Introduction#
Welcome to Defining Data Science! This book is a collection of materials that we use at University of Virginia’s School of Data Science (UVA SDS) to introduce students to the field. It comes from the DS1001 - Foundations of Data Science course, which is a new approach to an introductory data science course, and treats the field as its own entity rather than a piecemeal combination of computer science and statistics curriculumns. Our intention is that this book be a resource to those looking to understand the field of Data Science and see a path within that field for themselves. It can also be used as an instructional resource for instructors looking to incorporate these concepts into their courses. {insert something about licensing}
We have organized this book into 4 main sections: Design, Value, Systems, and Analytics - which comes from the 4 areas in the 4+1 model of Data Science described by Rafael Alvarado. This structure is also used as a basis for our degree programs at UVA SDS. Each section contains a narrative of the area provided by faculty members from UVA SDS along with instructional materials that can be adapted or used as is in a course. Note the assignments have a one-level rubric in line with Specifications Grading as described in the literature referenced. For more information on how our DS1001 course is structured, check out our course syllabus.
Instructional materials#
LABS activities are designed to be largely completed by most students in a 50 minute lab period. Many include hands on resources like game sets (Guess Who?!, Battleship) and visual/kinesthetic components (card sets, globes). All are intended to give students an opportunity to interact with data science concepts through an intuitive, hands on, and often gamified approach. Also included in the instructional materials for each section are student assessments, which include LOOK and CASE assignments, and are meant to be an opportunity for students to extend themselves beyond the content covered in the course and begin to develop a personal connection to the content. LOOK assignments provide a look at the type of work students encounter at later points in our Data Science curriculumn. CASE assignments present students with a case study that digs in to a real-world example of the concepts discussed in the course. All assignments lean heavily on student connection and refelction - prompting students to think deeply about the concepts being presented and how they connect to what they have learned throughout the course and their own lives. Data Science is a broadly reaching topic, which presents exciting opportunity to incorporate these connections throughout the course.
Final Thoughts
Appendix