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.