Data Science Essentials

Course overview

Data with a focus on Science Essentials 1. Introduction to Data Science Fundamentals: Definition and significance of Data Science, overview of the Data Science workflow, introduction to key tools and technologies 2. Data Preparation and Cleaning: Techniques for data cleaning and preprocessing, using Pandas and Numpy for data manipulation, best practices for data preparation 3. Exploratory Data Analysis (EDA): Techniques for exploratory data analysis, data visualization with Matplotlib and Seaborn, identification of patterns and trends in data 4. Introduction to Machine Learning: Overview of various machine learning models, supervised vs. unsupervised learning, implementation of simple models with scikit-learn 5. Model Evaluation and Optimization: Metrics for evaluating models, techniques for hyperparameter optimization, best practices for model improvement 6. Practical Projects in Data Science: Conducting projects to apply what has been learned, group work to solve real-world challenges, feedback and suggestions for improvement 7. Future Trends in Data Science: Discussion on upcoming trends in Data Science, preparation for future challenges, strategies for continuous improvement 8. Ethics and Data Protection in Data Science: Introduction to ethical standards and data protection, case studies on ethical dilemmas in Data Science, development of an ethical framework for Data Science projects

Upcoming dates

• 2026-08-23T22:00:00.000Z · 2027-03-03T23:00:00.000Z · Virtuelles Klassenzimmer
• 2026-08-23T22:00:00.000Z · 2027-08-22T22:00:00.000Z · Virtuelles Klassenzimmer
• 2026-08-23T22:00:00.000Z · 2027-03-03T23:00:00.000Z · München · Combined Learning
• 2026-08-23T22:00:00.000Z · 2027-08-22T22:00:00.000Z · München · Combined Learning