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: 21.10.2024 - 08.11.2024
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 assignments (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 1: Reflection paper (40%)
Assignment 2: Data analysis exercise 1 (60%)
Both assignments are to be undertaken individually.
Both assignments must be submitted and achieve a passing grade to pass the module. This means that for the reflection paper you need to achieve at least 20%, and for the data analysis exercise 1 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 1 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
9h
Class contact, optional drop-ins
9h
Self-study, directed/undirected
15h
Assignments (2 in total)
47h
Total
80h (3 ECTS)
DETAILS
Study Material
valid for whole curriculum period:
Course materials (including slides, Excel practice notes, exercises and associated commentary, datasets) as well as videos, books and journal articles.
applies in this implementation
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 IIRegistration:
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 1 Introducing data analytics
1a
Lectorial:
Monday 21.10.24
09:15-11.45
Introduction to the course
Data Analytics: emergence and evolution of the field.Types of analytics and their use.
Data characteristics
Guest speaker: TBC
Assignment 1:
Issued on 21.10.24
Due 01.11.24, 23:00
1b
Drop-in (optional): Weds 23.10.24
10:15-11:45
1c
Drop-in (optional): Fri 25.10.24
12:15-13:00
2a
Lectorial:
Monday 28.10.24
09:15-11.45
Descriptive statistics: Exploring a single numerical/categorial variable, two categorical variables, numerical variable between groups
Guest speaker: Dr Hertta Vuorenmaa (Aalto University Lecturer/PhD Research Director Future of Work, Chair of The Finnish Association of Work Life Research) on “Ethics and Data”
2b
Drop-in (optional): Weds 30.10.24
10:15-11:45
2c
Drop-in (optional): Fri 01.11.24
12:15-13:00
3a
Lectorial:
Monday 04.11.24
09:15-11.45
Visualization and storytelling.
Guest Speaker: Jan Brittner (Senior Consultant, EY Parthenon) on “Visualizing data analysis for business decisions”
Assignment 2:
Issued on 04.11.24
Due 15.11.24, 23:00
3b
Drop-in (optional): Weds 06.11.24
10:15-11:45
3c
Drop-in (optional): Fri 08.11.24
12:15-13:00