Duration
45h Th
Number of credits
| Master in sales management, professional focus (en alternance) | 5 crédits |
Lecturer
Language(s) of instruction
English language
Organisation and examination
All year long, with partial in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
The term Sales Analytics refers to the practice of generating insights from data, trends, and sales indicators in order to set goals and forecast future sales performance.
With the course Sales Analytics I, a prerequisite for this one, students became acquainted with the fundamental concepts of data collection and management, exploring data to summarize complex datasets; as well as some techniques of inferential statistics and forecasting.
In this advanced course, divided into two complementary parts, students become familiar with predictive techniques and carry out a complex data analysis project from start to finish.
Part I: Data Visualization
To decipher data from various channels, consolidate it, synthesize it, and thus illuminate decision-making, data visualization has established itself as
an indispensable solution. In this course, students will learn to implement dynamic data summaries using business intelligence software. Through practical exercises, they will master the essential features to easily create comprehensive and detailed views of complex data. The fundamental concepts of descriptive statistics taught in Sales Analytics I will be revisited.
Part II: Predictive Methods - Machine Learning/AI
Artificial intelligence systems allow to analyze and understand the collected sales data, to learn from it and to recommend actions (e.g. pricing strategies, predicting the next best offer, prioritizing opportunities according to their probability of success,...).
The sales manager of tomorrow must therefore understand the fundamentals of these techniques in order to be able to identify the analysis opportunities they allow, to evaluate the feasibility of a data mining project and to follow its implementation, to analyze the results such methods provide and to be able to critique them.
In this course, the fundamental concepts of predictive methods will be discussed. Several classical techniques (decision trees, regression,...) will be introduced and applied to real data sets. The focus will be on understanding the techniques and the interpretation and discussion of the results.
Learning outcomes of the learning unit
Part I :
Upon completion of this unit of instruction, the student will be able to:
- Choose an appropriate representation for a phenomenon to be analyzed
- Analyze business data by constructing dashboards and mastering a business intelligence tool
Part II:
Upon completion of this unit of instruction, the student will be able to:
- Understand and explain the fundamental principles of predictive methods
- Choose the most appropriate AI technique(s) to answer a problem
- Identify the limitations of the AI techniques used, their advantages and disadvantages
- Use an AI tool/module
- Interpret the results of predictive analysis
- Demonstrate critical thinking and analytical skills in monitoring a project using machine learning
Prerequisite knowledge and skills
The Sales Analytics I course is a prerequisite.
Planned learning activities and teaching methods
The learning sessions will be of several kinds:
- discussions around practical cases, lectures interspersed with direct application exercises of the concepts
- practical demonstration on software
- group work on business cases and Q/R session
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Additional information:
Face to face
Course materials and recommended or required readings
All required documents will be posted on lola.
Exam(s) in session
Any session
- In-person
oral exam
Written work / report
Further information:
Exam(s) in session
Any session
- In-person
oral exam
Written work / report
Further information:
Exam(s) in session
Any session
- In-person
oral exam
Written work / report
Further information:
see French version
Work placement(s)
Organisational remarks and main changes to the course
All sessions require the use of a laptop with all course software installed.
Contacts
S. Aerts (stephanie.aerts@uliege.be)
M. Dumont (morgane.dumont@uliege.be)