Information on individual educational components (ECTS-Course descriptions) per semester | |
Degree programme: | Bachelor International Business Administration Part-time |
Type of degree: | FH BachelorĀ“s Degree Programme |
Part-time | |
Summer Semester 2023 | |
Course unit title | Big Data |
Course unit code | 025007045004 |
Language of instruction | English |
Type of course unit (compulsory, optional) | Elective |
Semester when the course unit is delivered | Summer Semester 2023 |
Teaching hours per week | 1 |
Year of study | 2023 |
Level of course unit (e.g. first, second or third cycle) | First Cycle (Bachelor) |
Number of ECTS credits allocated | 2 |
Name of lecturer(s) | Eric KYPER |
Prerequisites and co-requisites |
Business students in their second or third year of studies |
Course content |
This course covers the rise of big data and the role it plays in industry and society. Topics will include data mining, the data advantage, backup needs, security, and societal issues related to large scale data collection and analysis. We will primarily focus on managerial implications of trends in corporate computing. |
Learning outcomes |
Understand the primary goals of Big Data in industry Identify general process of data mining Identify common threats to corporate security Identify common business continuity and backup plans to deal with loss of data Conduct a cost/benefit analysis of big data systems |
Planned learning activities and teaching methods |
(a) Articles and other materials will be assigned for reading prior to class (b) Lectures, group discussions and case studies will be used in class (c) Articles will be assigned during class for use in the final exam |
Assessment methods and criteria |
Team work report |
Comment |
Not applicable |
Recommended or required reading |
Pre-Course readings: Bhasin, M. L. (2006). Data Mining: A Competitive Tool in the Banking and Retail Industries. The Chartered Accountant, 588-594. Bialik, C. (2013, March 1). Data Crunchers Now the Cools Kids on Campus. The Wall Street Journal. Seifert, J. W. (2008). Data Mining and Homeland Security: An Overview. CRS Report for Congress. Stein, J. (2011, March 10). Data Mining: How Companies Now Know Everything About You. TIME. |
Mode of delivery (face-to-face, distance learning) |
Face-to-face |
Summer Semester 2023 | go Top |