Opetussuunnitelmat

Objective

The student is able to use methods and techniques of mathematical programming in business application, which is: data gathering and input challenges and practical solutions, rigorous models formulation, simple to complex and example to generalization problem approach. Student knows how to: understand and analyze model output, and use it to generate practical business recommendations. Student is accustomed with reporting and visualization techniques. The course will focus on practical applications with MS Excel.

IBCRI: Skills in Critical and Analytical Understanding: Critically review, analyze and understand information available from academic and professional business sources.

IBBUS: Applied Business Skills: Apply disciplinary and interdisciplinary knowledge to analyze business challenges and global trends to propose or put into action practical business solutions based on the findings.

Content

Business analytics with: optimization models, linear programming, non-linear programming, integer programming, goal programming, transportation modeling, simulation. Marketing analytics with: linear programming, multiple goals.

Location and time

100% virtual, self paced course.

Materials

Video lectures and tutorials
PowerPoint slides
MS Excel applications
Literature:
Business Analytics with Management Science Models and Methods, Arben Asllani, 2015.
Optimization in Operations Research: Pearson New International Edition, Ronald L. Rardin, 2014.

Teaching methods

100% virtual course. Distant learning.
Introductory meeting, video lectures and tutorials, homework exercises and assignments.

Exam schedules

Assignments or/and projects. Deadlines to be announced at the beginning of the course.

Completion alternatives

No

Student workload

Video lectures 30h, virtual study 15h, assignments 15h, independent study 15h.

Content scheduling

Business analytics with: optimization models, linear programming, non-linear programming, integer programming, goal programming, transportation modeling, simulation.
Marketing analytics with: linear programming, multiple goals.

Further information

Exchange students: 20 places (included in the total capacity)
Open UAS: 5 (included in the total capacity)

Evaluation scale

0-5

Assessment criteria, satisfactory (1)

Assignments 100%:

- Sufficient (1) - The student shows only a sufficient understanding of the subject and of relevant theories and methods and fails to show a convincing ability to apply the knowledge to real life situation nor to communicate the concepts in an effective manner.
- Satisfactory (2) - The students knowledge covers restricted part of concepts and theories related to the subject and has deficiencies in showing the ability to apply them in assignments. Communication skills need improvement.

Assessment criteria, good (3)

- Good (3) The students knowledge covers major parts of concepts and theories related to the subject. The student´s ability to apply the knowledge to assignments shows need for improvement, as well as there is room for improvement in oral and written communication.
- Very Good (4) - The student shows good knowledge of the subject fundamentals and concepts and is able to communicate these in an effective way.

Assessment criteria, excellent (5)

- Excellent (5) - The student shows exceptional theoretical knowledge of fundamentals of the subject. The student demonstrates the ability to apply the knowledge to assignments and presentations with excellent written and oral communications skills.

Assessment criteria, approved/failed

The general criteria of the competences that the Bachelor's Degrees awarded by Finnish Universities of Applied Sciences provide can be found on JAMK's web pages (http://www.jamk.fi/english/forstudents/studyguide). The learning outcomes of individual courses are assessed in relation to the objectives of the course concerned. Assessment is based on knowledge, skills and competence in accordance with the National and European Qualifications Framework, level 6. Grades are from 0 to 5.

Qualifications

Knowledge of basic descriptive statistics