Evaluating and Fine-Tuning Regression Models in Python with Scikit-learn

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Introduction to R for Data Science


Data science is emerging as a vital skill for researchers, analysts, librarians, and others who deal with data in their personal and professional work. In essence, data science is the application of the scientific method to data for the purpose of understanding the world we live in. More specifically, data science tasks emerge from an interdisciplinary amalgam of statistical analysis, computer science, and social science research conventions. Although other programming languages such as python exceed R in general popularity, R remains as one of the most popular programming languages for data scientists and researchers due to a focus on statistical programming. As such, R and RStudio are invaluable tools to the data scientist, statistician, researcher, and many others.

This guidebook aims to provide readers an opportunity to make a start towards learning R for a variety of data science tasks, include (a) data cleaning and preparation, (b) statistical analysis, (c) data visualization, (d) natural language processing, (e) network analysis, and (f) Structural Equation Modeling to name a few. In Chapters 1 and 2 we invite readers to install R and RStudio and to start manipulating data for analysis. Chapter 3 and Chapter 4 include introductory exercises to teach data visualization and statistical analysis in R. In Chapter 5 and beyond, you will explore basic analytic concepts (e.g., correlation and regression) and more advanced approaches to data modeling through the lenses of Structural Equation Modeling, Network Analysis, and Text Analysis.Read more.

Career Relevance by Data Role

The techniques and tools covered in Introduction to R for Data Science are most similar to the requirements found in Data Scientist job advertisements.

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