STS 2300 INTRODUCTION TO DATA ANALYTICS
This course will introduce students to a cutting edge statistical programming language, such as R, and will provide foundational statistical tools for data analytics. Students will learn to apply computer-intensive randomization-based approaches to statistical inference for a range of scenarios that build upon and expand topics covered in introductory Statistics classes. Key data analytics topics will include importing data from a wide variety of sources, data wrangling, data visualization, and exploratory data analysis. Simple and multiple linear regression will be introduced from a predictive modeling perspective. Throughout the course, students will learn to generate reproducible and dynamic statistical reports, with emphasis placed on communicating data analytic results to non-statistical audiences.
Prerequisite
STS 2120 (pre or corequisite), or by permission of the Statistics Program Coordinator.
Course Types
First-Year Foundation; Science
- This course will enable students to improve their data competencies using leading statistical software for data analytics, such as R. Specific outcomes that should prove valuable include the ability to:
- Import various types of data;
- Wrangle data into a format appropriate for conducting planned analyses;
- Build multi-layered data visualizations that transcend the default visualizations typically provided by statistical software;
- Generate reproducible and dynamic statistical reports and presentations using R Markdown, or a comparable platform;
- Fit simple and multiple linear regression models for the purpose of predicting a quantitative response variable;
- Test statistical hypotheses and generate confidence intervals for a broad range of data measures using randomization- and permutation-based approaches;
- Conceptualize, plan, and conduct a data analytics project, from beginning to end;
- Effectively communicate data analytic results orally, visually, and in writing.