data-science-using-python-r

Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets. 通过数据科学学习数据科学！ 数据科学使用Python和R将使您进入世界上两个最广泛的数据科学开源平台：Python和R. 数据科学很热门。 Bloomberg称数据科学家是“美国最热门的工作。”Python和R是世界上最畅销的两个开源数据科学工具。在使用Python和R的数据科学中，您将逐步学习如何使用最先进的技术为现实世界的业务问题提供实用的解决方案。 数据科学使用Python和R是为普通读者编写的，没有以前的分析或编程经验。整整一章致力于学习Python和R的基础知识。然后，每章都介绍了使用Python和R解决数据科学问题的分步说明和演练。 那些具有分析经验的人将会喜欢通过Python和R学习如何进行数据科学的一站式服务。涵盖的主题包括数据准备，探索性数据分析，准备数据建模，决策树，模型评估，错误分类成本，天真贝叶斯分类，神经网络，聚类，回归建模，降维和关联规则挖掘。 此外，还包括令人兴奋的新主题，如随机森林和一般线性模型。本书强调数据驱动的错误成本，以提高盈利能力，避免了可能使公司损失数百万美元的常见陷阱。 数据科学使用Python和R在每章的末尾提供练习，总共超过500本练习。因此，读者将有充分的机会测试他们新发现的数据科学技能和专业知识。在动手分析练习中，读者面临着使用真实数据集解决有趣业务问题的挑战。