Data Science, Bachelor of Science (BS)
This is an interdisciplinary degree program offered jointly between the Department of Mathematics and the Department of Computer Science. The Department of Mathematics is responsible for the advising of majors declared in the program. The program is built on the foundation of courses in mathematics, statistics and computer science with emphasis on skills in analysis and mining of data exhibiting the characteristics of high volume, velocity and variety, and model building and computational skills applicable for reducing and managing large data sets residing in the cloud.
|Required Computer Science Courses|
|CSCD 110||INTRODUCTION TO PROGRAMMING||5|
|CSCD 210||PROGRAMMING PRINCIPLES I||5|
|CSCD 211||PROGRAMMING PRINCIPLES II||5|
|CSCD 300||DATA STRUCTURES||5|
|CSCD 327||RELATIONAL DATABASE SYSTEMS||4|
|CSCD 429||DATA MINING||4|
|CSCD 430||BIG DATA ANALYTICS||4|
|Required Mathematic Courses|
|MATH 161||CALCULUS I||5|
|MATH 162||CALCULUS II||5|
|MATH 163||CALCULUS III||5|
|MATH 225||FOUNDATIONS OF MATHEMATICS||5|
|MATH 231||LINEAR ALGEBRA||5|
|MATH 241||CALCULUS IV||5|
|MATH 385||PROBABILITY AND STATISTICAL INFERENCE I||5|
|MATH 444||NUMERICAL LINEAR ALGEBRA||5|
|MATH 485||PROBABILITY AND STATISTICAL INFERENCE II||5|
|MATH 486||PROBABILITY AND STATISTICAL INFERENCE III||5|
|MATH 491||SENIOR THESIS||5|
All admitted students must officially Declare a Major by the time they reach 90 credits (junior standing).
Application for Graduation must be made at least two terms in advance of the term you expect to graduate (undergraduate and post-baccalaureate).
Use the Catalog Archives to determine two important catalog years.
SOAR calculates based on these two catalog years.
- The catalog in effect at the student's first term of current matriculation is used to determine BACR (Breadth Area Credit Requirements) and UGR (Undergraduate Graduation Requirements).
- The catalog in effect at the time the student declares a major or minor is used to determine the program requirements.
- apply data mining tools using real-world big data;
- apply software to reduce and manage large data sets residing in the cloud;
- communicate mathematical and statistical concepts both technically and non-technically;
- perform statistical analysis with numerical and symbolic statistical technology/software.
Note: The four listed PLOs will meet or exceed the learning outcomes of the Microsoft Professional Program in Data Science:
- apply statistical methods to data;
- create and validate machine learning models with Azure Machine Learning;
- create data models and visualize data using Excel or Power BI;
- follow a data science methodology;
- use Microsoft Excel to explore data;
- use R or Python to explore and transform data;
- use Transact-SQL to query a relational database;
- write R or Python code to build machine learning models.