We build the foundations of ethics and explore how the different phases of the modelling cycle can be biased and how to counter-act this bias. We explore successively: the data, the model and the model implementation.
This course offers an engaging and practical exploration of unconscious biases and their impact on decision-making, communication, and teamwork. Students will develop a deeper understanding of how biases form and how they can unconsciously influence behavior in professional and personal settings. Through interactive content and actionable strategies, participants will learn to recognize and mitigate bias, fostering greater fairness, inclusion, and collaboration. By the end of this course, students will be equipped with tools to make more objective decisions, improve interpersonal relationships, and contribute to a more equitable environment in their workplace and community.
Take charge of your future with our dynamic course! Dive into personal development, master the art of career growth, and create a powerful personal development plan. Plus, learn how to write an effective CV that opens doors to new opportunities. Elevate your career to the next level with us!
| lecture | lecture description |
|---|
| Explore your career |
Unlock your potential! Reflect on your career, strengths, and what makes you thrive. Plan your future, enhance personal development, and discover your life purpose. Whether starting out or seeking change, this workshop unlocks your roadmap to success. Join us and transform your career journey! |
| Using AI for career development |
Ideas to boost your career with generative AI. |
| Leadership |
Leadership transends management by one crucial point, and it is not inspiring vision. Learn the essential ingredients to leadership, management, and teamwork. |
Short module about innovation.
We start answering questions about what is innovation, how we can make our organisation the learning and innovative organsiation that we want to work for. We explore the history of innovation and use that knowledge to look into the future.
The data-science part of the course is based on "The big R-book: from data science to learning machines and big data. The homepage of the book is here.
| lecture | lecture description |
|---|
| Introduction to the course |
An introduction to your teacher, layout of the course, learning goals, agreements, etc. |
| The history of innovation |
Explore capitalism’s evolution through history, exponential growth, and innovation waves—from banking’s roots to today’s AI-driven era. Dive into emerging frontiers like quantum computing, biotech, and nanotech.
Inspired by 'The Big R-Book: From Data Science to Learning Machines and Big Data'. |
| Multi Criteria Decision Analytics |
Multi Criteria Decision Analytics is the art of making an informed choice in a situation where multiple competing criteria make it impossible to find one best soluiton (for example good quality comes at a high price - and we want a low price and high quality). |
| Building Models in R |
This course transitions from data preparation to practical model-building, covering foundational statistical methods (linear regression, generalized linear models like logistic regression) and key machine learning techniques (decision trees, random forests, support vector machines, neural networks, and k-means clustering). Focused on implementation, it bridges theory with real-world application. |
| Introduction to Large Language Models |
An overview, tailored for managers, of how large language models actually work and what one can expect from them. |
| Applications of AI in banking |
A deep dive about artificial intelligence and how it can be used in banking, including a practical look at pitfalls, methods and company culture. |
| Introduction to Companies |
This course explores the fundamentals of wealth creation in private enterprises, linking financial statements (balance sheets, profit/loss statements) to company valuation. It introduces core concepts of valuing businesses and connects these principles to financial markets, covering instruments like equities, bonds, options, and futures. Focused on practical insights, it bridges corporate finance theory with real-world market applications. |
| Bias |
Exploring bias in our perception, reasoning, and decission making processes |
| Ethics |
An introduction to Ethics. What is it? What is ethical and what not? How does the refernce point of view our judgement? |
| Bias in data |
Recognising bias in data and models and building robust, unbiased models. |
| Introduction to Quantum Computing |
An accessible introduction to Quantum Computing. |
| Applications of Quantum Computing in Banking |
Preparing for the quantum era from the banker's point of view. |
| Introduction to Bitcoin and Blockchain |
Understanding what the hype is all about. |
| Introduction to Big Data |
Understanding what Big Data is and how it is essential in today's world. |
| Introduction to Crowd Funding |
Understanding the basics of crowd funding and how to use it to your advantage. |
In this program we focus on a selection of the material presented in the boook "The big R-book: from data science to learning machines and big data." We start with introducing the staticistical programming language R and use it to wrangle data, build models, verify models and builds reports.
The homepage of the book is here.
| lecture | lecture description |
|---|
| Agreements and Introduction program |
Explain how the course will work, how we work together to the final presentations, how the scores are determined, etc. |
| The history of innovation |
Explore capitalism’s evolution through history, exponential growth, and innovation waves—from banking’s roots to today’s AI-driven era. Dive into emerging frontiers like quantum computing, biotech, and nanotech.
Inspired by 'The Big R-Book: From Data Science to Learning Machines and Big Data'. |
| Getting started with R |
In this module we get started using R and RStudio. This module introduces you to the language R. |
| Importing Data in R |
In this module we learn the basics of databases in general and relational databases in particular. Then we learn how to import data from SQL databases directly into R. |
| Data Wrangling in R |
Raw database data is rarely analysis-ready. Master essential preprocessing skills—feature engineering (adding columns, calculations), data cleaning (missing values, normalization), and transforming dates, strings, and bins—to transform raw data into a goldmine for modeling. |
| Building Models in R |
This course transitions from data preparation to practical model-building, covering foundational statistical methods (linear regression, generalized linear models like logistic regression) and key machine learning techniques (decision trees, random forests, support vector machines, neural networks, and k-means clustering). Focused on implementation, it bridges theory with real-world application. |
| Introduction to Companies |
This course explores the fundamentals of wealth creation in private enterprises, linking financial statements (balance sheets, profit/loss statements) to company valuation. It introduces core concepts of valuing businesses and connects these principles to financial markets, covering instruments like equities, bonds, options, and futures. Focused on practical insights, it bridges corporate finance theory with real-world market applications. |
| Automated Reporting in R |
This course focuses on transforming data insights into actionable outcomes through effective communication. Using R, RMarkdown, and Shiny, you’ll master automated workflows—from data import and analysis to generating dynamic reports, slides, static websites, and interactive dashboards. Learn to seamlessly integrate code, text, and visuals in reproducible documents, ensuring your findings drive informed decisions. |
| Bigger Data and Faster Code |
This course addresses the computational challenges of large-scale data by teaching scalable processing techniques. Learn to optimize performance through multi-core CPU utilization, GPU acceleration, distributed systems (e.g., Apache Spark), and efficient coding practices—including clean code design and integrating compiled languages like C++ into R workflows. Balance hardware scalability with software optimization to tackle real-world big data demands. |
| Ethics |
An introduction to Ethics. What is it? What is ethical and what not? How does the refernce point of view our judgement? |
| Bias in data |
Recognising bias in data and models and building robust, unbiased models. |
| Ideas for the end-projects |
The end-project is making a model, cross-validating it and reporting back. To do that, you will need data. Feel free to bring your own data to the party, but in case you struggle to find good sources, here are some ideas. |