TU-E5030 - Creating Value with Analytics D, Lecture, 28.2.2022-7.4.2022

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

After completing the course, student is able to:

  • Understand the basic principles and applications of contemporary analytics methods
  • Identify business problems that can be addressed using available data
  • Formulate analytics problems based on a given business problem
  • Select and apply a suitable analytics method for a given analytics problem
  • Define the data requirements for a given analytics method
  • Compare the benefits and drawbacks of alternative models
  • Evaluate the business benefits of a given analytics solution

Credits: 5

Schedule: 28.02.2022 - 07.04.2022

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Jukka Luoma, Lauri Saarinen

Contact information for the course (applies in this implementation):

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:

    The course provides an overview of the data analytics toolbox including topics such as causal inference, predictive analytics, optimization, and simulation. These topics are approached from two perspectives: 1) how can the analytics toolbox be applied to problems arising in operations management, strategic management, and organizational design and development; 2) what are the implications of increasing reliance on analytics for these fields. The lectures discuss how analytics relates to real-world problems while the weekly assignments provide an opportunity to gain hands-on experience in applying analytics methods. In the group project, the students work in teams to develop an analytics solution for a case organization.

Assessment Methods and Criteria

  • valid for whole curriculum period:

    • Individual assignments
    • Group project
    • Class activity

Workload

  • valid for whole curriculum period:

    • Lectures (24 hours)
    • Weekly readings (20 hours)
    • Weekly assignments (71 hours)
    • Group project (20 hours)

DETAILS

Study Material

  • valid for whole curriculum period:

    A collection of technical readings and cases assigned by the instructors.

Substitutes for Courses
Prerequisites

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Prerequisites for the course: basic course in statistics (e.g., MS-A0502/ MS-A0509), introductory course in programming is recommended (e.g., CS-A1110).

    Teaching Period:

    2020-2021 Spring IV

    2021-2022 Spring IV

    Course Homepage: https://mycourses.aalto.fi/course/search.php?search=TU-E5030

    Registration for Courses: In the academic year 2021-2022, registration for courses will take place on Sisu (sisu.aalto.fi) instead of WebOodi.

    Registration through WebOodi. The number of students admitted to the course is limited. Students are given priority as follows: 1) Master s students in Industrial Engineering and Management program, and Master s students in the Information Networks program for whom the course is compulsory, 2) exchange students in the Industrial Engineering and Management Master s program, and 3) other students.

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

Aalto University Code of Academic Integrity and Handling Violations Thereof