What is Business Analytics?
Business Analytics aims to develop business data-driven information using applied analytical disciplines. Utilizing various disciplines, Business Analytics is both the art and the science of achieving information from data. Business Analytics directly affects how we compile information and make business decisions.
Why ŞEHİR?
• Highly educated staff with international competence and industry experience
• Rather than a disciplinary approach, Business Analytics is taught within an interdisciplinary framework
• Integrated curriculum designed specifically for the program
• Evening program
• English education
Objectives and Primary Features of the Program
Mission: To train end-to-end competent analytics experts, wherever data is available
• Applied analysis and Problem Solving Techniques on real data sets and problems
• Data-driven decision making through various business scenarios
• Practical training through the most popular software in the industry
• End-to-end business analytics project: Manipulation, Managing and Completing
• Problem-oriented applications with a totalitarian perspective of different methods
• All courses in the program are designed to be integrated and transdisciplinary.
Target Audience
• Graduates of Business Administration, Engineering or similar fields; Those who aim to pursue a career in business analytics; Those who work in the relevant departments of their companies.
• The courses will be held on weekdays between 19:00-22:00 on Altunizade Campus and between 09:00-16:00 on Dragos Campus.
• The instruction medium is English.
Program Design
• İstanbul Şehir University Master of Business Analytics Program aims to empower participants’ knowledge with the most modern software and tools without going through the theoretical details of database and data analysis techniques.
• In İstanbul Şehir University Master of Business Analytics Program, the data coming from databases are analyzed and converted into business decisions by using various data analysis methods and software tools.
• Some of the skills to be developed in the program include:
- Communication and presentation techniques
- Descriptive, Predictive and Rational Data Analysis on R, SPSS Modeler, KNIME, Hadoop (or) Azure, MSProject, Python, SQL, OracleDB, SSIS, SSAS, NoSQL, Excel, and Tableau
Practicum
• All courses are designed to be practical and interactive.
• Specifically, in BAN 506 - Advanced Applied Business Analytics Methods and BAN 511- Professional Development and Experience Sharing courses, professionals and Academicians share best-known practices in the sector with students. Moreover, the content of other courses in the program has been specially designed to be applied in real life business problems.
CURRICULUM – Non-Thesis
FIRST SEMESTER
|
| Code | Course | Requirement | Theory | Hours | Credits | ECTS |
| -- | 5 BAN 5XX Optional Courses
|
| -- | -- | 15 | 40 |
| | Total | | | | 15 | 40 |
SECOND SEMESTER
|
| Code | Course | Requirement | Theory | Hours | Credits | ECTS |
| -- | 5 BAN 5XX Optional Courses | | -- | -- | 15 | 40 |
| | Total | | | | 15 | 40 |
THIRD SEMESTER
|
Code
| Course | Requirement | Theory | Hours | Credits | ECTS |
| BAN 591 | Final Project | | -- | -- | 0 | 30 |
| | Total | | | | 0 | 30
|
CURRICULUM – Thesis
FIRST SEMESTER
|
| Code | Course | Requirement | Theory | Hours | Credits | ECTS |
| MGT 590 | Seminar | | 3 | 0 | 0 | 4 |
| -- | 3 Program Optional Courses | | -- | -- | 9 | 24 |
| | Total | | | | 9 | 28 |
| SECOND SEMESTER |
| Code | Course | Requirement | Theory | Hours | Credits | ECTS |
| MGT 501 | Research Methods | | 3 | 0 | 3 | 8 |
| -- | 3 Program Optional Courses | | -- | -- | 9 | 24 |
| | Total | | | | 12 | 32 |
| THIRD SEMESTER |
| Code | Course | Requirement | Theory | Hours | Credits | ECTS |
| BAN 599 | Thesis | | 0 | 0 | 0 | 60 |
| | Total | | | | 0 | 60 |
FOURTH SEMESTER
|
| Code | Course | Requirement | Theory | Hours | Credits | ECTS |
| BAN 599 | Thesis | | 0 | 0 | 0 | 60 |
| Total | | | | 0 | 60
|
COURSE CONTENTS
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