Data Science for Business Leaders
Lecture Notes
Preface
About Data Science for Business Leaders
This course provides an overview of the tools and methods that data science offers to business leaders. Of course, we only scratch the surface on many topics. Many topics require more in-depth analysis to fully grasp the underlying principles and their benefits. In my opinion, business leaders often don’t understand every detail of the decisions they make. They can’t do that and often they don’t need to. However, they are good at identifying and prioritizing the key factors that can drive success. They have a sense of the essential elements that have a significant impact. Ultimately, leaders lead. They recognize, cultivate and manage the expertise of their employees and teammates. They orchestrate the knowledge around them, they lead their teams effectively and help them avoid critical missteps.
About the cover and the logo of the notes
I realize that having a visually appealing cover and logo does not directly help you become an exceptional leader. The images themselves do not teach you how to master the tools of data science or apply its methods effectively. However, I do believe that attractive visual elements appeal to many students and individuals. Therefore, as a teacher, I think it is wise to occasionally incorporate these elements into my notes. This approach may encourage you to continue reading and studying the material. By the way, it only took me a couple of minutes to create these two images using ChatGPT.
About the notes
- These notes aims to support my lecture at the HS Fresenius but are incomplete and no substitute for taking actively part in class.
- I hope you find this book helpful. Any feedback is both welcome and appreciated.
- This is work in progress so please check for updates regularly.
- These notes offer a curated collection of explanations, exercises, and tips to facilitate learning R without causing unnecessary frustration. However, these notes don’t aim to rival comprehensive textbooks.
- These notes are published under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. This means it can be reused, remixed, retained, revised and redistributed as long as appropriate credit is given to the authors. If you remix, or modify the original version of this open textbook, you must redistribute all versions of this open textbook under the same license.
- I host the notes in a GitHub repo.
Structure of these notes
About the author
Prof. Dr. Stephan Huber
Hochschule Fresenius für Wirtschaft & Medien GmbH
Im MediaPark 4c
50670 Cologne
Office: 4e OG-3
Telefon: +49 221 973199-523
Mail: stephan.huber@hs-fresenius.de
Private homepage: www.hubchev.github.io
Github: https://github.com/hubchev
I am a Professor of International Economics and Data Science at HS Fresenius, holding a Diploma in Economics from the University of Regensburg and a Doctoral Degree (summa cum laude) from the University of Trier. I completed postgraduate studies at the Interdisciplinary Graduate Center of Excellence at the Institute for Labor Law and Industrial Relations in the European Union (IAAEU) in Trier. Prior to my current position, I worked as a research assistant to Prof. Dr. Dr. h.c. Joachim Möller at the University of Regensburg, a post-doc at the Leibniz Institute for East and Southeast European Studies (IOS) in Regensburg, and a freelancer at Charles University in Prague.
Throughout my career, I have also worked as a lecturer at various institutions, including the TU Munich, the University of Regensburg, Saarland University, and the Universities of Applied Sciences in Frankfurt and Augsburg. Additionally, I have had the opportunity to teach abroad for the University of Cordoba in Spain, the University of Perugia in Italy, and the Petra Christian University in Surabaya, Indonesia. My published work can be found in international journals such as the Canadian Journal of Economics and the Stata Journal. For more information on my work, please visit my private homepage at hubchev.github.io.
I was always fascinated by data and statistics. For example, in 1992 I could name all soccer players in Germany’s first division including how many goals they scored. Later, in 2003 I joined the introductory statistics course of Daniel Rösch. I learned among others that probabilities often play a role when analyzing data. I continued my data science journey with Harry Haupt’s Introductory Econometrics course, where I studied the infamous Jeffrey M. Wooldridge (2002) textbook. It got me hooked and so I took all the courses Rolf Tschernig offered at his chair of Econometrics, where I became a tutor at the University of Regensburg and a research assistant of Joachim Möller. Despite everything we did had to do with how to make sense out of data, we never actually used the term data science which is also absent in the more 850 pages long textbook by Wooldridge (2002). The book also remains silent about machine learning or artificial intelligence. These terms became popular only after I graduated. The Harvard Business Review article by Davenport & Patil (2012) who claimed that data scientist is “The Sexiest Job of the 21st Century” may have boosted the popularity.
The term “data scientist” has become remarkably popular, and many people are eager to adopt this title. Although I am a professor of data science, my professional identity is more like that of an applied, empirically-oriented international economist. My hesitation to adopt the title “data scientist” also stems from the deep respect I have developed through my interactions with econometricians and statisticians. Considering their in-depth expertise, I feel like a passionate amateur.
Ultimately, I poke around in data to find something interesting. Much like my ten-year-old younger self who analyzed soccer statistics to gain a deeper understanding of the sport. The only thing that has changed since then is that I know more promising methods and can efficiently use tools for data processing and data analysis.
To students
I’m not a business leader myself. I’m a professor, and like most of my peers, I don’t run a big company nor do I hold a top position in a multinational corporation. Actually, I believe that leading a company successfully full-time is not compatible with being a full-time professor, and vice versa. Despite this, in my role as a professor and study program director, I do have certain responsibilities and occasionally lead people, particularly students. This, however, doesn’t let me feel like I am a business leader. Is this necessary to teach you a course entitled “Data Science for Business Leaders”? I don’t think so: Firstly, if you want to become a business leader, do not pick a role model that works in academics like I do. Secondly, I believe I can provide you with valuable insights into data science to support you on the journey of becoming a business leader. And thirdly, this course is not entitled “I am a business leader who teaches you data science”. Thus, this course is “for” business leaders and “for” you and not about me or my personality.
Enjoy.