Adam Tashman

Associate Professor
Department: Data Science School
Office location and address
31 Bonnycastle Dr
Charlottesville, Virginia 22903
DS 2001: Programming for Data Science
Credits: 3
The course will expose students to three different programming languages that are core to the Field of Data Science. SQL will be covered first to include a discussion on SQL's mathematical foundations and usage as a declarative language, this will likely cover half of the course. In demand programming language Python and R will be covered in the second half of the class with popular data frame focused packages being targeted.
CS 5012: Foundations of Computer Science
Credits: 3
Provide a foundation in discrete mathematics, data structures, algorithmic design and implementation, computational complexity, parallel computing, and data integrity and consistency for non-CS, non-CpE students. Case studies and exercises will be drawn from real-world examples (e.g., bioinformatics, public health, marketing, and security). Prerequisite: CS 5010, CS 1110 or equivalent, Math 1210 or equiv, Math 3351 or equiv, Math 3100 or equiv.
DS 5100: Programming for Data Science
Credits: 3
An introduction to essential programming concepts, structures, and techniques. Students will gain confidence in not only reading code, but learning what it means to write good quality code. Additionally, essential and complementary topics are taught, such as testing and debugging, exception handling, and an introduction to visualization. This course is project based, consisting of a semester project and final project presentations.
DS 5110: Big Data Systems
Credits: 3
This course will focus on Spark, an open-source, general-purpose computing framework that is scalable & fast. Fundamental data types & concepts are covered. You will learn how to use Spark for large-scale analytics & machine learning, among other topics. Tools for data storage and retrieval are covered, including AWS.
DS 5559: New Course in Data Science
Credits: 1–4
This course provides selected special topics in data science to graduate and undergraduate students.