University of Information Technology

Data Science for Business

Course Description

This course is intended to introduce a collection of the most important fundamental concepts of data science, and machine learning. The concepts span the process from envisioning the problem, to applying data science techniques, to deploying the results to improve decision-making. The concepts also undergird a large array of business analytics methods and techniques.

Intended Learning Outcomes

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

  • recognize the fundamental concept of machine learning, data science, Business Analytics and Data Analytics and machine learning tasks.
  • recognize the components of data science tasks.
  • visualize  how to solve the business problem by data science.
  • recognize the work flow of prediction models, data fitting into the models and avoidance of overfitting data.
  • apply the appropriate data science concepts and methods for solving business problem.
  • illustrate the appropriate curves (ROC,AUC, Profit Curves) for performance analysis of model.
  • recognize the privacy and ethic of data science.
  • observe the future trends and success of data science.

Text and References Books


  1. Kelleher, John D., and Brendan Tierney. “Data science”. MIT Press, 2018.
  2. Provost, F., & Fawcett, T. (2013). “Data Science for Business: What you need to know about data mining and data-analytic thinking”, O’Reilly Media, Inc.


  1. Evans, James R. “Business Analytics”, Global Edition. Pearson, 2016.
  2. Insights, Synthesizing Actionable, and Ervin Varga. “Practical Data Science with Python 3.”,2019, Apress Media LLC
  3. Cao L. Data Science Thinking. In Data Science Thinking 2018. Springer, Cham

Assessment system

Evaluation Marks Percentage
Tutorial 10 Marks 10%
Attendance 10 Marks 10%
Assignments/Presentation 20 Marks 20%
Final Examination 60 Marks 60%