Learning Data Science with R and Python

Data science has become a key technology in the digital era and has applications in all industries ranging from health, finance, and retail to high-tech. As organizations face increasing competition in a technology-driven market, they are turning to data-driven approaches to tackle real-world problems. As the volume of available data continues to grow, so too does the array of tools for processing it, including popular languages like R and Python.

This book guides readers through the entire data science workflow using both R and Python. The book first introduces readers to setting up the right environment and the basic programming skills needed for the data science workflow. It also covers data wrangling and cleaning, exploratory data analysis, and data visualization to gain insights and communicate findings. Finally, you will learn the basic statistical modeling concepts, including probability distributions, hypothesis testing, and simple regression analysis. This book goes beyond the basics and covers machine learning. It covers key elements of R and Python, including syntax, data manipulation and visualization, essential statistical functions, and machine learning packages. It uses real-world data science problems as examples of how to apply these concepts.

By the end of this book, you will be able to analyze data, program in R and Python, and effectively present results using real-world scenarios.

WHAT YOU WILL LEARN

● Set up R and Python data science environments.

● Understand core programming concepts in R and Python.

● Clean, transform, and manipulate real-world datasets.

● Perform exploratory data analysis to uncover patterns.

● Create effective visualizations using modern plotting libraries.

● Apply statistical techniques for inference and modeling.

● Build and evaluate machine learning models efficiently.

WHO THIS BOOK IS FOR

This book is for students, aspiring data scientists, data analysts, software engineers, researchers, and business analysts who want to strengthen their skills. No prior R or Python experience is required, though basic computer literacy and fundamental mathematics knowledge are recommended.

Dr. Mamta Kapoor is presently working as an associate professor-research at the Marwadi University Research Center, Department of Mathematics, Faculty of Engineering and Technology, Marwadi University, Rajkot, Gujarat 360003, India. She is a distinguished mathematician and researcher with extensive expertise in numerical and applied mathematics. Her research interests include the numerical approximation of linear and nonlinear partial differential equations, semi-analytical solutions of fractional partial differential equations, fractional calculus, fuzzy calculus, scientific computing, numerical analysis, and fluid mechanics.Dr. Kapoor has authored 71 research publications, including several book chapters, in reputed national and international Scopus-indexed journals. In addition to her scholarly contributions, she is the author of Practical MATLAB and Python: A Comparative Approach to Problem-Solving to Code Like a Pro; Advanced Algebra Practice Questions: Linear Algebra, Abstract Algebra, and Advanced Topics; and Statistics MCQs for Engineering Students.Driven by a strong interest in data science, Dr. Kapoor has earned several professional certifications in the field. These include the Professional Certificate Course in Data Science from the E & ICT Academy, IIT Kanpur, and the Advanced Certification in Applied Data Science, Machine Learning, and IoT offered by the E & ICT Academy, Indian Institute of Technology Guwahati, among others.