MAN-BKV69
Introduction to Big Data Analytics with applications to international business issues
Course infoSchedule
Course moduleMAN-BKV69
Credits (ECTS)6
CategoryBA (Bachelor)
Language of instructionEnglish
Offered byRadboud University; Nijmegen School of Management; Bachelor Business Administration;
Lecturer(s)
Lecturer
dr. H.P.L.M. Korzilius
Other course modules lecturer
Examiner
H.S.A. Mahmoud, MSc
Other course modules lecturer
Lecturer
H.S.A. Mahmoud, MSc
Other course modules lecturer
Coordinator
H.S.A. Mahmoud, MSc
Other course modules lecturer
Contactperson for the course
H.S.A. Mahmoud, MSc
Other course modules lecturer
Academic year2022
Period
2  (07/11/2022 to 29/01/2023)
Starting block
2
Course mode
full-time
Remarks-
Registration using OSIRISYes
Course open to students from other facultiesYes
Pre-registrationNo
Waiting listNo
Placement procedure-
Aims
Upon successful completion of this course, students should be able to:
  • Deploy the Data Analytics Lifecycle to address big data analytics challenges
  • Reframe a business issue as an analytics challenge
  • Choose the appropriate analytic techniques and tools to analyze big data, create statistical models, and identify insights that can lead to actionable results
  • Select appropriate data visualization technique to communicate analytic insights to business sponsors and analytic audience
  • Use R and RStudio for data analysis purposes
  • Explain how advanced analytics can be leveraged to create international competitive advantage
  • Develop group project report, and communicate results through group presentation
Content
This course provides practical foundation level knowledge of Big Data analytics. It includes an introduction to big data and the Data Analytics Lifecycle to address international business issues that leverage big data. The course provides grounding in basic and advanced analytic methods and an introduction to big data analytics technology and tools using the statistical programming language R. The course is divided between lectures and labs. The labs using R offer opportunities for students to understand how these methods and tools may be applied to real world business issue. The course takes an open and technology-neutral approach, and includes a final lab which addresses a big data analytics challenge by applying the concepts taught in the course in the context of the Data Analytics Lifecycle.

Content and timetable
Lectures:
  • Week One: Introduction to Big Data Analytics
  • Week Two: Data Analytics Lifecycle
  • Week Three: Review of Basic Data Analytic Methods
  • Week Four: Advanced Analytical Theory and Methods: Clustering
  • Week Five: Advanced Analytical Theory and Methods: Association Rules
  • Week Six: Advanced Analytical Theory and Methods: Classification
  • Week Seven: Putting It All Together
Weekly technical labs using R-language are parallel to the lectures where students apply the theoretical knowledge they learned.
Level
 
Presumed foreknowledge
To complete this course successfully and gain the maximum benefits from it, a student should have quantitative background and a solid understanding of basic statistics, as would be found in:
  • Statistics
  • Quantitative Research Methods
Test information

Examination
Written exam (taken digitally)
Project report and presentation (based on group project)

Both parts of the examination need to be ≥ 5.5 in order to pass the course
 

Entry requirements exams
80% attendance of both lectures and labs

Specifics

Level
KV

Required materials
Book
ISBN:978-1-118-87613-8
Title:Data Science & Big Data Analytics Discovering, Analyzing, Visualizing and Presenting Data.
Author:Dietrich, D., Heller, B., & Yang, B
Publisher:John Wiley & Sons, Inc.

Instructional modes
Lecture
Type of instructional modeLecture

Office hours for Q&As
Type of instructional modeSelf study

Seminar
Attendance MandatoryYes
Type of instructional modeSeminar

Tests
Exam
Test weight1
Test typeDigital exam with CIRRUS
OpportunitiesBlock 2, Block 3