Enrolment options

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

Upon successful completion of this course, the student will be able to:

  • Understand the basic principles of planning and leading with data, including critical evaluation of the source and context of the data, and the challenges and pitfalls of introducing and using data analytics to support organizational decision making
  • Apply data analysis to business problems, including 1) combining data from multiple sources, 2) conducting analysis by using relevant software, 3) visualizing the findings and storytelling, and 4) making recommendations based on them
  • Understand the legal and ethical issues associated with collecting, storing and handling data
  • Build and interpret the results of basic models that are commonly used to support decision making, including Linear Programming, Forecasting and Decision Analysis models

Credits: 3

Schedule: 11.11.2024 - 03.12.2024

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Anastasia Koulouri

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

Email: anastasia.koulouri@aalto.fi

Office: Business School, Department of Management Studies

Office hours: By appointment, please email


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 consists of two modules: 1) Introduction to data analytics; and 2) Modelling to inform decision making

    The first module introduces data analytics and its emergence, evolution and use in informing decision making. It focuses on analyzing and visualizing data to draw insights and make recommendations. It also touches upon the challenges associated with using data to inform decisions including those related to governance and ethics.

    The second module focuses on the use of models to inform decision making, including optimization, decision trees, and forecasting. It introduces the basics of each of these three modelling approaches and focuses on building, using and interpreting the results of basic models.

Assessment Methods and Criteria
  • valid for whole curriculum period:

    Based on participation and successful completion of assginments (module 1), and assignment and exam (module 2)

    Students taking module 1 have the option to attempt a pre-course test in the first week of the teaching period and receive 3 credits without the above requirements on a pass/fail basis (that is no grade out of 5).

  • applies in this implementation

    The module assessment will be based on the following components:

    Assignment: Data analysis exercise 2 (40%)

    Exam (60%)

    The data analysis exercise 2 and the exam are to be undertaken individually.

    The assignment must be submitted and the exam must be undertaken, and a passing grade must be achieved at both, to pass the module. This means that for the data analysis exercise you need to achieve at least 20%, and for the exam you need to achieve at least 30%.

    No late submissions are accepted unless there is a valid reason supported by evidence (e.g. doctor’s certificate which should be sent to confidential@aalto.fi). In such a case, please contact the Responsible Teacher to inform of the situation and discuss alternative arrangements-please do not email your certificate!

    The final grade (0 to 5) for module 2 is based on the cut-off points below:

    0-49 points = 0

    50-59 points = 1

    60-69 points = 2

    70-79 points = 3

    80-89 points = 4

    90-100 points = 5

     


Workload
  • valid for whole curriculum period:

    Lectures and drop-in sessions

    Module 1: Assessment based on individual assignments; mandatory attendance to 2 out of 3 lectures.

    Module 2: Assessment based on individual assignment and exam; mandatory attendance to 2 out of 3 lectures.

    Attendance to drop-in sessions is optional.

  • applies in this implementation

    Class contact, mandatory lectorials

    9h

    Class contact, optional drop-ins

    9h

    Self-study, directed/undirected (including exam preparation)

    18h

    Assignment (1 in total)

    11h

    Exam preparation

    33h

    Total

    80h (3 ECTS)


DETAILS

Study Material
  • valid for whole curriculum period:

    Course materials (including slides, Excel practice notes, exercises and associated comnnetary, datasets) as well as videos, books and journal articles.

Substitutes for Courses
Prerequisites

FURTHER INFORMATION

Further Information
  • valid for whole curriculum period:

    Teaching Language: English

    Teaching Period: 2024-2025 Autumn II
    2025-2026 Autumn II

    Registration:

    Students are admitted to the course in the following priority order 1) Global Management / People Management and Organizational Development students, 2) CEMS / Strategic Management in a Changing World students, 3) other students. 

  • applies in this implementation

    This is an introductory course to data analytics and quantitative methods for decision making. Students who have attended other courses on these subject areas might find this course unsuitable for their needs.

    The module delivery comprises weekly:

    • One 3-hour mandatory lectorial during which you will be introduced to theoretical concepts and techniques and have the opportunity to apply them in practice using Excel.
    • Two optional drop-in sessions (2hr+1hr) to address any individual questions/difficulties you might have.

    Attendance to 70% of the lectorials is mandatory to pass the module. In practice this means that you should attend a minimum of 2 out of the 3 lectorials. 


Details on the schedule
  • applies in this implementation

    Session

    Input:

    Date, Time

    Topic(s) covered

    Deliverable


    Module 2 Modelling to inform decision-making

    4a

    Lectorial:

    Monday 11.11.24

    09:15-11.45

    Optimization

    Assignment:

    Issued on 11.11.24

    Due 22.11.24, 23:00

    4b

    Drop-in (optional): Weds 13.11.24

    10:15-11:45

     

     

    4c

    Drop-in (optional): Fri 15.11.24

    12:15-13:00

     

     

    5a

    Lectorial:

    Monday 18.11.24

    09:15-11.45

    Forecasting

     

    5b

    Drop-in (optional): Weds 20.11.24

    10:15-11:45

     

     

    5c

    Drop-in (optional): Fri 22.11.24

    12:15-13:00

     

     

    6a

    Lectorial:

    Monday 25.11.24

    09:15-11.45

    Decision Trees

     

    6b

    Drop-in (optional): Weds 27.11.24

    10:15-11:45

     

     

    6c

    Drop-in (optional): Fri 29.11.24

    12:15-13:00

     

     

     

     

     

    Exam:                          On-campus, 03.12.2024

    10:00-12:00

     

     

     

    Exam re-sit:                          On-campus, 03.02.2025

    16:30-18:30


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