Financial Data Science
(Last update: 2023-04-18)
Chapter 1 Welcome!
Here you will find the course pages for the projectcourse Financial Data Science.
The course is offered regularly in the summer term and aims at providing in-depth knowledge about the programming language Python and its most important libraries for data analysis. Furthermore, the course introduces the topic 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. The final grade will be determined based on a case study of a financial dataset.
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 will also collaborate directly with the financial industry and the local FinTech scene.
1.2 Our Courses
We offer the following courses at LMU university. Please visit the institute’s webpage for more granular information and course details.
Bachelor Courses
| Code | Name | ECTS | Semester |
|---|---|---|---|
| tba | Digital Finance - Capital Markets | 6 | Summer |
| tba | Digital Finance - Investments | 6 | Winter |
| 48081 | Topics in Financial Innovation & Technology | 6 | Winter Summer |
| tba | Bachelor Thesis | 18 | Winter Summer |
Master Courses
| Code | Name | ECTS | Semester |
|---|---|---|---|
| tba | Financial Data Science | 12 | Summer |
| tba | Advanced Digital Finance | 12 | Winter |
| tba | Financial Technology in Consumer Finance | 6 | Summer |
| tba | Advanced Risk Management | 6 | Winter |
| tba | Master Thesis | 30 | Winter Summer |
1.3 How to use this Book
This book will be your only study material. It does make sense to read the texts carefully and replicate the code examples within the workbook.
We require you to submit three homeworks in order to pass the course. The assignments are not graded and mainly serve the purpose of you becoming comfortable with the coding languages and concepts. You can find the assignments at the end of each chapter within this course, the submittment is done via the corresponding Moodle section.
When submitting your work, we highly encourage the use of notebooks as they offer an intuitive way of combining text and code sections. You might want to look into Jupyter Notebooks or R Markdown, as they allow for a clean presentation of written text, code and code output.
Preliminary Timetable
We will have meetings on the following dates, which will be a mix of learning about programming concepts and interactive coding applying the newly aquired knowledge.| 05.05.2023 | Structured Query Language |
| 12.05.2023 | Python Introduction |
| 19.05.2023 | Statistical Programming with Python |
| 26.05.2023 | Big Data with Python |
| 15.08.2023 | Submission of final project via Moodle |
The Role of Google
Throughout this course, we will do our best to provide all relevant materials and concepts to solving the tasks at hand. However, this document might not cover all the necessary concepts to solve the provided exercise sets. An vast amount of knowledge will be gained by searching for solutions for your problems online.
Learning how to code is a process and therefore involves designing code snippets, resolving errors and optimizing fractions. You will spend a lot of time searching for bugs and their solutions in the internet. Do not get frustrated - we have all been there and you will learn as you progress.
A good source in order to look for coding solutions is Stackoverflow.