Software Implementation Illustrated with R and Python
About This Book
- 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.
Who This Book Is 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
In Detail
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.
Style and approach
Developing better and smarter ways to analyze data. Making better decisions/future predictions. Learn how to explore, visualize and perform statistical analysis. Better and efficient statistical and computational methods. Perform practical examples to master your learning
Prabhanjan Narayanachar Tattar has a combined twelve years of experience with R and Python software. He has also authored the books A Course in Statistics with R, Wiley, and Practical Data Science Cookbook, Packt. The author has built three packages in R titled gpk, RSADBE, and ACSWR. He has obtained a PhD (statistics) from Bangalore University under the broad area of survival snalysis and published several articles in peer-reviewed journals. During the PhD program, the author received the young Statistician honors for the IBS(IR)-GK Shukla Young Biometrician Award (2005) and the Dr. U.S. Nair Award for Young Statistician (2007) and also held a Junior and Senior Research Fellowship at CSIR-UGC. Prabhanjan has worked in various positions in the analytical industry and nearly 10 years of experience in using statistical and machine learning techniques.
