Data Analytics with R

With the growing advent of technologies, big data has evolved from different sources. This big data is complex and is able to solve many business problems. But, the traditional data processing software is not able to manage big and voluminous data. Hence, an important part of the tasks in solving business problems and preparing for Industry 4.0 is the adaption of the higher education according to requirement of data science.


Data science spans across a number of industries and markets on a global level owing to its multi-fold application. After the digital revolution, the hardware and software cost has reduced drastically and data science technologies like R, Python, Tableau, Power BI etc. has emerged. The knowledge of these technologies has a large impact on today’s career landscape- from jobs focused on sustainability to jobs which can manage latest data science technologies.


My book on “Data Analytics with R” is an attempt to provide a reservoir of updated knowledge on varied tools for academicians, consultants, research scholars and students. The goal is to open the doors of opportunity related to different analytical techniques from a broader array of datasets. This book has 16 chapters classified into four sections.  


The first section “Basics of R” deals with the basic operations available in R. This section includes five chapters including introduction to R, data structures, programming in R, data exploration and manipulation, input and output. The second section “Visualization Techniques” comprises of basic and advanced visualization techniques which include step by step procedure to create different type of charts in R for better interpretation. Topics related to different types of statistical techniques available in R are covered in the third section named “Statistical Analysis”. The last section “Machine Learning” includes six chapters related to machine learning problems of regression and classification; unsupervised machine learning algorithms like dimension reduction techniques and cluster analysis; supervised machine learning algorithms like Naïve Bayes, k-NN, support vector machines and decision tree; supervised machine learning ensemble techniques like random forest, bagging and gradient boosting; machine learning for text data which include text mining and sentiment analysis. The last chapter is the heart of the book which focuses on Neural Networks.          

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