Quản trị dữ liệu và trực quan hóa

I wrote this book to scratch my own itch: the book I wanted to
teach out of for my graduate visualization (vis) course did not exist. The itch grew through the years of teaching my own course at the University of British Columbia eight times, co-teaching a course at Stanford in 2001, and helping with the design of an early vis course at Stanford in 1996 as a teaching assistant.

If I am honest with myself, the writing of this book is primarily a therapeutic exercise to help me exorcize 20 or more years of frustration with certain issues in quantitative research. Few concepts in this book are new—many are
the better part of a century old. So why should you read it? Because there are important steps every quantitative researcher should take prior to collecting their data to ensure the data meet their goals. Because after collecting data, and
before conducting the critical analyses to test hypotheses, other important steps should be taken to ensure the ultimate high quality of the results of those analyses. I refer to all of these steps as data cleaning, though in the strictest sense of the concept, planning for data collection does not traditionally fall under that label 

Tổng hợp bài giảng của thầy Trần Việt Trung| Bài giảng môn quản trị dữ liệu và trực quan hóa| Trường Đại học Bách Khoa Hà Nội. Tài liệu gồm 798 trang giúp bạn đọc ôn tập và đạt kết quả cao trong kỳ thi sắp tới. Mời bạn đọc đón xem.

Tổng hợp bài giảng của thầy Nguyễn Hữu Đức| Bài giảng môn quản trị dữ liệu và trực quan hóa| Trường Đại học Bách Khoa Hà Nội. Tài liệu gồm 674 trang giúp bạn đọc ôn tập và đạt kết quả cao trong kỳ thi sắp tới. Mời bạn đọc đón xem.

“The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decades ...’’

Questions To Answer
What Does Uncertainty Mean?
How Should I Visualize It?
What Can Go Wrong?

Varieties of “big data”…

Graphs
Model relations among data
Nodes and edges
Trees
Graphs with hierarchical structure
Connected graph with N-1 edges
Nodes as parents and children

TOPICS

Focus+Context (Trees, Spatial Navigation)
Data Density of Time Series
Perceptual Organization of Graphs
Discussion and Course Evaluation

A variety of useful toolkits have been designed to help support information visualization applications. Some include support for the full visualization pipeline from data to interactive graphics, while others focus only on a subset, typically graphics and interaction.