Introduction to Data Analytics and R Programming

Welcome to CMSC 121 Introduction to Data Analytics and R Programming

This site contains all lab exercises and slides for the course

The growth and use of data is increasingly vital for many disciplines, from the natural sciences to the social sciences, from business to the humanities. This course will introduce you to data science/data analytics with a focus on how to collect, manage, process, analyze, and visualize data from a computational perspective. We’ll touch on ways to understand different types of data, various database techniques, and a variety of data analysis approaches. The focus of the course will be on gaining a breadth of knowledge about basic techniques of and applications of data analysis.

Learning Objectives

Thanks to analysis, coding, reading, writing, and presentation, by the end of the course you should be able to:

  • understand and engage with a full data science workflow, from inputting data to generating meaningful inferences from those data.

  • write your own code, and employ open-source tools (primarily the software environment RStudio) to generate insights from diverse data sources.

  • synthesize the results of data analyses for public consumption through visual representation, narrative/written description, and verbal presentation.

  • apply critical thinking skills to evaluate existing data analyses and visualization

  • consider various ethical, political, and social issues in the world of data science practice.

References

The following websites were used in the construction of this workbook. My gratitude to the people who invested their time and effort in developing and offering these valuable resources to the public.

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International CC BY-NC-SA 4.0