Understand the full data science lifecycle
Learn how to turn a problem into a data science issue
Explore different roles, analysis types, and tools
Recognize what a model is and how to use it
Learn why math is essential to data science
Recognize and apply basic functions in analytics
Get a grip on linear algebra, matrices, and calculus
Explore logic, set theory, and graph theory
Understand optimization and how to use it in models
Translate a business question into a data analysis
Work with stakeholders and determine valuable insights
Select the right data and determine success criteria
Load datasets and explore content
Clean, typecast, and combine data with merge and join
Learn how to deal with missing values
Understand what a feature is and how to build it
Assess the relevance and strength of your features
Use basic classification, regression, and clustering algorithms
Apply them to realistic datasets
Explore neural networks and ensemble methods
Understand deep learning and convolutional networks
Test the reliability of your model
Work with validation methods such as Holdout and K-Fold
Choose the right evaluation metrics
Live models with Docker and cloud services
Learn about governance and monitoring performance
Weigh which model works best in practice
Sometimes we are also surprised at the prices in the market. Because our goal is ultimately to reform education, we are not looking for the highest possible profit margins. We also keep your investment under control by offering smart digital education and making good use of our community.