Module overview
Module 1 - Use Cases and Process Models
The first module, will focus on a range of use cases in which data plays a key role and discuss the requirements of generating value from data. You will evaluate the relevant roles in data-based projects and how to best collaborate in interdisciplinary teams.
In a next step, you will explore the structure of data-based projects, looking into each stage and its ideal execution. You will discuss typical challenges and familiarize yourself with various approaches that guarantee the successful completion of a data science project.
Module 2 - Communication, Stakeholder Management and Compliance
The second module will share ways of collaborating with stakeholders and managers at various levels. You will learn methods to communicate data-specific topics at different management levels to successfully convince decision-makers of the importance of a project or its funding.
You will also investigate legal aspects relevant to the management of data science projects. You will to comply with the law, specifically with data protection legislation, when working with data.. Finally, you will discover ways to find out where data science can create the most added value in your company’s business process landscape (principles of data governance).
Module 3 – Data Engineering
In the third module, you will start our journey by studying Python.
You will engage with the foundational concepts and techniques of data engineering, that is, with the question of how to draw data from different systems. You 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. The module will conclude with a discussing of the foundational concepts of databases and SQL (language to access structured data stored in a database).
Module 4 – Data Science Models and Cloud Tools
The fourth module focuses on loading and visualizing data (Python libraries pandas and matplotlib). You 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 do practical exercises 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, you will study real-life corporate use cases.
Transfer project
In the transfer project, 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 time to work their project supervised by 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, boosting for their CV and creating value for their companies.
