Practical Data Science with R pdf epub mobi txt 电子书 下载 2025
- R
- 数据分析
- DataScience
- 数据挖掘
- 统计学
- 计算机
- 数据科学
- data
- R
- 数据科学
- 统计学
- 机器学习
- 数据分析
- 数据挖掘
- 实用指南
- 编程
- 数据可视化
- 商业分析
具体描述
Simply put, data science is the discipline of extracting meaning from data. More and more business analysts are called to work as data scientists and while it can involve deep knowledge of statistics, mathematics, machine learning, and computer science; for most non-academics, data science looks like applying analysis techniques to answer key business questions. Sophisticated software and, in particular, the R statistical programming language, gives practical data scientists more tools than ever to help make quantitative business decisions and build custom data analysis tools for business professionals.
Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully-explained examples based in marketing, business intelligence, and decision support. Using these examples, you'll learn how to create instrumentation, to design experiments such as A/B tests, and to accurately present data to audiences of all levels.
作者简介
Nina Zumel and John Mount are co-founders of Win-Vector, a data science consulting firm in San Francisco. Nina holds a Ph.D. in robotics from Carnegie Mellon and was a content developer for EMC's Data Science and Big Data Analytics Training Course. John has a Ph.D. in computer science from Carnegie Mellon and over 15 years of applied experience in biotech research, online advertising, price optimization and finance. Both contribute to the Win-Vector Blog, which covers topics in statistics, probability, computer science, mathematics and optimization.
目录信息
1 The Data Science Process - FREE
2 Starting with R and Data - AVAILABLE
3 Exploring Data - AVAILABLE
4 Managing Data - AVAILABLE
PART 2:MODELING METHODS
5 Using Memorization Methods
6 Linear and Logistic Regression
7 Using Unsupervised Methods
8 Exploring Advanced Methods
PART 3: RESULTS
9 Evaluating Models
10 Managing Models in Production
11 Building Successful Presentations
12 Presenting to different audiences
13 Deployment Documentation
14 Conclusion
APPENDICES:
A Working With R and other tools
B Important statistical concepts
C Transforming Problems and Data
D Further Reading
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如果你想从事数据科学工作，请读这本书；如果你想学习如何用R展开数据科学的工作，请读这本书；如果你想了解常用的机器学习算法，请读这本书；如果你想进一步锻炼你的英语水平，请读这本书。
评分也许值得重看。
评分如果你想从事数据科学工作，请读这本书；如果你想学习如何用R展开数据科学的工作，请读这本书；如果你想了解常用的机器学习算法，请读这本书；如果你想进一步锻炼你的英语水平，请读这本书。
评分值得一读。查了一下，有趣的是，如同O'Reilly'封面用的全是动物图案，Manning这套技术书封面用的是Camille Bonnard搜集并编辑的Costumes Historiques中的插图。
评分比较与时俱进的R入门书。
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