Syllabus

LEARNING OUTCOMES

The course provides an overview of data analytics for organizational decision making.

It aims to introduce data analytics in the business context, and specifically the use of data to gain insights, and support decision making, by describing what has happened or what is happening, diagnozing why something happened, predicting what will happen in the future, and recommending what to do to make something happen.

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

• Familiar with the challenges and pitfalls of introducing and using data analytics to support organizational decision making, and the basics of making decisions informed by data, including identifying data requirements, critically evaluating data sources, modelling and analyzing data, and interpreting findings.
• Able to make sense of, model, and analyze data in the context of specific business problems, including 1) modeling business problems; 2) conducting analysis by using relevant software; 3) visualizing the findings and storytelling; and 4) making recommendations based on them.
• Able to build, and interpret the results of, basic models that are commonly used to support decision making, such as forecasting, project planning and decision analysis models.

Credits: 6

Schedule: 04.09.2024 - 17.10.2024

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):

Responsible Teacher: Dr Anastasia Koulouri, Senior University Lecturer

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 will:

  • Introduce data analytics and its emergence, evolution and use in informing organizational strategy and decision making.
  • Touch upon topics such as the Internet of Things, big data, the challenges associated with using data to inform decisions including those related to governance and ethics, and the impact of analytics in organizations.
  • Explore techniques to describe and visualize data and use storytelling.
  • Consider the use of models to inform decision making-such as project planning, decision tables and trees, and forecasting-by introducing the basics of each of these modelling approaches and focusing on building, using and interpreting the results of basic models.

• applies in this implementation

  
  

Assessment Methods and Criteria

• valid for whole curriculum period:

  Participation.

  Successful completion of individual assignment and end-of-course exam.

• applies in this implementation

  The course will be conducted on campus and in person.

  The course delivery comprises weekly:

  • One 1-hour mandatory lecture during which students will be introduced to theoretical concepts and techniques.
  • One 2-hour mandatory small-group tutorial/lab during which students will have the opportunity to apply concepts and techniques in practice.

  Attendance to 70% of the lectures and 70% of the tutorials/labs is mandatory to pass the course. In practice this means that students should attend a minimum of 4 out of the 6 lectures and 4 out of 6 tutorials/labs to pass the course.

  The course assessment will be based on the following components:

  Assignment: Data analysis and visualization exercise (40%)

  Exam (60%)

  The assignment 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 course. This means that for the assignment students need to achieve at least 20%, and for the exam students 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-biz@aalto.fi). In such a case, students should contact the Responsible Teacher to inform of the situation and discuss alternative arrangements; students should not email medical certificates to the Responsible Teacher.

  The final grade (0 to 5) 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

  Note that submitting course assignments and an exam response is considered acknowledgement of the guidelines on scholastic honesty and academic integrity. 
  

  Aalto University Code of Academic Integrity and Handling Violations Thereof | Aalto University

   

  
  
  
  

Workload

• valid for whole curriculum period:

  Lectures and small group tutorials/labs.

  Assessment based on individual assignment and an end-of-course exam.

  Mandatory attendance to 4 out of 6 lectures and 4 out of 6 labs

• applies in this implementation

  Class contact, mandatory lectures	6h
  Class contact, mandatory tutorials/labs	12h
  Self-study, directed/undirected	67h
  Assignment	30h
  Exam	45h
  Total	160h (6 ECTS)

  
  

  It would be beneficial for students to complete each week’s directed study (including practice tasks, exercises, and/or readings) before the next lecture in order to keep up with the course pace.

  Time to work on the assignment and to prepare for the exam is separate from the time calculated for self-study, though time invested in studying and completing each week's direct study will be invaluable for both completing the assignment and undertaking the exam.
  

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

  For more details, see the draft course syllabus on MyCourses.

Substitutes for Courses

• valid for whole curriculum period:

  Substitutes for Courses

Prerequisites

• valid for whole curriculum period:

  Prerequisites

SDG: Sustainable Development Goals

8 Decent Work and Economic Growth

FURTHER INFORMATION

Further Information

• valid for whole curriculum period:

  Teaching Language: English

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

  Course is offered only for Bachelor's students majoring in Management.

Details on the schedule

• applies in this implementation

  Please refer to Sisu.

Description