Dr. Binil Starly

Dr. Binil Starly

Industrial and Systems Engineering

Phone: 919-515-3263
Instructor Website

ISE 535 Python Programming for Industrial & Systems Engineers

3 Credit Hours

Over the last decade, Python and its ecosystem of libraries has become one of the top programming languages used by data scientists and engineers for a wide range of data and engineering applications. This course is designed for senior undergraduate and graduate students to obtain the basics of the Python language and learn to use it to perform numerical and scientific computing within Python with three of its most popular packages in use for heavy data intensive analysis – Numpy, Pandas and SciPy. Several engineering examples from physics, industrial engineering core courses and general engineering will be used to contextualize the programming examples. As part of any data analysis skills using Python, you will also learn how to collect, pre-process, store, analyze and conduct interactive visualization of data through the extensive python libraries built by the community. There are various MOOC courses on Python and self-help courses that will teach you Python provided you have the discipline to complete course elements. This course will contain a mashup of content relevant to the Industrial Engineering graduate by framing datasets and exercises common to industrial engineering applications.


Graduate Standing; Students must be familiar with a prior programming language in the past (C, Matlab or Visual Basic). Part 1 of the course will assume that you have had some programming language experience prior to coming into the course.

Course Objectives

  • Learn the fundamental data structures available in Python for use in Numerical and Scientific Computing
  • Be able to use Python’s extensive libraries for handling, cleaning, analyzing and visualizing data
  • Be able to write Python based programs using the NumPy and SciPy packages and Pandas Libraries
  • Be able to reuse and distribute your code packages to the external community using GitHub

Course Requirements

Grading Components:

Homework Coding Assignments 30%
Class Forum Participation 10%
Take Home Tests (3) 30%
Project 30%


None. The Instructor has prepared self-paced learning videos to complement the course. In addition, there are lots of free books available online. Youtube/Lynda self-help tutorials will also be assigned on the course website as necessary to complement course videos/lectures.

Computer and Software Requirements

Please review minimum computer specifications recommended by NC State University and Engineering Online.

Updated 1/5/2021