Kursens beskrivning

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

  
  

  Class contact, mandatory lectorials	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:

  Substitutes for Courses

Prerequisites

• valid for whole curriculum period:

  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 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

  
  

Beskrivning