- Intensive exploration of supervised learning methods, including regression, classification, and the application of these techniques to real-world data sets.
- Career Development in Data Professions: Strategies and tips for career planning and development in the data industry, including networking, interviewing and continuous learning.
- Group instruction and self-study: Combination of structured group instruction and flexible self-study modules to maximize the learning experience and accommodate individual learning needs.
- Machine Learning Fundamentals: Introduction to the basic concepts and techniques of machine learning to lay the foundation for further studies and applications.
- Model interpretation and big data with PySpark: Using PySpark to process and analyze large amounts of data and techniques to interpret model results to support decision making.
A data scientist analyzes and interprets large amounts of data in order to make informed decisions. Tasks include developing analysis methods, identifying patterns and trends, processing and analyzing unstructured data, implementing automation and setting up machine learning algorithms and AI. You will work closely with other departments to optimize business processes. The skills in analysis, programming and machine learning acquired during the training are essential to be successful as a data scientist.
Data Scientists nutzen Technologien wie Artificial Intelligence, Deep Learning und neuronale Netze, um komplexe Daten zu analysieren. Maschinelles Lernen, einschließlich Random Forests und Unsupervised Learning, ist zentral für predictive Analytics. Python-Tools wie Pandas und Module für Unit Testing spielen eine wichtige Rolle bei der Datenverarbeitung und im Test Driven Development.
Data Modelling, Clusteranalysen und Varianzanalysen unterstützen die statistische Auswertung. Weiterbildungen in IT und Informatik fördern analytisches Denken und technische Fähigkeiten, während ein Informatikzertifikat die Karrierechancen im Bereich Data Science erhöht.