Module overview
Module 1 - Use Cases and Process Models
In the first module, we will focus on a range of use cases in which data plays a key role. We will discuss the requirements of generating value from data. We will consider the relevant roles in data-based projects and how interdisciplinary teams can best collaborate.
In a next step, we will explore the structure of data-based projects, looking into each stage and its perfect execution. We will discuss typical challenges and familiarize ourselves with various approaches that guarantee the successful completion of a data science project.
Module 2 - Communication, Stakeholder Management and Compliance
In module 2, we will share ways of collaborating with stakeholders and managers at various levels. We will learn methods to communicate data-specific topics at different managerial levels to convince the people involved of the importance of a project or its funding.
We will also investigate the legal aspects relevant to the management of data science projects. We will learn to work with data in compliance with the law, specifically with data protection legislation. Finally, we will discover ways to find out where data science can create the most added value in a company’s business process landscape (principles of data governance).
Module 3 – Data Engineering
In this module, we will start our journey by studying Python.
We will engage with the foundational concepts and techniques of data engineering, that is, with the question of how to draw data from different systems. We will learn to extract data from different sources (tabular data, unstructured data such as images, audio or log files, etc.), to analyze and to understand them. We will conclude the module by discussing the foundational concepts of databases and SQL (language to access structured data stored in a database).
Module 4 – Data Science Models and Cloud Tools
Module 4 focuses on loading and visualizing data (Python libraries pandas and matplotlib). We will discuss the most popular libraries for machine learning (e.g., scikit-learn).
The participants will learn the most commonly used machine learning algorithms for predictions. You will work on practical exercises in the fields of supervised (such as linear regression) and unsupervised learning (such as clustering and anomaly detection).
This module teaches the basic principles of cloud technologies like Kubernetes and virtualization. Specifically, you will familiarize yourself with MS Azure, Google Cloud and Amazon Web Services. Finally, you will learn which solutions are best suited for an existing software landscape. To round things off, we will study real-life corporate use cases.
Transfer project
In the transfer project, the participants work with a real-life dataset, typically in teams of two.
The process of completing the transfer project comprises the following stages:
- The participants either choose an existing project/problem from their professional practice or define one based on their personal interests.
- They discuss the idea with the lecturers, who directly approve it or suggest changes.
- During the program, the participants are given the time to work their project supervised by their lecturers.
- The participants present their projects on the final day of the CAS program.
The goal is for the participants to gain experience with a real-life project, creating added value for their CV and their companies alike.