数据分析入门书籍，只有51页，里面很多图表实例，手把手教你如何EXCEL画图，对各种知识点（平均值，模式，中值，方差，标准偏差）的讲解相当的到位。

俄罗斯系列丛书一直深受数学专业学生的喜爱，本资源是那汤松 第五版 实变函数论（对课后答案和勘误做了总结），是实变（十遍）的经典教材！

大数据分析，英文原版，文字版，非常方便下载到kindle之类阅读器中查看

包含IBM官方发布的全套Cognos11中文用户手册指南，共22本。

非常轻量级的客户行为抓取js，可以记录客户浏览行为，记录日志到后台。 供参考学习

美国辛辛那提大学James R. Evans教授写的商业分析著作，本书2017年的新书。搞商业分析的小伙伴可以参考一下。

Data Analysis and Science Using Pandas, matplotlib, and the Python Programming Language

Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you’ll focus on particular analys

This in-depth guide provides managers with a solid understanding of data and data trends, the opportunities that it can offer to businesses, and the dangers of these technologies. Written in an accessible style, Steven Finlay provides a contextual roadmap for developing solutions that deliver benefits to organizations. Table of Contents Chapter 1 Introduction Chapter 2 Using Predictive Models Chapter 3 Analytics, Organization And Culture Chapter 4 The Value Of Data Chapter 5 Ethics And Legislation Chapter 6 Types Of Predictive Models Chapter 7 The Predictive Analytics Process Chapter 8 How To Build A Predictive Model Chapter 9 Text Mining And Social Network Analysis Chapter 10 Hardware, Software And All That Jazz Appendix A. Glossary of Terms Appendix B. Further Sources of Information Appendix C. Lift Charts and Gain Charts

As today’s organizations are capturing exponentially larger amounts of data than ever, now is the time for organizations to rethink how they digest that data. Through advanced algorithms and analytics techniques, organizations can harness this data, discover hidden patterns, and use the newly acquired knowledge to achieve competitive advantages. Presenting the contributions of leading experts in their respective fields, Big Data: Algorithms, Analytics, and Applications bridges the gap between the vastness of Big Data and the appropriate computational methods for scientific and social discovery. It covers fundamental issues about Big Data, including efficient algorithmic methods to process data, better analytical strategies to digest data, and representative applications in diverse fields, such as medicine, science, and engineering. The book is organized into five main sections: Big Data Management―considers the research issues related to the management of Big Data, including indexing and scalability aspects Big Data Processing―addresses the problem of processing Big Data across a wide range of resource-intensive computational settings Big Data Stream Techniques and Algorithms―explores research issues regarding the management and mining of Big Data in streaming environments Big Data Privacy―focuses on models, techniques, and algorithms for preserving Big Data privacy Big Data Applications―illustrates practical applications of Big Data across several domains, including finance, multimedia tools, biometrics, and satellite Big Data processing Overall, the book reports on state-of-the-art studies and achievements in algorithms, analytics, and applications of Big Data. It provides readers with the basis for further efforts in this challenging scientific field that will play a leading role in next-generation database, data warehousing, data mining, and cloud computing research. It also explores related applications in diverse sectors, covering technologies for media/data