The Faculty of Business and Social Sciences  Print
The Academic Board of Business Administration
 
Big data as a means for improved decision making
Big data as a means for improved decision makingTeaching activity id: 9269701.
Teaching language: English.ECTS / weighting: 10 ECTS / 0.167 full-time equivalent.
Exam activity id: 9269702.
Examination language: English.Approved: 07-03-16.
Period: Autumn 2016.
Grading: Internal grading.
Assessment: 7-point scale.
Offered in: Odense.
 

Subject director:
Associate Professor Rene Chester Goduscheit.
 
Prerequisites:
Familiarity with statistical analysis and regression model analysis.
 
Purpose:
Big data analytics has emerged as the driving force behind critical business decisions and generally its role is growing within the characterization and understanding of individuals and of firms’ behaviour. Advances in our ability to collect, store, and process different kinds of data from traditionally unconnected and unstructured sources enables us to answer massively complex, data-driven questions in ways that have never been possible before.

The main purpose of this course is to prepare students to make sense of real-world phenomena and everyday activities by synthesizing and mining big data by uncovering relevant patterns, relationships, and trends with the intention of making better informed decisions.

The course will provide the student with knowledge about the central methods related to generating, analysing and processing big data. The students will have the skills to apply these methods to particular, empirical problems. And the course will give the students the competence to predict and evaluate expedient practices in related to a wide range of big data related problems as for instance:
  • Businesses can predict future sales results by combining their customers’ preference profiles with website click-stream data, social network interactions, and location data.
  • Police and fire departments collaborate with emergency managers to develop more accurate models of automotive and pedestrian traffic by using GPS data from cars, buses, taxis, and mobile phones.
  • Emergency room physicians are able to reduce time to initial treatment and, as a result, patient mortality, by fusing aggregate patient histories with the results of up to the minute lab tests.
  • Web scraping analytic tools applied to for example twitter can be used to measure real-time international conflict sentiment levels and to measure political tendencies and their movements prior to important elections etc.
  • With the development of electronic health records, remote treatment, and the ability to share data online, we have an array of new healthcare solutions available. The use of mobile technologies to collect and distribute information might help significantly with the prevention and treatment of disease.
 
Content - Key areas:
Throughout the course, the students combine theoretical knowledge with an extensive project work, where they get hands-on experience in accessing and working with big data. The course has three main areas:
  • Datafication and data collection: Methods to generate and structure data in an expedient and operable format
  • Data analysis and data visualization: Methods to process, analyze and visualize the data
  • Decision making: Methods to making the right decisions on the basis of the data analysis
 
Goals description (SOLO taxonomy):
After taking the course, the student should be able to apply quantitative modelling and data analysis techniques to the solution of real world problems in the social sciences, communicate findings, and effectively present results using data visualization techniques.

The student, should after the course be able to:
  • Competently use data mining software to solve the problems based on Big Data.
  • Perform clearly articulated and informed decision making based on Big Data Analytics.
  • Account for and discuss all three phases of working with big data and specific methods for generating, processing/analysing and making informed decisions on the basis of Big Data. The student should be able to generate clear and operable management/policy implications on the basis of these three phases.
  • Identify and assess big data resources relevant in social sciences.
  • These abilities will be documented through the work with a particular case study, which will include:
  • Selecting and applying specific methods relevant for a particular case study.
  • Accessing relevant big data sources and analyze them using specialized software.
  • Clearly outlining the academic and managerial implications of working with the specific methods to an academic and a practitioner audience.
 
Literature:
Examples:
Bosch-Sijtsema, P. & Bosch, J. 2015. User Involvement throughout the Innovation Process in High-Tech Industries. Journal of Product Innovation Management, 32, (5) 793-807
Erevelles, S., Fukawa, N., & Swayne, L. 2016. Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69, (2) 897-904.
Lycett, M. 2013. 'Datafication': making sense of (big) data in a complex world. European Journal of Information Systems, 22, (4) 381-386
McAfee, A. & Brynjolfsson, E. 2012. Strategy & Competition Big Data: the Management Revolution. Harvard Business Review, 90, (10).
 
Time of classes:
August (summer school).
 
Scheduled classes:
9.15-13.00 eight times during august.
 
Form of instruction:
Individual studies as preparation for the lectures in the classes and a total of 30 hours in class (including practical exercises). Project work in groups. The readings for the course will be outlined by the end of June so that the students can commence the preparation well in advance of the teaching period.

The workload of the course is, on average, distributed as follows:

Class teaching: 30 hours
Group work beyond classes: 40 hours
Preparation for teaching: 170 hours
Preparation for exam: 30 hours.
Total: 270 hours.
 
Time of examination:
Ordinary exam in week 34.
Re-examination in week 39

The form for the re-exam can be changed by the study board. Such a change will be announced 14 days before the reexam takes place.

Registration for the course is automatically a registration for the ordinary examination in the course. Cancellation is not possible. If the student does not participate in the examination, the student will use an examination attempt.
The university may grant an exemption from the rules in case of exceptional circumstances.
 
Examination conditions:
None.
 
Form of examination for the certificate:
Take-home assignment and an oral exam.
 
Supplemental information for the form of examination:
1) Report written in groups of 3-4 students.
Duration: Deadline for handing in will appear from the examination plan.
Location: Home assignment.
Internet Access: Necessary.
Hand out: Course page in Blackboard.
Hand i: Via SDU-assignment.
Extent: 30-40 standard pages.
Exam Aids: All exam aids allowed.

2) 20 minutes are set aside for the oral exam.

The examination takes its starting point in the report and a presentation hereof. The examination also includes supplementary questions on theoretical, methodological and, if relevant, practical topics associated with the submitted report.

Grading according to the Danish 7-point scale. The grading is an overall assessment of the report and the performance at the oral exam. Grading based on the performance of the individual student compared to the learning goals.
 
Programmes:
M.Sc. - all profiles
3rd semester, elective subject. Offered in: Odense.