Chapter 1 Welcome!

Here you will find the course pages for the projectcourse Financial Data Science.

The projectcourse is offered regularly in the winter and summer term and aims at providing in-depth knowledge about the programming language Python and its most important libraries for data analysis. Each summerterm, the course is taught in cooperation with the Institute for Finance & Banking and consists of two parts.

Each winterterm, the course extends the introduction of programming language Python with a comprehensive overview of database management and the process of retrieving, aggregating and manipulating data using SQL. Students will learn to develop structured approaches for solving practical problems in a financial context using statistical methods.

1.1 Our Institute

We are a new institute at LMU Munich School of Management and conduct academically rigorous, relevant and exciting research and teaching in the domain of financial innovation and technology. We cover topics like high frequency trading, sustainable finance, cryptocurrencies, financial data science and the application of machine learning in capital markets. We present our research at international conferences and publish in leading journals. We also collaborate directly with the financial industry and the local FinTech scene. Find out more about us and the institute on Webpage.

We offer the following courses at LMU university. Please visit the institute’s webpage for more granular information and course details.

Bachelor Courses

Name ECTS Semester
Digital Finance - Capital Markets 6 Summer
Topics in Financial Innovation & Technology 6 Winter Summer
Bachelor Thesis 18 Winter Summer

Master Courses

Name ECTS Semester
Financial Data Science 12 Summer Winter
Financial Technology in Consumer Finance 6 Summer Winter
Master Thesis 30 Winter Summer

1.2 How to use this Book

This book will be your only study material for the projectcourse. Therefore, we expect you to read the texts carefully and replicate the code examples within the book. The end of each chapter contains exercises to deepen your knowledge about the newly learned concepts. This way, you will become familiar with the coding concepts and the language’s syntax. Each chapter contains relevant exercises to put your newly acquired knowledge into practice.

For following the course structure, we recommend the use of IPython notebooks (.ipynb files) or RMarkdown (.rmd files) for your coding snippets. Both file types offer an intuitive way of combining text and code sections by allowing for a step-by-step execution of code and an immediate output. Additionally, code can be annotated easily to ease understanding of programming concepts and document your learning journey. You can find out more by checking out Jupyter Notebooks or R Markdown.

Course Outline

The course is taught interactively by presenting standard coding concepts and subsequently have students directly apply the newly acquired knowledge in interactive coding sessions.
First Chapter Structured Query Language
Second Chapter Python Basics
Third Chapter Case Study: Studying Trades
Fourth Chapter Case Study: Studying Quotes

The Role of Google

Throughout this course, we will do our best to provide all relevant materials and concepts to solve the exercises at hand. However, this document might not cover all ultimately necessary concepts to solve the provided exercises. A vast amount of your coding knowledge will be gained by searching for solutions to your problems online.

Learning how to code is a process and therefore involves designing code, resolving errors, revising and optimizing code fragments. You will spend a lot of time debugging your code and searching for solutions in the internet. A good source in order to look for coding solutions is Stackoverflow.

Do not get frustrated - we have all been there and you will learn as you progress with time and experience.