Statistical Application Development with R and Python: Develop applications using data processing, statistical models, and CART , Second Edition

€41.99

4.3 (4 Ratings)

Paperback Aug 2017 432 pages 2nd Edition

€41.99

Paperback Aug 2017 432 pages 2nd Edition

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Key benefits

Learn the nature of data through software which takes the preliminary concepts right away using R and Python.
Understand data modeling and visualization to perform efficient statistical analysis with this guide.
Get well versed with techniques such as regression, clustering, classification, support vector machines and much more to learn the fundamentals of modern statistics.

Description

Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions. This book explores statistical concepts along with R and Python, which are well integrated from the word go. Almost every concept has an R code going with it which exemplifies the strength of R and applications. The R code and programs have been further strengthened with equivalent Python programs. Thus, you will first understand the data characteristics, descriptive statistics and the exploratory attitude, which will give you firm footing of data analysis. Statistical inference will complete the technical footing of statistical methods. Regression, linear, logistic modeling, and CART, builds the essential toolkit. This will help you complete complex problems in the real world. You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python. The data analysis journey begins with exploratory analysis, which is more than simple, descriptive, data summaries. You will then apply linear regression modeling, and end with logistic regression, CART, and spatial statistics. By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.

Who is this book for?

If you want to have a brief understanding of the nature of data and perform advanced statistical analysis using both R and Python, then this book is what you need. No prior knowledge is required. Aspiring data scientist, R users trying to learn Python and vice versa

What you will learn

• Learn the nature of data through software with preliminary concepts right away in R
• Read data from various sources and export the R output to other software
• Perform effective data visualization with the nature of variables and rich alternative options
• Do exploratory data analysis for useful first sight understanding building up to the right attitude towards effective inference
• Learn statistical inference through simulation combining the classical inference and modern computational power
• Delve deep into regression models such as linear and logistic for continuous and discrete regressands for forming the fundamentals of modern statistics
• Introduce yourself to CART – a machine learning tool which is very useful when the data has an intrinsic nonlinearity

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Antonio Amodeo Feb 05, 2018

I'm really impressed by this book, as a Data Scientist, I deal in daily basis with the contests between R and Python, but the author could make all the chapters in a very friendly way showing the codes in R and Python at same time.In terms of content, the book is a very well wrote book, where it serves as a great guide to any levels of data analysts or data scientists.The main challenge of a good data scientist is to make exploratory analysis and the author explore it very well, covering the more usual statistical tools and algorithms.I did try to read the book as a no very deep learner and it really impressed me because if a beginner navigates through the pages, certainly the knowledge of him/her will raise a great level.

Amazon Verified review

Reynald Francisco Oct 08, 2017

I received a free advanced copy of the book and I find it as a great reference. This is good balance between statistical theory and R coding.The book is for those that have basic understanding of statistics and needs some guidance on how to implement it using R or Python. I am glad to have this as one of my reference kits.

Shaun.Ngai Dec 30, 2017

Received a free copy from the owner to review this book. Generally this book provides the reader as to how to apply statistics using R & Python. Would recommend any young aspiring data scientist to have this book as data science 101.

Pavithra Sep 25, 2017

Happy to receive a copy on Statistical Application Development with R and Python. This covers widely on basics as well as advanced 'R and Python' - Application perspective. Quite a lot of examples are provided to the users to achieve good amount of understanding. But the flow at which the book is developed is random and ambiguous, which is difficult for us to gain a continuity on chapters.Thanks

Statistical Application Development with R and Python: Develop applications using data processing, statistical models, and CART , Second Edition

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