Data science and machine learning - two of the world's hottest fields - are attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world's #1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help students with widely diverse backgrounds learn foundational Python so they can use it for data science and machine learning. This book is catered to introductory-level college courses on data science. Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once students have learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving. Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and more - all created with colab (jupyter compatible) notebooks, so students can execute all coding examples interactively without installing or configuring any software.
Process Mining to Facilitate Process Improvement in a Healthcare Environment: An Emergency Department Case Study
... 8 dissimilarity matrix and their row total St. Note that the first row has the maximum total distance (S) = 12.05), ... Both (1.2) and (1.3) gives the same solution as in Heiser and Arabie “, Pliner * or Lau, Leung and Tse * with ...
The text mining process included data selection, preprocessing, transformation, lexical analysis, and classification, performing at an 89% accuracy.
Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics.
This comprehensive professional reference for scientists, engineers, and researchers brings together in a single resource all the information a beginner will need to rapidly learn how to conduct data mining and the statistical analysis ...
Data Mining: Techniques And Trends
责任者译名:罗伊尔。
基礎からWeb、ソーシャルメディアまで
本书不仅介绍了搜索,页面爬取和资源探索以及链接分析等传统的Web挖掘主题,而且还介绍了结构化数据的抽取,信息整合,观点挖掘和Web使用挖掘等内容 ...
高等学校研究生系列教材