Data Science and ML Engineering in the Age of AI

Course overview

Objective The course "Data Science and ML Engineering in the Age of AI" teaches methods of Data Science and their transition into productive Machine Learning systems. It covers the process from data preparation through model development to the operation of models in a live system. The course is aimed at individuals who connect Data Science with tasks of ML Engineering. Additionally, it contextualizes the role of generative models in Data Science projects. Content (Selection) - Data Science: Data preparation, exploratory analysis & feature engineering - Statistical fundamentals and model evaluation in Data Science - Machine Learning methods: Supervised, Unsupervised & Deep Learning - From Data Science to ML Engineering: Transitioning models to production - MLOps: Versioning, deployment & monitoring of models - Data pipelines and infrastructure for Data Science applications - Use of LLMs and foundation models in Data Science projects - Model quality, bias & reproducibility - Data protection and legal requirements in Data Science projects - Collaboration between Data Science, Engineering & business units. Working Method The course combines the methodological foundations of Data Science with implementation in code. Exercises and case studies represent the individual steps of the model lifecycle. Knowledge of Python is required. Competencies (Outcome) - Analyze & prepare datasets using Data Science methods - Develop & evaluate Machine Learning models - Transition & operate models in productive environments - Use MLOps tools for versioning and monitoring - Consider requirements for data protection, model quality & reproducibility. Career Perspectives and Practical Application Data Science and ML Engineering are utilized in companies across various industries, such as retail, manufacturing, finance, and logistics. Typical job roles include Data Scientist, ML Engineer, and Data Engineer. Responsibilities include developing forecasting and classification models, building data pipelines, and operating and monitoring models in production. Tools (Examples) Python, pandas, scikit-learn, PyTorch, MLflow, Docker, Git, SQL Summary The course covers Data Science and ML Engineering throughout the entire model lifecycle. Participants work with data, models, and the tools for their operation.

Upcoming dates

• 2026-10-27T23:00:00.000Z · 2027-01-18T23:00:00.000Z · Virtuelles Klassenzimmer
• 2026-10-27T23:00:00.000Z · 2027-04-12T22:00:00.000Z · Virtuelles Klassenzimmer
• 2026-10-27T23:00:00.000Z · 2027-01-18T23:00:00.000Z · Köln · Hybrid Learning
• 2026-10-27T23:00:00.000Z · 2027-04-12T22:00:00.000Z · Köln · Hybrid Learning
• 2027-01-17T23:00:00.000Z · 2027-04-10T22:00:00.000Z · Virtuelles Klassenzimmer