Principles and Techniques for Data Science
11 credits
- Introduction: different components of the data science life cycle
- Classification of different methods for data processing: retrieval, structuring
- Cleaning, transformation, etc., and description of various data types
- Introduction of different paradigms for exploratory data analysis, such as statistics
- Analysis, scores, ranking, hypothesis testing, and data visualization
- Overview of data mining techniques for understanding trends, outliers, and patterns from large amounts of data
- Presentation of various methods for predictive modeling
- Introduction of data science tools: programming, computing environments, and big data infrastructures
- Presentation of data ethics: privacy, security, fairness, bias, and interoperability
- Providing guidance on how to do data science project
