Learn R fundamentals, advanced analytics, machine learning, and deep learning for data science
Work on practical labs and exercises to reinforce data manipulation, modeling, and visualization
Equip for practical data, projects, case studies, and translate theory into actionable insights

Description

R is a programming language and environment designed for statistical computing, data analysis, and graphical representation. R is widely used by statisticians, data scientists, researchers, and analysts for various tasks related to data manipulation, statistical modeling, and visualization. R is particularly well-suited for tasks involving data analysis, visualization, and statistics, chosen for its flexibility and a wide array of available tools. This course takes us on a transformative journey through R programming, from foundational concepts to cutting-edge techniques. We delve into R’s fundamentals, data types, variables, and structures. We will explore R programming with custom functions, control structures, and data manipulation. We will analyze data visualization with leading packages, statistical analysis, hypothesis testing, and regression modeling. With regular expressions, we will understand advanced data manipulation, outlier handling, missing data strategies, and text manipulation. We will learn about ML with regression, classification, and clustering algorithms. We will explore DL, neural networks, image classification, and semantic segmentation. Upon completion, we will create dynamic web apps with Shiny and emerge as skilled R practitioners, ready to tackle challenges and contribute to data-driven decision-making.

Who is this book for?

The course caters to aspiring and established data scientists, analysts, programmers, researchers, and professionals seeking to enhance their skills in data manipulation, statistical analysis, ML, and DL using R programming. It caters to individuals with varying experience levels, from beginners looking to enter the field to experienced practitioners aiming to expand their expertise in data-driven decision-making and advanced analytics. Prerequisites include prior programming experience but this course can accommodate learners with varying levels of data science concepts and R programming familiarity.