Includes bibliographical references and index.

Basic Types -- R vectors versus Numpy arrays and Pandas' Series -- Numpy ndarrays versus R's matrix and array types -- R's lists versus Python's lists and dicts -- Functions -- Categorical data -- Data frames -- Input and output -- Using third-party code -- Control flow -- Reshaping and combining data sets -- Visualization -- An introduction to object-oriented programming -- An introduction to functional programming.

"An Introduction to R and Python For Data Analysis helps teach students to code in both R and Python simultaneously. As both R and Python can be used in similar manners, it is useful and efficient to learn both at the same time, helping lecturers and students to teach and learn more, save time, whilst reinforcing the shared concepts and differences of the systems. This tandem learning is highly useful for students, helping them to become literate in both languages, and develop skills which will be handy after their studies. This book presumes no prior experience with computing, and is intended to be used by students from a variety of backgrounds. The side-by-side formatting of this book helps introductory graduate students quickly grasp the basics of R and Python, with the exercises providing helping them to teach themselves the skills they will need upon the completion of their course, as employers now ask for competency in both R and Python. Teachers and lecturers will also find this book useful in their teaching, providing a singular work to help ensure their students are well trained in both computer languages"-- Provided by publisher.

College of Engineering and Computer Studies Bachelor of Science in Information Technology

In English