ISM-C1004 - Business Analytics 1, Lecture, 24.10.2022-8.12.2022

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Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

The fundamental aim of Business Analytics is utilizing analytical models to help make better business decisions. This course focuses on optimization models that are commonly used in business applications. After the course the student can (i) recognize the types of real-life 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: 24.10.2022 - 08.12.2022

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: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

  • valid for whole curriculum period:

    Linear programming, network and distribution models, mixed-integer linear programming, non-linear programming.

  • 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 3h

    Total: 160h

DETAILS

Study Material

  • 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
Prerequisites

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language : English

    Teaching Period : 2022-2023 Autumn II
    2023-2024 Autumn II

Rules of Conduct - Students´ Rights and Responsibilities (in force from 1 August 2020)

Aalto University Code of Academic Integrity and Handling Violations Thereof