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Course module: 30K217-B-6
30K217-B-6
Digitization & Big Data Analytics
Course info
Course module30K217-B-6
Credits (ECTS)6
CategoryBA (Bachelor)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Economics and Management; TISEM Other;
Is part of
B Economics and Business Economics
Lecturer(s)
Lecturer
dr. S. Angelopoulos
Other course modules lecturer
Academic year2020
Starting block
SM 2
Course mode
Full-time
Remarks-
Registration openfrom 19/01/2021 up to and including 20/08/2021
Aims
The main aim of this course is to provide the participants with a clear understanding of the possibilities that Big Data provide to our society, but also, present their shortcomings and limitations. By the end of this course, the participants will be familiar with the terminology that surround Big Data, and competent to use the most popular platforms. Moreover, a series of guest lectures related to Big Data will help the participants to understand how the concept is used in practice, what technological approaches are used, and how such applications are advancing our society. The objective of this course is not to teach participants how to programme or how to use specific analytical methods, since this is part of other courses.
The course focuses on advanced cases and applications to understand complex problems in broader areas that surround the management of Big Data. A large part of the course is dedicated on how to use high performance computing infrastructures and learn about their possibilities and limitations. By the end of the course, the participants will be able to:
  • Give an account of how high-performance computing methods are applied to solve complex problems, improve decisions, and add value to institutions and individuals
  • Define and differentiate amongst the components of high-performance computing infrastructures, used both for analytics, and for developing advanced applications
  • Make sense of the ethical implications surrounding Big Data Analytics, and be able to outline solutions that take into consideration users’ rights, privacy, and security.
Content
In the networked context we live in, millions of transactions and behaviors are being recorded daily by a large number of networked sensors and devices. Such data are related to ourselves, our friends, our preferences, and our location and can include intimate information, such as our sleeping or eating habits. In addition to this, every day internet users from around the world collectively send more than 45 billion emails, submit more than 95 million tweets, and generate 2.5 quintillion bytes of data, and this is only set to grow. As the data accumulates, the management and strategic leverage of such information resources becomes a critical success factor in creating competitive advantage. This ability to harness vast collections of data is expected to give rise to new opportunities for economic and societal value creation. It has already given rise to a total of 4.4 million jobs globally to support Big Data Analytics, whilst the technology and services it spurs are expected to project growth rates at about seven times the value of the overall information and communication market. Real world problems, however, are usually complex and often ill-defined, since they do not come with labels, and the only objective reality is data, which in itself may be incomplete and of questionable quality, making Data Science and Big Data Analytics the profession of the future.
Course available for exchange students
Conditions of admission apply
Contact person
dr. S. Angelopoulos
Timetable information
Digitization & Big Data Analytics
Required materials
-
Recommended materials
-
Tests
Final grade

Laboratories (10%)

Term Paper (50%)

Presentation (40%)

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Kies de Nederlandse taal