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Why Business Analytics?

The program offers a variety of modeling and analysis tools in Business Analytics, as well as their application in the business environment.

  • Demonstrated understanding of data-driven decision modeling and analysis concepts/frameworks
  • Knowledge of and hands-on experience with fundamentals of business analytics, management information systems, statistical and prediction models
  • Ability to transform complex data into valuable insight and resulting value-adding actions
  • Skills in hands-on data-mining tools and techniques
  • Exposure to the analytical methods in basic business disciplines such as marketing, operations, and finance

Professionals who graduate from the engineering, business, economics or similar programs who are currently working in various departments in their organizations and who want to advance their careers in their fields can apply.

Graduates of this program are equipped to take the role of data/analytics translator, analytics professional, or business analyst on the way to become top level professionals in a variety of data-rich industries including:

  • Financial and banking sector
  • Retail Telecommunication
  • Insurance
  • Airline Industry
  • Manufacturing
  • Health

Altunizade Campus

Days of Education: Tuesday - Thursday 19:00 - 22:00 and Saturday 09:30 - 16:30

It is a part-time program consisting of 5 modules, each lasting 7 weeks. The language of education is English.


Module 1: 

BAN 827 Descriptive Analytics

BAN 835 Computational Tools and IT for Analytics

Module 2: 

BAN 805 Predictive Analytics

BAN 831 Data Warehousing & Business Intelligence


Module 3: 

BAN 801 Marketing Analytics

BAN 803 Operations Analytics

Module 4: 

BAN 807 Financial Analytics (Elective)

BAN 821 Optimization & Simulation

Module 5:

BAN 892 Applied Advanced Analytics

BAN 809 Project Management in Analytics (Elective)

BAN 801 Marketing Analytics

This course is about generating marketing insights from empirical data in such areas as segmentation, targeting and positioning, satisfaction management, customer lifetime analysis, customer choice, and product and price decisions using conjoint analysis. This will be a hands-on course based on the Marketing Engineering approach and Excel software

BAN 803 Operations Analytics

This course introduces analytical methods for various operational, tactical, and strategic decisions in operations management function of the firms. Topics covered in detail are forecasting techniques, planning under deterministic and uncertain demand, operations planning and scheduling, queuing theory, service operations management, capacity and revenue management, and supply chain management

BAN 805 Predictive Analytics *

This course introduces basic concepts and models of supervised and unsupervised statistical learning models. The topics include, multiple regression, logistic regression, classfication, resampling methods, subset selection, the ridge, the lasso, tree-based methods, support vector machines, principal component analysis, and clustering.

BAN 807 Financial Analytics (Elective)

An introduction to methods and tools useful in decision-making in the financial industry, including: macroeconomic event studies, analysis of term structures, Morningstar equity data, style analysis, credit card receivables, trading analytics, execution algorithms, etc. This course blends easy-to-use statistical tools with complex machine learning tools and algorithms to equip the participants with the requisite skill set in analyzing data. 

BAN 809 Project Management for Analytics (Elective)

This course introduces students to the theory and practice of project management. This course examines the management of complex projects and the tools are available to assist managers with such projects. Some of the specific topics we will discuss include project life cycle models, work break down structure, organization break down structure, cost break down structure, graphical presentations and precedence diagramming, network analysis and scheduling techniques, concepts of system life cycle costing, and cost estimation methods and trade-off analysis, risk management, and monitoring and control. 

BAN 821 Optimization and Simulation

This course introduces the basic principles and techniques of mathematical modeling that will aid managerial decisions. Students will learn how to develop analytical models and use techniques such as linear and mixed integer programming, Monte Carlo simulation, discrete-event simulation and decision trees. The applications are on models that are widely used in diverse business functional areas.

BAN 827 Descriptive Analytics *

This course aims to provide a review of methods for statistical inference, and develop an understanding of how these tools can be applied in a variety of business problems. The emphasis of this course would be on applications, through practical examples and cases. A variety of statistical software will be introduced. Topics covered include descriptive statistics, probability distributions, hypothesis testing, regression, design of experiments and analysis of variance.

BAN 831 Data Warehousing and Business Intelligence

This course introduces the basics of structured data modeling, gain practical SQL coding experience, and develop an in-depth understanding of data warehouse design and data manipulation. It also allows working with large data sets in a data warehouse environment to create dashboards and introduces a variety of business intelligence solutions.

BAN 835 Computational Tools and IT for Analytics

This course explores both the functional and technical environment for the creation, storage, and use of the most prevalent source and type of data for business analysis. Students will learn how to access and leverage information via SQL for analysis, aggregation to visualization, MapReduce, Apache Spark and Graph databases.  This course will also give an introduction to a set of tools and techniques for dealing with large data such as Python and R. 

BAN 892 Applied Advanced Analytics

This is a hands-on course to equip students with ways to prepare a culminating project that follows a multifaceted approach in business analytics. The course employs an end-to-end approach by following CRISP-DM (Cross-Industry Standard Process for Data Mining) throughout the module. The course also recapitulates earlier courses in the program and dives into further intricacies of descriptive, predictive and prescriptive analytics.

PBAN 2020-2021 Academic Calendar
 Orientation Day: 3 October 2020
 Fall TermSpring Term
Start6 October 20202 February 2021
End16 January 202131 July 2021
Exam Days24-28 November 202123-27 March 2021
 19-23 January 2021

1-5 June 2021 3-7 August 2021

Semester Break    24 January-1 February 2021 
Graduation Ceremony 16 October 2021
Official Holidays29 October 2020 Republic Day 31 January 2020 New Year1 May 2021 Labour Day 11-15 May 2021 Religious Holiday 15 July 2021 Democracy & National Unity Day 20-24 July 2021 Religious Holiday


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