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CS38A0040 Marketing analytics, 6 cr 
Code CS38A0040  Validity 01.08.2017 - 31.07.2019
Name Marketing analytics  Abbreviation Marketing analy 
Credits6 cr   
TypeBasic studies  
ClassCourse   
  Grading scaleStudy modules 0-5,P/F 
  Eligibility for post-graduate studiesno
    Allowed to study several timesno
Organisation LUT School of Engineering Science 

Description by Study Guide
Note 

If the course enrollment is more than the course maximum, then students are accepted in the following order: students from MBAN programme, students from MIMM programme, other master’s programme students, other students.

 
Year 

M.Sc. (Tech) 1

 
Period 

4

 
Teaching Language 

English

 
Teacher(s) in Charge 

Christoph Lohrmann, M.Sc. (Econ.), Junior Researcher (modified 13.09.2018)

 

 
Aims 

The aim of the course is to offer extensive knowledge on the use of various analytical techniques in marketing. The students will be introduced to the process of decision support in marketing using analytics in various typical problems. Through several practical examples, the course aims to provide the tools that focus on data understanding and preprocessing, modelling choices and implementation until the interpretation, visualization and utilization of the analysis in various marketing-related problems. The course will provide hands-on lectures to using the various methodologies with the selected software environments. After the course the students: have an understanding of the process of performing marketing analytics, know how to collect, understand and preprocess data to be used in marketing problems, know the most important applications and can identify the appropriate tool for a specific problem, are capable of performing marketing analytics using software, understand the role of big data in marketing.

 
Contents 

Core content: role of data in modern marketing, traditional methods (clustering, forecasting, market-basket analysis), machine learning-based methods in marketing (recommendation systems, advertising on the web)
Additional content: social network analysis, sentiment analysis
Special content: use of the introduced methods with relevant software

 
Teaching Methods 

Lectures 20 h, computer room tutorials 10 hours, course assignments involving data analysis with software 75h. Written exam and preparation for the exam 55 h. Total workload for the student 160 h.

 
Examination in Examination schedule (Yes/No) 

Yes

 
Examination in Moodle (Yes/No) 

No

 
Examination in Exam (Yes/No) 

No

 
Assessment scale and assessment methods 

Course assignments (70% of the grade), written examination (30% of the grade), grading 0-5.

 
Course Materials 

The course will largely be based on the free online book (http://www.mmds.org/)
Leskovec-Rajaraman-Ullman: Mining of Massive Datasets
Additional material will be distributed during the course via Moodle.

 
Prerequisites 

The course will use an analytics capable software (to be announced later; Matlab or R, perhaps even Excel) - the students are expected to know how to use the software. Basic knowledge in statistics.

 
Limitation for students? (Yes, number, priorities/Leave empty) 

Yes. 50, priority to MBAN students (Masters program in business analytics), then students from MIMM programme, other master's programme students, other students.

 
Places for exchange-students? (Yes, number/No) 

No

 
Places for Open University Students?(Yes, number/No) 

No

 


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