Education
A core part of the MoWaTER program is an Introduction to Data Science class, designed to:
- introduce principles of data science;
- in an inquiry-driven environment;
- open to any student from any background.
Course Description:
STA/CSI 2300 Introduction to principles of data science, including problem workflow, variable types, visualization, modeling, programming, data management and cleaning, reproducibility, and big data.
Course Development:
This course was developed and taught by a set of statistics and computer science faculty, including Professors Hering, Poor, and Hamerly along with faculty from Colorado School of Mines Doug Nychka and Tzahi Cath. It was taught by this core team in Spring 2020, Spring 2021, and Spring 2022. This course continues to be taught in both the Statistics and Computer Science departments at Baylor University and is a required course in the Data Science major.
Course Materials:
The materials developed for the Introduction to Data Science Course is designed for undergraduate students from any background. Introduction to principles of data science in R, including problem workflow, variable types, visualization, modeling, programming, data management and cleaning, reproducibility, and big data are provided. Real examples and exercises are given with each topic to strengthen and deepen comprehension. These materials aim to inspire students to pursue additional data science training to prepare them for their future careers. The course was designed with a flipped structure wherein students watched a series of short videos of the instructor explaining the lecture notes before class, and class time was spent on completing the in-class exercises. In this repository, all lecture material, in-class exercises and solutions, required data, and projects. At the time of publication, all code runs, but we provide no guarantees on future versions of R or packages used in this course.