Syllabus

LEARNING OUTCOMES

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

• Understand the basic principles of planning and leading with data, including critical evaluation of the source and context of the data
• Understand the legal and ethical issues associated with collecting, storing and handling data
• 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 4) making recommendations based on them
• Build and interpret the results of basic optimization models that are commonly used to support decision making

Credits: 3 - 6

Schedule: 23.10.2023 - 05.12.2023

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 consists of two modules: 1) Principles of planning and leading with data; and 2) Decision making based on optimization and modelling. 

  The first module introduces the key issues associated with collecting, interpreting, and making conclusions based on data. Legal and ethical issues associated with different types of data are also emphasized. On this hands-on course, the students will analyze data related to real-life business decisions and make recommendations based on them. The analyses are conducted with selected software. Other software are discussed to provide an understanding of alternative options and opportunities.

  The second module focuses on decision making based on mathematical optimization and modelling. Various business problems where models bring added value are discussed. The students will build and solve basic optimization models, as well as interpretate the results. In both modules, the students also visualize their findings and make recommendations based on them in a crisp and concise way.

Assessment Methods and Criteria

• valid for whole curriculum period:

  Based on participation, assignments, exam.

• applies in this implementation

  The course assessment will be based on the following components:

   Module 1

  Assignment 1: Reflection paper (20%)

  Assignment 2: Data analysis exercise 1 (30%)

   

  Module 2

  Assignment 3: Data analysis exercise 2 (20%)

  Exam (30%)

   All elements need to be attempted and passed. All assignments are individual.

  Attendance to 70% of the lectorials is mandatory to pass-see below regarding course schedule. In practice this means that a minimum of 2 out of the 3 lectorials for each module should be attended.

  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.

  
  
  
  

Workload

• valid for whole curriculum period:

  Lectures and workshops

  Individual assignments

  Exam
  

• applies in this implementation

  For Module 1:

  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)

   

  For Module 2:

   

  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

• applies in this implementation

  The course materials will be placed on MyCourses on Mondays before/after the lectorial including:

  ·         Lectorial slides;

  ·         Notes and/or videos detailing how to carry out different procedures;

  ·         Tasks for the week.

   Notes on the week’s tasks will be provided at the end of each week, on Fridays, as necessary.

  Each week-at the end of the lectorial-you will be directed to additional reading from these materials: book and articles (available via Aalto Library and Google Scholar).

  Module 1 

  Black, K. (2014) Business statistics for contemporary decision making (8^th ed.). Wiley.

   

  Alibasic, A., Upadhyay, H., Simsekler, M. C. E., Kurfess, T., Woon, W. L., and Omar, M. A. (2022). Journal of Big Data, 9(32).

  Chen, L., and Nath, R. (2018). Business analytics maturity of firms: an examination of the relationships between managerial perception of IT, business analytics maturity and success. Information Systems Management, 35(1), 62-77.

  Hansen, H. K., and Mühlen-Schulte, A. (2012). The power of numbers in global governance. Journal of International Relations and Development, 15(4), 455-465.

  Martinez, L. R. (2022). How much should we trust the dictator’s GDP growth estimates?. Journal of Political Economy, 130(10), 2731-2769.

  Saltz, J. S., and Dewar, N. (2019). Data science ethical considerations: a systematic literature review and proposed project framework. Ethics and Information Technology, 21(3), 197-208.

   

  Module 2

   Anderson, D. R., Sweeney, D. J., Williams, T. A., Wisniewski, M. (2009). An introduction to Management Science: Quantitative approaches to decision making. (11^th ed.). Cengage Learning EMEA.

   

  Chambers, J. C., Mullick, S. K., and Smith, D. D. (1971). How to choose the right forecasting technique. Harvard Business Review.

  Fleischmann, B., Ferber, S., and Henrich, P. (2006). Strategic planning of BMW’s Global Production Network. Interfaces, 36(3), 194-208.

  Lee, S., Lee. S., and Park, Y. (2007). A prediction model for success of services in e-commerce using decision tree: E-customer’s attitude towards online service. Expert Systems with Applications, 33, 571-581.

