etc for an undergraduate business intelligence and data visualization course.

ISA 401: An Undergrad Course on Business Intelligence & Data Visualization

This repo contains my lecture notes (created in R Markdown) for an undergraduate business intelligence and data visualization course. The course is divided into three main phases:

• Phase 1: Extracting, Transforming and Loading Data -- I chose to utilize the R programming language as the software tool of choice for this phase. Other instructors at Miami use Power BI (a Microsoft software tool for this purpose).
• Phase 2: Data Visualization, which covers both basic principles behind data visualization and how to make charts. Many of our students are expected to learn Tableau for their jobs and hence, I mix Tableau and Power BI (to mantain consistency with other sections taught at my school) and introduce some neat charts from R along the way due to my belief that scripted languages provide a good understanding of the grammar of making good graphics.
• Phase 3: A Very Short Introduction to Exploratory/Visual Data Mining Tools.

Schedule

The raw .Rmds for all presentations can be found under the folder titled lectures. The schedule below has links to the associated GitHub Pages where the generated HTML slides are hosted. The schedule below is for the Spring 2024 semester.

Week	Date	Topic	Slides (Hosted Page)	Slides (PDF)	Slides (PPTX)
01	01/30	Introduction to Data Viz and BI	01_Introduction.html	01_Introduction.pdf	01_Introduction.pptx
	02/01	Introduction to R	02_introduction_to_r.html	02_introduction_to_r.pdf	02_introduction_to_r.pptx
02	02/06	Data Import and Export in R	03_data_import_export.html	03_data_import_export.pdf	03_data_import_export.pptx
	02/08	Scraping Web Pages in R	04_scraping_webpages.html	04_scraping_webpages.pdf	04_scraping_webpages.pptx
03	02/13	Scraping Web Pages in R (Cont.)	04_scraping_webpages.html	04_scraping_webpages.pdf	04_scraping_webpages.pptx
	02/15	Scraping Multiple Web Pages in R	06_scraping_multiple_webpages.html	06_scraping_multiple_webpages.pdf	06_scraping_multiple_webpages.pptx
04	02/20	Web Scraping Review	--	--	--
	02/22	Intro to Web APIs	08_intro_to_apis.html	08_intro_to_apis.pdf	08_intro_to_apis.pptx
05	02/27	Tidy Data in R	09_tidy_data.html	09_tidy_data.pdf	09_tidy_data.pptx
	02/29	Technically Correct & Consistent Data	10_technically_correct_and_consistent_data.html	10_technically_correct_and_consistent_data.pdf	10_technically_correct_and_consistent_data.pptx
06	03/05	Technically Correct & Consistent Data (Cont.)	10_technically_correct_and_consistent_data.html	10_technically_correct_and_consistent_data.pdf	10_technically_correct_and_consistent_data.pptx
	03/07	Data Correction and Imputation	12_data_correction_and_imputation.html	12_data_correction_and_imputation.pdf	12_data_correction_and_imputation.pptx
07	03/12	Exam 01: ETL	--	--	--
	03/14	Fundamentals of Data Visualization	14_fundamentals_data_viz.html	14_fundamentals_data_viz.pdf
14_fundamentals_data_viz.pptx
08	03/19	Fundamentals of Data Visualization (Cont.)	--	--
--
	03/21	Hands-On Lab: Tableau and PowerBI Intro	--	--
--
09	04/02	Commonly Used Charts	17_commonly_used_charts.html	17_commonly_used_charts.pdf	17_commonly_used_charts.pptx
	04/04	Time-Series Charts	18_ts_charts.html	18_ts_charts.pdf	18_ts_charts.pptx
10	04/09	Commonly Used Charts	19_spatial_and_spatiotemporal_charts.html	19_spatial_and_spatiotemporal_charts.pdf	19_spatial_and_spatiotemporal_charts.pptx
	04/11	Charts for High Dimensional Data	20_high_dimensional_charts.html	20_high_dimensional_charts.pdf	20_high_dimensional_charts.pptx
11	04/16	Business Reports	21_business_reports.html	21_business_reports.pdf	21_business_reports.pptx
	04/16	Exam 02: Data Viz	--	--	--
12	04/23	Data Mining Overview	23_data_mining_overview.html	23_data_mining_overview.pdf	23_data_mining_overview.pptx
	04/25	Data Mining Overview (Cont.)	23_data_mining_overview.html	23_data_mining_overview.pdf	23_data_mining_overview.pptx
13	04/30	A Brief Intro to Clustering	25_clustering_intro.html	25_clustering_intro.pdf	25_clustering_intro.pptx

Acknowledgments

• Phase I has benefited heavily from the following references:
  • The excellent STATS 220 Data Technologies course by @earowang.
  • The manuscript An Introduction to Data Cleaning with R.
  • Hadley Wickham's excellent books (R For Data Science and Advanced R).
• Phase III capitalizes on the third edition of Mining of Massive Datasets.

Old Versions of the Course

• The Fall 2023 version of the course can be accessed by clicking on the tag titled fall2023.

• The Fall 2022 version of the course can be accessed by clicking on the tag titled fall2022.

• The Spring 2022 version of the course can be accessed by clicking on the tag titled spring2022.

Notes

• I did my best to add references whenever possible. If I missed a reference, please let me know (via a pull request, PR).
• If you have identified mistakes/typos, please let me know. Teaching a course in the midst of a pandemic, with your university's decision to increase the number of course preps, is interesting (to say the least).