Ders İçerikleri
BAN 501 Introduction to Data Analysis and Descriptive Analytics 
This course covers the basics of data analysis in business context. First half of the course focuses on a variety of business data analysis basics, tables, graphics, descriptive statistics, select probability distributions, randomness, sampling distributions. This part wil also introduce very basics of a statistical software package “R”, which is widely used by both statisticians and data scientists. The second part of course illustrates basics of descriptive business analytics methods, data visualization, dashboard designs, statistical inferencing based on techniques such as regression. This part of courses also makes use of software tools for data visualization and dashboard design. 
Credits: 3 (3+0); ECTS: 8

BAN 502 Data Oriented Programming
This course aims at developing student skills on basics of programming and software development from data analytics point of view. Topics such as basic programming skills, basic data structures, debugging, software testing, data processing are covered. The lecture is designed with a variety of assignments and lab applications to give students a more hands-on learning opportunity. 
Credits: 3 (3+0); ECTS: 8

BAN 503 Data Warehousing 
This course covers topics such as database management systems, SQL, OLAP, ETL, Data-Mart, Operational Data Stores, SSIS, SSRS, logical and conceptual designs, query processing and optimisation. 
Credits: 3 (3+0); ECTS: 8

BAN 504 Predictive Business Analytics 
In this course students learn different classification and prediction techniques such as regression, neural networks, decision trees, support vector mechines. They also learn about unsupervised learning, association rules, model validation on one or more data mining suites.  
Credits: 3 (3+0); ECTS: 8

BAN 506 Advanced Practicum in Analytics
This course builds the bridge across “Descriptive Analytics”, “Predictive Analysis”, “Data Oriented Programming” and “Data Warehousing” by following CRISP-DM approach. The prerequisite of this course is the completion of IAN 501, IAN 502, IAN 503, and IAN 504. The course follows an “end to end” approach starting from problem description, to data definition, data access, data preprocessing, machine learning, data mining or statistical model development/selection. 
Credits: 3 (3+0); ECTS: 8

BAN 507 Marketing Analytics 
This course aims at a case-based approach on learning select applications within marketing domain. Marketing analytics can be broadly defined as “collection, processing and analysis of relevant marketing related data in order to make accurate business decisions”. This course covers application topics such as segmentation, targeting, perceptual maps, product planning, market response models, campaign design, and direct marketing. 
Credits: 3 (3+0); ECTS: 8

BAN 508 Prescriptive Business Analytics 
This course includes decision analysis and optimisation techniques. Topics such as linear programming, decision analysis, integer programming and other optimization models, simulation are covered within business analytics framework. 
Credits: 3 (3+0); ECTS: 8

BAN 509 Big Data, Tools and Technologies
This course introduces a vast area of topics and techniques such as Hadoop ecosystem’s MapReduce, and Pig. Tools that are widely used in Machine Learning such as Spark, and Azure will also be introduced.  
Credits: 3 (3+0); ECTS: 8

BAN 511 Professional Development and Practice Sharing
This course aims at sharing variety of business analytics applications and best practices, creating a sharing environment where professionals come and talk about their analytics success stories and exchange information. 
Credits: 3 (3+0); ECTS: 8

BAN 512 Project Management
This course focuses on the project management process and the management of a portfolio of projects from business analytics perspective. It introduces basics of the project management techniques to overcome the pitfalls and obstacles that can potentially occur during an analytics project. It is designed for business analytics professionals who are responsible for implementing projects.
Credits: 3 (3+0); ECTS: 8