Data Analytics

Abstract

This Course takes Business Practitioners on a journey of Business Analytics: using data to derive insights, make predictions, and decide on plans of action that can be communicated and actualized in a Business context.

“Business analytics, or simply analytics, is the use of data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models to help managers gain improved insight about their business operations and make better, fact-based decisions. Business analytics is”a process of transforming data into actions through analysis and insights in the context of organizational decision making and problem solving.”

-Libertore and Luo, 2010

The Course starts with Descriptive Analytics: Datasets from various domains of Business enterprise and activity are introduced. The datasets are motivated from the point of view of the types of information they contain: students will relate the Data Variables (Qualitative and Quantitative) to various types of Data/Information Visualizations.

Statistical Concepts such as Sampling, Hypothesis Tests, Simulation / Modelling, and Uncertainty will be introduced.

Predictive Analytics will take us into looking at Data and training standard ML algorithms to make predictions with new Data. Regression, Clustering, and Classification will be covered.

Prescriptive Analytics will deal with coming to terms with the uncertainty in Predictions, and using tools such as both ML, Linear/non-Linear Programming, and Decision-Making to make Business Decisions, with an assessment of the Risks involved.

The Course will culminate in a full Business Analytics Workflow that includes Data Gathering and Cleaning, Descriptive and Predictive Analytics, Prescriptive Analytics and Decision Making, and Communication resulting in a publication-worthy documents.(HTML / PDF/ Word)

What you will learn

• Data Basics: What does data look like and why should we care?
• Rapidly and intuitively creating Graphs and Data Visualizations to explore data for insights
• Use Statistical Tests, Procedures, Models, and Simulations and to answer Business Questions
• Using ML algorithms such Regression, Classification, and Clustering to develop Business Insights
• Use Linear Programming to make Business Decisions
• Create crisp and readable Reports that can be shared in a Business Context

Texts

1. James R Evans, Business Analytics: Methods, Models, and Decisions, Pearson Education, 2021.

References

1. Robert Kabacoff. Modern Data Visualization with R. https://rkabacoff.github.io/datavis/. Available free Online.

2. Jack Dougherty and Ilya Ilyankou, Hands-On Data Visualization: Interactive Storytelling from Spreadsheets to Code, https://handsondataviz.org/. Available free Online.

3. Claus O. Wilke, Fundamentals of Data Visualization, https://clauswilke.com/dataviz/. Available free Online.

4. Jonathan Schwabish, Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks, Columbia University Press, 2021.

5. Alberto Cairo, The Functional Art:An introduction to information graphics and visualization, New Riders. 2013. ISBN-9780133041361.

6. Cole Nussbaumer Knaflic, Storytelling With Data: A Data Visualization Guide for Business Professionals, Wiley 2015. ISBN-9781119002253.

7. Judd, C.M., McClelland, G.H., & Ryan, C.S. (2017). Data Analysis: A Model Comparison Approach To Regression, ANOVA, and Beyond, Third Edition (3rd ed.). Routledge. https://doi.org/10.4324/9781315744131

8. Thomas Maydon, The 4 Types of Data Analytics, https://www.kdnuggets.com/2017/07/4-types-data-analytics.html

9. Dimitris Bertsimas, Robert Freund, Data, Models, and Decisions: the Fundamentals of Management Science, Dynamic Ideas Press, 2004.

10. Cliff T. Ragsdale, Spreadsheet Modeling & Decision Analysis: A Practical Introduction to Management Science, South Western, Cengage Learning, Mason, OH, 2012.

11. Keith McNulty. Handbook of Regression Modeling in People Analytics: With Examples in R, Python and Julia https://peopleanalytics-regression-book.org

Pedagogical Notes

Our Tools

This is eventually meant to be a three-in-one course, during which we will gain exposure to the following free and open source tools:

1. R https://cran.r-project.org/ and RStudio https://posit.co/

   R is a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc. RStudio is an integrated development environment (IDE) for R and Python.

2. Orange Data Mining https://orangedatamining.com/

   Orange is a FOSS visual point-and-click software for Data Mining and ML, developed at the University of Slovenia, Ljubljana.

2. Radiant – Business analytics using R and Shiny https://radiant-rstats.github.io/docs/index.html

Radiant is a FOSS platform-independent browser-based interface for business analytics in R, developed at the University of San Diego. The application is based on the Shiny package and can be run using R, or in your browser with no installation required. The tool automatically installs a version of R and adds a Shiny-based GUI that removes the need to write R-code. Radiant can also be installed on top of an existing installation of R and invoked from within RStudio.

---

Modules