BZAN 6351

Programming for Business Analytics

Advanced Digitization Trends

Course Overview

BZAN 6351 introduces students to the essential programming skills required for modern business analytics. The course focuses on technology enablement, data manipulation, and visualization techniques using industry-relevant tools such as R, Python, and Spotfire. Students will gain hands-on experience writing R scripts and using programming to solve real-world business problems, preparing them for roles in analytics, supply chain, and digital transformation.

Course Purpose

This course is designed to build foundational programming capabilities for students pursuing careers in analytics-driven environments. Through a structured progression from basic to intermediate R programming, students will learn to manage data, create compelling visualizations, and perform statistical analysis. The course also introduces emerging tools and trends, including Python, Spotfire, and the role of programming in digitized supply chains, artificial intelligence, and Generative AI (GenAI).

Students will develop the ability to think critically, solve problems creatively, and communicate insights effectively — skills that are essential for navigating today’s technology-enabled business landscape.

Photo for BZAN 6351 Basic Programming for Business Analytics

Learning Objectives

By the end of this course, students will be able to:

  • Use RStudio to write, debug, and execute R scripts for business analytics tasks.
  • Perform data manipulation and transformation using packages such as dplyr, tidyverse, and magrittr.
  • Create data visualizations using ggplot2 and communicate insights through R Markdown.
  • Understand the fundamentals of Python and Spotfire and their applications in business analytics.
  • Apply programming techniques to support decision-making, performance analytics, and cost optimization.
  • Develop professional skills in problem solving, self-directed learning, and technology tool fluency.
  • Explore the role of programming in enabling resilient, antifragile, and digitally integrated supply chains.

Topics Covered

  • Introduction to R and RStudio
  • Data Structures and Data Frames
  • Data Manipulation with dplyr and tidyverse
  • Data Visualization with ggplot2
  • Statistical Analysis and Comparison Tests
  • Correlation, Simple and Multiple Regression
  • Introduction to Python and Spotfire
  • Programming for Business Decision-Making
  • Reporting with R Markdown
  • Programming Careers in Analytics and Emerging Technologies (e.g., GenAI)

Experiential Learning & Course Pedagogy

The course is delivered in an asynchronous online format with a strong emphasis on hands-on learning. Students complete structured programming assignments through DataCamp, progressing from basic syntax to applied analytics.

Key experiential components include:

  • A capstone programming project simulating a real business scenario.
  • Interactive coding exercises and visualizations.
  • Exposure to emerging trends in analytics programming and AI.
  • Opportunities to explore technology tools used in industry for data-driven decision-making.

Students are encouraged to collaborate in discussions but must complete all coding assignments independently to ensure mastery of programming skills.

Course Format & Assessment

The course is fully online and self-paced, with instructor support through virtual office hours and exam reviews. Assessment includes:

  • A series of graded homework assignments completed via DataCamp.
  • Three non-cumulative exams testing conceptual and applied programming knowledge.
  • A final project applying R programming to a simulated business analytics challenge.
  • Participation in online discussions and peer learning activities.