Data-Informed Business Strategies
Data-Informed Business Strategies |
| Semester & Location: |
Fall 2024 - DIS Copenhagen |
| Type & Credits: |
Elective Course - 3 credits |
| Major Disciplines: |
Business, Economics, Computer Science, Data analysis, Econometrics, Decision Making |
| Prerequisite(s): |
"None" |
| Faculty Members: |
Nicola Menale |
| Program Contact: |
Nicola Menale(current students contact via Canvas inbox) |
| Time & Place: |
Mondays and Thursdays 18:00-19:20 Classroom: F24-503 |
Course Description
To succeed in today's data-driven, fast-paced business landscape you need the ability to make fast decisions. In situations where going with your gut feeling is not enough, you need stronger arguments to back your decisions. Here data can help you in informing and supporting your decision-making.
This course will teach you how to build the bridge between business challenges and data-informed decisions. You will learn to develop your own framework that easily translates complex questions into simple hypotheses that can be tested using data and analytics.
The course aims at teaching students how to use simple yet powerful frameworks for tackling strategic business challenges and supporting their decision-making by using data to inform those decisions.
Major Disciplines/Topics
Major Disciplines include Business, Economics, Computer Science, Data analysis, Econometrics, Decision Making. We will approach all those disciplines during the semester finding for each of them a practical application in real job-related environment.
Learning Objectives
By the end of this course students will be able to carry on a complete business project by themself:
- Properly define the scope of a project
- Set clear expectation with the clients
- Distinguish, understand and apply proper data analysis tools to carry on the project
- Make a complete data analysis using excel/python/other statistical tools
- Make a state of the art report in Tableau/PowerBI/PowerPoint
- Translate data into a story to convey to your customer
- Being able to explain results from the report
Faculty
Nicola Menale studied Business Economics in Vanvitelli University (Italy) in 2011 (with Thesys focused in Game Theory & Microeconomics). MSc in Economics and Social Sciences in Bocconi University (Italy) in 2013 with Thesys focused in Game theory & Decision Making. Master in Data Science at Nicoló Cusano University (Italy) in 2020 with Thesys focused in Deep Neural Network. Since 2013 he is working in Copenhagen in the data analysis field. He worked for International companies as Oticon A/S, Hollister inc., Radiometer A/S and he is currently working in Danske Bank as Senior Business Intelligence Analyst. Nicola is part of the DIS family since September 2019. His motto is "In God we trust, all the rest must bring data".
Readings
- L.L. THOMPSON, The mind and heart of the negotiator, Upper Saddle River, NJ, Prentice-Hall 2004 ( https://www.pearsonhighered.com/assets/preface/0/1/3/5/0135197996.pdf)
- Daniel Khaneman, Thinking fast and slow, Farrar, Straus and Giroux 18 West 18th Street, NewYork 10011 (http://dspace.vnbrims.org:13000/jspui/bitstream/123456789/2224/1/Daniel-Kahneman-Thinking-Fast-and-Slow-.pdf)
- Statistical method and Data analytics UCLA (https://stats.oarc.ucla.edu/spss/whatstat/what-statistical-analysis-should-i-usestatistical-analyses-using-spss/)
- Wes McKinney, Josef Perktold, Skipper Seabold, Time Series Analysis in Python with statsmodels (https://www.researchgate.net/profile/Josef-Perktold/publication/340142416_Time_Series_Analysis_in_Python_with_statsmodels/links/5ef23e75299bf1031f1bf557/Time-Series-Analysis-in-Python-with-statsmodels.pdf)
- Sadrach Pierre, Mastering Time Series Analysis with Python Classes (https://towardsdatascience.com/mastering-time-series-analysis-with-python-classes-1a4215e433f8)
- Eryk Lewinson, Verifying the Assumptions of Linear Regression in Python (https://towardsdatascience.com/verifying-the-assumptions-of-linear-regression-in-python-and-r-f4cd2907d4c0)
- Course slides
Field Studies
The field studies will bring students in contact with experienced users of data analysis tools to bridge the gap between the theory and the practical work environment.
Guest Lecturers
Professor Nikolaj Opstrup will hold 2 guest lecturers to expand students knowledge and go deeper into test of hypotesys and usage of datascience tools for making business related decision.
Approach to Teaching
In order to make our classes productive, it is important that you are well prepared. Preparation will usually consist of going through readings/videos and / or practical exercises in Excel, Python and Tableau. In the calendar and in announcements you will see what there is to do.
The approach will be extremely practical: students will have the opportunity to work on real data from project coming from big danish companies such as Oticon A/S (www.oticon.dk), Radiometer A/S (www.radiometer.dk), Danske Bank A/S (www.danskebank.dk) and others.
Moreover the students will have the opportunity to visit Danske bank facility and interact with higly skilled professional employees. The employees will show students how we use the tools explained in class.
Expectations of the Students
Students are expected to actively participate in class. The class will be mostly a discussion between me and the students trying to understand what are the problems to overcame in an everyday job life of a professional data analyst/data scientist. The students are expected to understand the content of the arguments discussed in class and being able to apply the concept to solve real job-life problems: after this course students will be able to carry on a data analyst project by themself
Evaluation
Students will be evaluate on the engagement/partecipation, and on the quizzes/exercises and on the final project they will deliver.
| Assignment |
Percent |
| Engagement/participation |
20% |
|
Various quizzes/exercises |
50% |
|
Business case project |
30% |
Engagement/participation in class: 20%
Your participation grade will be determined by 4 factors: attendance, preparedness for class, active engagement in class including field studies and groupwork perceived participation (see more info below). You are required to attend all classes. If you miss a class, you must contact an instructor as soon as possible (before the class starts) and provide an explanation. The assigned readings for each lecture should be read prior to the class.
Various quizzes and exercises: 50%
Several quizzes are meant to evaluate your progress during the course. The quizzes refer to the assigned readings and to what we did in class. The answers have to be submitted electronically via Canvas. The quizzes will evaluate the overall understanding of the subject and not how much the student can remember. Therefore all the quizzes will be open-book
Business case project: 30%
In your research project towards the end of the semester, you will apply data-related tools to complete a business case of our choice. The project will be done in small groups. It will consist into a real life presentation to a client of data, conclusions and complete analysis of the results.
During the semester I will appoint some group representatives (one for each group). I will reserve 10 minutes before every class to check on the progress for each group. At the end of the semester each student will be asked to evaulate the participation of every group component to evaulate the overall perceived workgroup participation (making sure that all the students equally participate to the final assignment). This will be taken into serious consideration in assigninig the Engagement/participation in class evaluation.
Academic Regulations
Please make sure to read the Academic Regulations on the DIS website. There you will find regulations on:
Academic Regulations
Please make sure to read the Academic Regulations on the DIS website. There you will find regulations on:
DIS - Study Abroad in Scandinavia - www.DISabroad.org
Course Summary:
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