This article presents an overview of Stream Algebra, a research field that has emerged after the birth of Relational Algebra used in Database Management Systems (DBMS). When Stream Data appeared and needed to be processed in real-time, the role of Stream Algebra became evident. Since the rise of Big Data, this field has received attention in recent years, the role of Stream Algebra has been further demonstrated.

Lecture "Administration and visualization: Chapter 5.1 - Exploratory data analysis" provides students with content about: Data science process; Exploratory data analysis (EDA) focus; EDA definition; EDA common questions;... Please refer to the detailed content of the lecture!

Lecture "Applied data science: Introduction" includes content: data science application, history data science, history statistisc, history big data, data science lifecycle,... We invite you to consult!

Lecture "Applied data science: Application" includes content: digital transformation, data science lifecycle, data science application,... We invite you to consult!

Lecture "Applied data science: Validation" includes content: validation set approach; overfitting; cross-validation; data leakage; nested cross-validation; bootstrapping;... We invite you to consult!

Lecture "Applied data science: Clustering" includes content: Exemplary technique - K-means clustering; Exemplary technique - Hierarchical clustering; Practical issues in clustering; Case study;... We invite you to consult!

Lecture "Applied data science: Regularisation" includes content: variable subset selection, shrinkage methods, dimension reduction, considerations in high dimensions,... We invite you to consult!

Lecture "Applied data science: Classification" includes content: Classification - logistic regression review ; classification evaluation metrics; the expected value framework;... We invite you to consult!

Lecture "Applied data science: Evaluation, deployment, ethics" includes content: evaluation validation components, evaluation dataset, simpon's paradox, some ethical issues in ML,... We invite you to consult!

Lecture "Applied data science: Linear regression (review)" includes content: the regression model formulation, understanding the regression results, potential problems in regression model,... We invite you to consult!

Lecture "Applied data science: Exploratory data analysis" includes content: definitions; data types; steps in Exploratory Data Analysis (EDA); EDA in real-life practice;... We invite you to consult!

Lecture "Applied data science: Learning process and Bias – variance tradeoff" includes content: learning process, bias – variance tradeoff, variance tradeoff, bias variance,... We invite you to consult!

Bài giảng Nhập môn lập trình khoa học dữ liệu: Bài 1 Giới Thiệu môn học cung cấp cho người học những kiến thức như: Thông tin chung về môn học; Data science (khoa học dữ liệu) là gì; Data scientist (nhà khoa học dữ liệu) làm gì. Mời các bạn cùng tham khảo!