Course Descriptions

CS/DASC 125 Introduction to Computer and Data Sciences

Computer science, broadly, studies how to solve problems using computers.  Data science is a related field that focuses on acquiring, cleaning, and exploring data, via visualization and statistical analysis, to aid decision making. This course introduces the fundamental skill of computer science, programming, using data science examples and applications. 

  • No prerequisites.  
  • Offered most semesters. 

CS128 Computer Science II

Introduction to data structures and algorithmic problem solving. Encapsulation and information hiding, recursion, algorithm techniques, and time complexity. Advanced object oriented programming with inheritance, static and dynamic memory allocation. Linked lists, stacks, queues, and sequential and binary search.

  • Prerequisites: CS 125/DASC 125, CS 126, or CS 127.
  • Offered each term.

CS 135 Applications of Sets, Logic, and Recursion

Introduction to functional programming and discrete mathematics. Sets, functions, and relations. Basic logic, including formal derivations in propositional and predicate logic. Recursion and mathematical induction. Programming material: Data types and structures, list-processing, functional and recursive programming.

  • No prerequisites.
  • Offered each spring.

CS 136 Computational Discrete Mathematics

Additional concepts in discrete mathematics. Recurrence relations, counting, and combinatorics. Discrete probability. Algorithmic graph theory. Programming with advanced data structures.

  • No prerequisites.
  • Offered each fall.

DASC 225 Data Analytics with Visualization

An examination of advanced concepts and tools relevant to data cleaning, organization, and transformation. Development of skills and knowledge relevant to identifying and applying statistical tools to answer data-driven questions. Examination of ethical issues in analysis. Introduction to databases and creation of static and interactive reports.

  • Prerequisites: CS/DASC 125 and one of the following: BIOL 323, ECON 227, MATH 141, MATH 325, or PSYC 227.
  • Offered each fall.  

CS 314 Database Systems

Introduction to the relational and semi-structured database models. Theoretical concepts include relational algebra and calculus, logical and physical database design, normalization, database security and integrity, data definition and data manipulation languages. Programming topics: database creation, modification, and querying using XQuery, MySQL and PHP.

  • Prerequisites: CS 128 and Math 135.
  • Offered alternate years.

CS/DASC 377 Applied Data Analysis

This course further develops the programming, mathematical, and statistical skills required for the application of data science tools to data analysis, data visualization, and decision making. This course includes a methodology/writing component in which students develop a draft research proposal for a capstone project. 

  • Prerequisites: CS/DASC 125, CS 126, or CS 127; CS/MATH 136; one of the following: BIOL 209, ECON 227, MATH 141, MATH 325, or PSYC 227.
  • Offered alternate years.

CS 387 Deep Learning

Deep learning (machine learning using large neural networks) has proven to be effective at a a number of difficult tasks, with active research ongoing. Student will study mathematical foundations, implementation of neural network optimization in Python, and a number of applications of deep learning including machine vision and natural laguage processing.

  • Prerequisites: CS128 with a C- or above and CS/MATH 136.
  • Offered alternate years. 

DASC 395 Directed Study, Data Science Capstone

Individual directed study to complete a capstone project.  Requires an approved proposal for a substantial project that applies data science techniques to gather, clean, analyze, visualize, and make inferences with data.  Project culminates in written and oral reports. 

  • Prerequisites: CS/DASC 377 and approval of the program director.
  • Offered alternate years.