Introduction to Data Science
Students learn how to work through data science problems within a modern statistical programming language. The course covers the complete analytical process, from gathering the data, to applying appropriate exploratory and statistical analysis, and communicating the results. Important topics in data science projects workflows, version control, and efficient programming are integrated throughout the course. Fundamentals of analytics, data visualization, and management of data are presented.
“Programming Skills for Data Science: Start Writing Code to Wrangle, Analyze, and Visualize Data with R”. Freeman and Ross. ISBN-13: 978-0135133101. Pearson. Addison-Wesley Data & Analytics Series (2018)
“R for Data Science” (R4DS) by Garrett Grolemund and Hadley Wickham https://r4ds.had.co.nz/
This course is an application-driven introduction to data science. The goal of this course is to teach applied and theoretical aspects of programming for data science. Attention is also given to mastering concepts and tools necessary for implementing reproducible research. A final project includes a novel data analysis with associated documentation and presentation.
- Effective data visualizations
- Summarizing, sorting, subsetting, merging data
- Programming essentials
- Exploratory Data Analysis
- Topics in classification and clustering
- Maps and interactive graphics
- Social Media Data Analytics
Students learn how to work through data science problems within the statistical programming language R. The course covers the complete analytical process, from getting the data, to applying appropriate exploratory and statistical analysis, and communicating the results. R is free, open-source, and one of the most widely used programming languages in data analytics.
Textbook: “R for Data Science”, by Hadley Wickham. ISBN-13 (Print version): 978-1491910399 (O'Reilly) . Book also available online at http://r4ds.had.co.nz/