CIS 3120 – Sample course syllabus

Title Programming for Analytics
Description This course introduces the aspects of programming that can support business analytics. The course introduces students to programming (using a language such as python) and its uses in business analytics. The course covers hands-on issues in programming for analytics which includes accessing data, creating informative data graphics, writing functions, debugging, and organizing and commenting code.
Prerequisites CIS 2200
Learning Goals At the completion of the course, students will be able to:

  • Demonstrate literacy in practical data science (including use of Python programming language) in enterprises.
  • Identify challenges and opportunities that pertain to programming for Data Analytics.
  • Develop programs in a suitable programming language (example – python) that help in data analytics.
Bi-weekly individual programming assignments 30%
Quizzes 10%
Midterm Exams 40%
Group Programming Project 20%

Introduction to Python programming language.

Printing, Building user Interface.

Reading and Writing Files

Functions and Variables

Introduction: What Is Data Science?

Implementing Logic and making Decisions

Exploratory Data Analysis and the Data Science Process.

Spam Filters, Naive Bayes, and Wrangling.

Loops and Lists

Branches and Functions

Time Stamps and Financial Modeling.

Designing and Debugging


Extracting Meaning from Data.

Recommendation Engines: Building a User-Facing Data Product at Scale.

Learning to speak Object Oriented

Data Visualization and Fraud Detection

Continue learning to speak Object Oriented

Social Networks and Data Journalism.

Causality – Correlation Doesn’t Imply Causation.


Lessons Learned from Data Competitions: Data Leakage and Model Evaluation.