  Magee, J. F. (1964, July 1964). Decision trees for decision-making. Harvard Business Review.

  Schaeffer, S. E., and Sanchez, S. V. R. (2020). Forecasting client retention-A machine-learning approach. Journal of Retailing and Consumer Services, 52(2020) 101918.

  Zhang, S., and Song, H. (2018). Production and distribution planning of Danone Waters China Division. Interfaces, 48(6), 578-590.

  
  

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 : 2022-2023 Autumn II
  2023-2024 Autumn II

  Enrollment :

  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

  The course will be conducted only on campus and in person.

   The course 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.

   Module 1 sessions run during the first three weeks of Period II. Module 2 sessions follow during the latter three weeks of Period II.

  For the lectorials as well as for the course assignments (see sections 5 and 6 for further details), you will need your own laptop and Excel.

  Aalto students can use the Microsoft 365 services, which include Excel, on their home device. For accessing applications online or installing them follow the link: Microsoft 365 Services | Aalto University

  Please ensure that you have access to Excel before the start of the course.

  To make the most of this course, you need to take responsibility for your learning and fully engage in the learning process by:

  • Attending all lectorials and actively participating in the in-class activities;
  • Attending the optional drop-in sessions if you have any questions or require a re-cap of some element from each week’s lectorial;
  • Keeping up with the work by completing all reading and using the notes and tasks provided to practice and develop your competence in the practical application of the theoretical concepts and techniques taught;
  • Working independently on the course’s assignments (see sections 5 and 6 for details);
  • Proactively seeking assistance and clarifications;
  • Ensuring that your schedule permits you to attend all mandatory sessions and fully engage with the course.

   

  If in doubt about something, please contact the Responsible Teacher, Anastasia Koulouri (anastasia.koulouri@aalto.fi).

   

  
  

Details on the schedule

• applies in this implementation

  Session	Input: Date, Time	Topic(s) covered	Deliverable
  Module 1 Introducing data analytics
  1a	Lectorial: Monday 23.10.23 09:15-12:00	Introduction to the course   Data Analytics: emergence and evolution of the field   Types of analytics and their use. Key concepts   Descriptive statistics: Exploring a single categorical variable   Guest speaker: TBC	Assessment 1: Issued on 23.10.23 Due 03.11.23, 23:00
  1b	Drop-in (optional): Weds 25.10.23 10:15-11:45
  1c	Drop-in (optional): Fri 27.10.23 12:15-13:00
  2a	Lectorial: Monday 30.10.23 09:15-12:00	Descriptive statistics: Exploring a single numerical variable, two categorical/numerical variables   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 01.11.23 10:15-11:45
  2c	Drop-in (optional): Fri 03.11.23 12:15-13:00
  3a	Lectorial: Monday 06.11.23 09:15-12:00	Visualizing data.   Guest Speaker: Jan Brittner (Senior Consultant, EY Parthenon) on “Visualizing data analysis for business decisions”	Assessment 2: Issued on 06.11.23 Due 17.11.23, 23:00
  3b	Drop-in (optional): Weds 08.11.23 10:15-11:45
  3c	Drop-in (optional): Fri 10.11.23 12:15-13:00
  Module 2 Modelling to inform decision-making
  4a	Lectorial: Monday 13.11.23 09:15-12:00	Optimization	Assessment 3: Issued on 13.11.23 Due 24.11.23, 23:00
  4b	Drop-in (optional): Weds 15.11.23 10:15-11:45
  4c	Drop-in (optional): Fri 17.11.23 12:15-13:00
  5a	Lectorial: Monday 20.11.23 09:15-12:00	Decision Trees
  5b	Drop-in (optional): Weds 22.11.23 10:15-11:45
  5c	Drop-in (optional): Fri 24.11.23 12:15-13:00
  6a	Lectorial: Monday 27.11.23 09:15-12:00	Forecasting
  6b	Drop-in (optional): Weds 29.11.23 10:15-11:45
  6c	Drop-in (optional): Fri 01.12.23 12:15-13:00
  			Exam:                          On-campus, 05.12.2023 10:00-12:00
  			Exam re-sit:                          On-campus, 02.02.2024 14:00-16:00

  
  

Description