Duration
30h Th
Number of credits
| Master in management (120 ECTS) | 5 crédits |
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
Did you know that...
- in 2016, 2.5 million gigabytes of new data were produced every day.
- over 90% of all the data in the world was created in the past 2 years.
- the total amount of data being captured and stored by industry doubles every 1.2 year.
... but what in the world do businesses do with all of this data?
The main question is: How to transform raw "stupid" data into valuable "actionable" information to help decision making? The answer is : « By using Marketing Analytics techniques ! »
Marketing analytics involves the creation and use of quantitative data to derive consumer insights and make decisions. It is often heralded as a critical resource necessary for effective marketing.
Common applications in marketing and sales are the analysis of customer loyalty, the analysis of the market basket, the identification of prospective customers.. Common applications in eCommerce are recommendation tools, converting clicks into customers... Other common applications in Supply Chain Management are revenue management (e.g. airline ticketing), capacity management, diagnosis of production faults, forecasting demand, predictive modeling, advanced reporting based on ERP systems... Common applications in finance are fraud detection, credit risk analysis, risk profile, pricing...
In this course, you will learn :
Good practices in data analysis
Descriptive analytics tools:
- Advanced reporting and visualisation tools,
- Market Basket Analysis
- Clustering/Segmentation
- Regression (linear and logistic)
- Decision trees
Learning outcomes of the learning unit
This course will help students to :
- Gain the attitudes, skills, knowledge and understanding specific to the chosen concentration field.
- Establish a strategy to optimize the value chain of a company.
- Use modelization methods efficiently.
- Develop scientific precision and a critical sense (arguing).
- Take advantage of data digitalization.
- Acquire the capacity to research autonomously and methodically the information needed to solve a complex, transversal management problem.
- Integrate autonomously researched information, tools, knowledge and context to build and propose original, creative and viable solutions to concrete complex management problems whether real or simulated.
- Provide concrete solutions to a management problem, integrating modelization methods and/or a dimension of technology, innovation or production.
- aqcuire a professional capacity for written communication.
Prerequisite knowledge and skills
Basic computer skills.
Basic statistics (descriptive statistics, and elements of probability)
Planned learning activities and teaching methods
Emphasis will be placed on the understanding and real-life application of the analytics methods. To this end, all the theoretical concepts will be diretcly illustrated on small examples and students will practice on real-data examples. They will acquire a basic theoretical knowledge but also learn to use professional tools like Excel, Tableau or SAS Enterprise Miner.
The students will also have to complete a group project on a provided dataset. This project will run throughout the year and will be broken down into small steps.
A flipped classroom approach is used for some chapters: The students have to prepare some topics before the lecture thanks to an online tutorial and/or slides. The lecture will focus on the difficulties encountered during the preparation, as well as on the interpretation of the results.
The project and the preparations are compulsory. If a student has not completed all his/her assignments, he/she will not be allowed to sit the exam (failure mark).
Mode of delivery (face-to-face ; distance-learning)
All lectures are given in a computer room.
Recommended or required readings
All required documents will be published on Lol@.
Recommended:
Introduction to Data Mining, Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Pearson International.
Assessment methods and criteria
First session
Three parts:
- Understanding of the methods - 1/3
- Practice on the computer - 1/3
- Project - 1/3
Two parts:
- Understanding of the methods - 1/2
- Practice on computer - 1/2
The project and the preparations are compulsory. If a student has not completed all his/her assignments (first and second sessions), he/she will obtain a failure mark and will not be allowed to sit the exam.
The marks for the (group) project are not anymore taken into account during the second session but one question could imply a modification of the project.
Work placement(s)
Organizational remarks
The examinations are organized twice a year during the official sessions. All the students sit the exam in Liège. Due to the nature of this course, we do not accept to organize any additional exam at a different date and/or at a different location (even for students from abroad).
Contacts
S. Aerts, HEC-ULg, N1 (334) Stephanie.Aerts@uliege.be
Adaptation of teaching commitments following the COVID-19 pandemic for the May-June 2020 session
Teaching methods implemented : distance-learning
Podcasts and slides are posted on lol@, as well as self study exercises and projects.
Optional questions and answer sessions are organized via LifeSize.
Assessment subjects
Initially, the evaluation was planned as such:
Option 1: No certification:
Project 1: Visualization: 1/6
Project 2: Machine Learning: 1/6
Exam Part 1: Theory & Understanding of the methods : 1/3
Exam Part 2: Practice Visualization (Excel + Tableau): 1/6
Exam Part 3: Practice Machine Learning (SAS): 1/6
Option 2: Certification
Project 1: Visualization: 1/6
Exam Part 1: Theory & Understanding of the methods: 1/3
Exam Part 2: Practice Visualization (Excel + Tableau): 1/6
Certification exam: 1/3
The changes are the following:
- For all: Part 1 of the exam is cancelled. Concept comprehension will be assessed through the few theoretical questions included in the certification examination for Option 2 students, and through comprehension questions included in a practical examination for Option 1 students (see below). There will therefore be no 'purely theoretical' questions of restitution.
- For all : Part 2 of the exam is also cancelled. The data visualization part will be assessed solely on the basis of the Tableau project.
- For Option 1 students : Part 3 of the exam is maintained and will be organized online. This part, organized de facto as an open course exam, will not contain any restitution questions but will include, in addition to data analysis carried out using SAS software, questions of interpretation and understanding (which therefore require a good understanding of the theory slides as well).
- For Option 2 students: The certification exam will be held online, with remote monitoring by a SAS proctor via webcam. You will be able to take this exam from your home, at a date and time of your choice (individual choice).
Assessment methods
The new repartition is thus as follows:
Option 1:
Project 1: 1/3
Project 2: 1/3
Exam Part 3: 1/3 (online exam with open questions requiring software manipulation)
Option 2 :
Project 1: 1/3
Certification exam: 2/3 (online exam with MCQ and open questions requiring software manipulation)
Contacts
Stephanie.Aerts@uliege.be
Adaptation of teaching commitments following the COVID-19 pandemic for the Aug-Sept 2020 session
Assessment subjects
Same content as for the first session examination. To prepare yourself for the best, see all documents and information posted for the 1st session examination.
Assessment methods
The repartition is the same as in first session. Students do not need to stick to their initial option, and are allowed to switch, as far as they inform the teacher before the 1st of August.
Partial carry-over for each project or the written exam may be obtained if the 1st session grade for this part is 10/20 or higher.
Each group project in which the student did not participate in 1st session becomes an individual project in 2nd session. Projects must be submitted by the official 2nd session exam date.
Option 1:
Project 1 (Data vizualisation): 1/3
Project 2 (Predictive methods) : 1/3
Exam Part 3: 1/3 (online exam with open questions requiring software manipulation). This part, organized de facto as an open course exam, will not contain any restitution questions but will include, in addition to data analysis carried out using SAS software, questions of interpretation and understanding (which therefore require a good understanding of the theory slides as well).
Option 2 :
Project 1 (Data vizualisation): 1/3
Certification exam: 2/3 (online exam with MCQ and open questions requiring software manipulation). The student can either sit the online version of the certification exam by the end of July or sit the HEC certification session organized on the official 2nd session exam date.
Contacts
Stephanie.Aerts@uliege.be