LEARNING OUTCOMES
Business Analytics refers to the use of quantitative models to help make better business decisions. This course focuses on mathematical optimization models that are commonly used in business applications. After the course the student can (i) recognize the types of real-world business decision problems where use of these models brings added value, (ii) interpret results of these models to derive defensible decision recommendations, and (iii) build and solve these models using relevant software to support business decision making.
Credits: 6
Schedule: 21.10.2024 - 04.12.2024
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Juuso Liesiö
Contact information for the course (applies in this implementation):
Prof. Juuso Liesiö (juuso.liesio(at)aalto.fi)
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: Finnish, English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
Linear, mixed-integer, non-linear and multiple objective optimization/programming models with applications examples in supply chain management, production planning, finance and marketing.
applies in this implementation
This course focuses on Business Analytics & Management Science methods based on mathematical optimization such as Linear Programming, Mixed-Integer Linear Programming and Non-Linear Programming. These methods are introduced through applications in, for instance, production planning & scheduling, logistics, marketing and finance. The course also introduces approaches for modelling uncertainties and multiple decision objectives in optimization models.
Assessment Methods and Criteria
valid for whole curriculum period:
Assignments and exam.
applies in this implementation
There are three assignments each consists of (i) a quiz in MyCourses, which can be taken multiple times until the deadline, and (ii) a spreadsheet to be returned before the deadline. The grading formula is
final_points = 50%*min(assignment_points, 100) + 50% * exam_points + feedback_points,
where assignment_points belongs to the interval [0,112] (see Grades-page for details), exam_points to [0,100] and feedback_points to [0,1]. Hence, the final_points belongs to the interval [0,101]. The final_points determine the course grade as follows: >50p->1, >60p->2, >70p->3, >80p->4, and >90p->5, with the exception that at least half of the exam_points are required to pass. These bounds maybe relaxed during final grading.
Workload
valid for whole curriculum period:
Lectures, exercise sessions and exam.
applies in this implementation
Sessions 36h
Class preparation 12h
Assignments 102h
Preparing for the exam 7h
Exam 3hTotal: 160h
DETAILS
Study Material
valid for whole curriculum period:
Lecture slides, scientific articles, assignments, computer implementations of mathematical models, and the textbook (An Introduction to Management Science by Anderson et al., 2014, ISBN Code: 978-1-111-82361-0).
applies in this implementation
Lecture slides, articles, assignments, computer implementations of mathematical models, and the textbook (An Introduction to Management Science by Anderson et al., 2014, ISBN Code: 978-1-111-82361-0).
All material except for the textbook will be available at MyCourses.
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language: English
Teaching Period: 2024-2025 Autumn II
2025-2026 Autumn II
Details on the schedule
applies in this implementation
Visual presentation of the schedule for 2024 can be found on "General"-page.