WVU IENG 331

Spring 2026: Computer Applications in Industrial Engineering

Data Analysis
SQL
Python
Polars
Altair
Quarto
Git
Introduction to computer applications in industrial engineering: emphasis on system design and analysis and the role of computers in productivity improvement.
Author
Affiliation

Mr. Ozan Ozbeker

Industrial and Management Systems Engineering

Published

January 12, 2026

Course Description

This course teaches Industrial Engineering students how to build data products using SQL, Python, and professional development tools. Rather than treating programming as a collection of one-off scripts, the curriculum emphasizes the construction of reliable, maintainable, and reproducible data systems that mirror real-world engineering practice. Students progress from foundational computing concepts through database querying, Python programming, and ultimately to building end-to-end data products that connect databases, analyze and visualize data, and ship as polished documents or command-line tools. The course follows the open textbook Computational Workflows for Engineers (CW4E).

Course Structure

The course is organized into four units that build progressively on one another:

  1. The Professional Toolkit — Computer fundamentals, file systems, document authoring with Quarto, the command line, version control with Git, and collaboration with GitHub.
  2. Data with SQL — Databases, querying data, joining tables, analytical SQL, and data modeling.
  3. Python — Fundamentals, collections, control flow, functions and modules, working with files, objects and type hints, project structure, and code quality tooling.
  4. Building Data Products — Connecting Python to databases, DataFrames with Polars, data visualization with Altair, working with Excel, computational documents, Arrow and ADBC connectivity, and CLI tools with Typer.

Learning Objectives

Upon successful completion of this course, students will be able to:

  1. Navigate the Professional Toolkit: Use the file system, command line, and Git/GitHub fluently to manage code and collaborate on engineering work.
  2. Author Reproducible Documents: Produce polished technical reports and computational documents using Quarto that integrate prose, code, and results.
  3. Query and Model Data with SQL: Write analytical SQL — including joins, aggregations, and window functions — against relational databases, and reason about how data is modeled and stored.
  4. Program in Python: Apply Python fundamentals, collections, control flow, and functions to solve engineering problems, and structure code into well-organized projects with type hints and modern code quality tooling.
  5. Build Data Products: Connect Python to databases, transform data with Polars DataFrames, and create effective visualizations with Altair to communicate findings to technical and non-technical audiences.
  6. Integrate Familiar Tools: Read from and write to Excel programmatically, and move data efficiently between systems using Arrow and ADBC.
  7. Ship Engineering Tools: Package analyses as command-line tools with Typer so that workflows are repeatable, shareable, and usable beyond a single notebook.
  8. Engage with Real-World Data: Develop proficiency in handling diverse, real-world datasets through hands-on projects, preparing for industry or academic pursuits.

These objectives align with ABET-defined Student Outcomes, fostering critical skills in problem-solving, communication, teamwork, and lifelong learning. The emphasis on building durable data products — rather than disposable scripts — prepares students for the computational realities of modern industrial engineering practice.