Module Catalogues, Xi'an Jiaotong-Liverpool University   
 
Module Code: IFB106TC
Module Title: Demand Forecasting & Management
Module Level: Level 1
Module Credits: 2.50
Academic Year: 2020/21
Semester: SEM2
Originating Department: School of Intelligent Finance and Business
Pre-requisites: MTH008
   
Aims
The objective of this module is to develop an understanding of data analytics with emphasis on forecasting as a powerful tool for analyzing complex issues and solving business problems. The course will make productive use of analytics tools available in MS Excel (and dedicated add-ins) and R (optional). While the class focuses on simplified models, it aims to bridge the classroom knowledge and business applications.
Learning outcomes 
A. Use effectively forecasting strategies

B. Administer critically forecasting techniques

C. Demonstrate understanding of how to measure and improve forecast accuracy, measure and reduce forecast error & bias


Method of teaching and learning 
The teaching philosophy of the module follows very much the philosophy of Syntegrative Education. This has meant that the teaching delivery pattern, which follows more intensive block teaching, allows more meaningful contribution from industry partners. This philosophy is carried through also in terms of assessment, with reduction on the use of exams and increase in coursework, especially problem-based assessments that are project focused. The delivery pattern provides space in the semester for students to concentrate on completing the assessments.


Students will be expected to attend lectures, seminars and tutorials to learn about the academic and theoretical content as well as the practical skills which are the subject of the module. A conjunction of continuous assessment tasks and in-class tests will be used to test to which extent theoretical content and practical skills have been learned. In addition, in groups of 3-4, students will be working on a small project in which they will do a careful data analysis of real application. The project should include a clearly defined business problem, data preparation, analytic model, and solution supported by performance evaluation.
Syllabus 
Topics will typically include:

• Introduction and Review of Statistics

• Business Problems and Data Science Solutions

• Time Series Methods

• Predictive Modeling

• Fitting a Model to Data: Regressions and Overfitting

• Similarity, Neighbors, and Clusters

• The Box-Jenkins Methods: ARIMA model

• Decision Analytical Thinking I: What Is a Good Modelbr> Visualizing Model Performance

• Decision Analytic Thinking II: Toward Analytical Engineering

• Other Data Science Tasks and Techniques

• Data Driven Business and Conclusion

Delivery Hours  
Lectures Seminars Tutorials Lab/Prcaticals Fieldwork / Placement Other(Private study) Total
Hours/Semester 20  2  6      47  75 

Assessment

Sequence Method % of Final Mark
1 In-Class Test 50.00
2 Case Study(1000 Words) 50.00

Module Catalogue generated from SITS CUT-OFF: 10/21/2020 6:49:46 PM