Program’s structure & philosophy

Several factors have been taken into consideration to structure the content of the program:

• existing programs on business analytics and data science in business schools and engineering/CS departments at well known universities worldwide,
• proposals and suggestions of members of the advisory committee of the program, consisting of university professors and industry experts in the US and Asia,
• evaluations, comments and feedback of the participants in AUEB’s seminars and specialization programs on Big Data and Business Analytics during the last two years.

In brief, the program covers in detail theoretical concepts on business, statistics and data management, while it recognizes the importance of practical training on systems and tools. In addition, special care has been given to the “breadth requirement”: exposure on analytics applications in different fields and domains. The result is a well-balanced program between theory and practice. Theoretical concepts account for 50% of the program, systems and tools account for 25% of the program and the “breadth requirement” accounts for another 25% of the program. Theoretical concepts fall in four broad thematic areas:

• Business Environment and Processes (Information Systems & Business Process Management, Innovation and Entrepreneurship, Business and Privacy Issues in Data Analysis)
• Statistics (Statistics for Business Analytics I, Statistics for Business Analytics II, Advanced Topics in Statistics)
• Data Management (Data Management & Business Intelligence, Big Data Systems, Advanced Topics in Data Engineering)
• Optimization and Knowledge Discovery (Large-scale Optimization, Mining Big Datasets, Social Network Analysis)

Practical training on system and tools involve the following platforms: SAS, R, Hadoop and related projects, Spark, MongoDB, Redis, Neo4j, Python (tentatively).

Finally, case studies will be presented in the context of finance, marketing, health, energy, human resources, transportation, supply chain analytics.

Part-time program

The part time program consists of two years of coursework, followed by a semester-long thesis or capstone project. Details on the courses can be found here.

Fall Quarter, Year 1

• Data Management and Business Intelligence
• Statistics for Business Analytics I

Winter Quarter, Year 1

• Big Data Systems
• Statistics for Business Analytics II
• Analytics Practicum I (1st part – Python for Analytics)

Spring Quarter, Year 1

• Mining Big Datasets
• Social Network Analysis (short course)
• Machine Learning and Content Analytics (short course)

Fall Quarter, Year 2

• Large Scale Optimization
• Information Systems & Business Process Management
• Certificate in SAS Programming & Data Mining (info)

Winter Quarter, Year 2

• Business and Privacy Issues in Data Analysis
• Analytics Practicum I (2nd part – SAS tools)
• Innovation and Entrepreneurship (short course)

Spring Quarter, Year 2

• Analytics Practicum II (Visualization, Cloud Analytics)
• Business Analytics Use Cases
• Advanced Topics in Statistics or Advanced Topics in Data Engineering

Capstone project/Thesis