Python, SQL, Tableau for Data Science (berufsbezogenes Zertifikat)

Master Python programming for data analysis in this comprehensive course designed for aspiring data scientists. Through hands-on projects using real-world datasets, you'll learn essential data manipulation, visualization, and statistical analysis techniques while integrating modern AI tools and version control practices.

This course is perfect for analysts and professionals who want to advance beyond spreadsheets to powerful programming solutions. Starting with Python fundamentals and progressing through advanced analysis techniques, you'll develop practical skills that directly apply to real-world data challenges. Upon completion, you'll be able to: • Import, clean, and manipulate data using Python's powerful libraries (Pandas, NumPy) • Create compelling visualizations with Matplotlib, Seaborn, and Plotly • Perform statistical analysis and A/B testing for data-driven decisions • Automate data workflows and generate professional reports • Implement version control best practices using GitHub

Leverage modern data visualization tools in this comprehensive course designed for business professionals and analysts. Through hands-on projects using real-world datasets, you'll learn to create compelling visualizations and interactive dashboards using industry-standard tools like Tableau and Excel, enhanced by AI-driven insights.

This course is perfect for analysts and business professionals who want to transform complex data into clear, actionable insights. Starting with fundamental visualization principles and progressing to advanced dashboard creation, you'll develop the practical skills needed to communicate data stories effectively across any organization. Upon completion, you'll be able to: • Create powerful visualizations and interactive dashboards using Tableau and Excel • Design and implement custom calculations and metrics for deeper analysis • Build compelling data stories through narrative visualization techniques • Optimize dashboard performance and implement real-time updates • Leverage AI tools to enhance visualization design and automation

Learn advanced machine learning techniques and cloud deployment in this comprehensive course designed for data professionals. Through hands-on projects, you'll learn to build, evaluate, and deploy sophisticated machine learning models using AWS services, while leveraging AI tools to enhance your workflow.

This course is perfect for data analysts and scientists ready to advance their machine learning capabilities and gain practical experience with cloud computing. Starting with advanced ML concepts and progressing through AWS integration, you'll develop the technical expertise needed to implement enterprise-level data science solutions. Upon completion, you'll be able to: • Build and evaluate sophisticated machine learning models using advanced techniques • Deploy scalable solutions using AWS SageMaker and related services • Perform advanced feature engineering with AI assistance • Implement time series analysis and unsupervised learning methods • Create end-to-end machine learning pipelines in the cloud

Transform theoretical knowledge into practical expertise in this comprehensive project-based course designed for aspiring data professionals. Through an end-to-end project using synthetic customer support data (designed to mirror real-world scenarios) , you'll integrate advanced analytics, cloud computing, and AI-assisted development to solve authentic business challenges. Leveraging AWS services throughout the project, you'll work with S3 for data storage and management, utilize SageMaker for model development and deployment, and create automated data pipelines—gaining hands-on experience with industry-standard cloud tools.

Upon completion, you'll be able to: • Design and implement end-to-end data science solutions • Build automated data pipelines with AWS integration • Create production-ready machine learning models • Develop interactive dashboards and reports • Generate comprehensive project documentation