Lecture Applied data science: Exploratory data analysis

Lecture 'Applied Data Science: Exploratory Data Analysis' is a comprehensive guide on definitions, data types, steps in EDA, real-life applications, and more. It provides valuable insights into various aspects of EDA, including balance between theory and practice, regression, classification, confirmation, adjustment, grouping, evaluation, implementation, and ethics.

Sinh viên, nhà nghiên cứu, doanh nghiệp, luyện tập viên data science và những người quan tâm đến khoa học dữ liệu.

Lecture 'Applied Data Science: Exploratory Data Analysis' delves deep into the world of data analysis. It begins with an overview, followed by its application, EDA, learning process, balance between theory and practice, regression, classification, confirmation, adjustment, grouping, evaluation, implementation, and ethics. The lecture covers various types of structured and unstructured data, including time series, categorical, continuous, transactional, geographical data, etc. It discusses the importance of EDA as a crucial tool in data science, used for developing hypotheses, identifying relationships between variables, discovering outliers and unusual values. The lecture provides step-by-step guidance on evaluating common statistical properties, examining data structure, assessing the number of records and fields, determining variable types, and more. It underscores the importance of proper implementation and ethical considerations in EDA.