Principles and Techniques for Data Science

- 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