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Data Science (BS)

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Statistics Bachelors BS

Minimum Credit Hours

74.5

Maximum Credit Hours

74.5

Major Academic Plan

Title

Data Science Practice

Learning Outcome

Students will be able to acquire, clean, wrangle, store, analyze and visualize a wide variety of data types using a wide variety of data science tools; they will be able to apply their knowledge of tools to solve novel problems, and understand the benefits and limitations of new tools.

Title

Data Science Theory

Learning Outcome

Students will demonstrate the ability to analyze data by appropriately visualizing, fitting, assessing, and interpreting a variety of models, and will understand the limits and possibilities of conclusions drawn from data.

Title

Data Science Ethics

Learning Outcome

Students will be able to mitigate the effects of biased or unreliable data, will be able to avoid the dangers of overreliance on unreliable data, and will be an ethical and positive voice arguing for fairness and transparency in high-impact applications of data science.

Title

Communication Skills

Learning Outcome

Students will be able to create effective visualizations, write technical reports and make technical presentations that convey insight harvested from data.

Title

Professional Preparation

Title

Belonging

Learning Outcome

This program is accessible to everyone, including women, minorities, and those new to STEM. Several strategies to support students from different backgrounds include broad advertisement, individual advisement, representation of professors and advisors from diverse backgrounds, working closely with the Office of Belonging, and belonging training for faculty and advisors.

Program Requirements

Requirement 1 — Complete 2 Requirements

Take the following introductory courses:

Requirement 1.1 — Complete 4 Courses

course - Intro to Computer Science 3.0

course - Calculus 1 4.0

course - Data Science Careers 0.5

course - Intro to Stat Data Analysis 3.0

Requirement 1.2 — Complete 1 of 2 Options

Option 1.2.1 — Complete 1 Course

course - Intro to Data Science 3.0

Option 1.2.2 — Complete 1 Course

course - Intro to Data Science 3.0

Requirement 2 — Complete 7 hours

Take the following courses in mathematical foundations:

course - Calculus 2 4.0

course - Elementary Linear Algebra 2.0

course - Computational Linear Algebra 1.0

Requirement 3 — Complete 12 hours

Take the following courses in statistical foundations:

course - Statistical Modeling 1 3.0

course - Probability and Inference 1 3.0

course - Intro to Bayesian Statistics 3.0

course - Statistical Modeling 2 3.0

Requirement 4 — Complete 10 hours

Take the following courses in computing foundations:

course - Data Structures 3.0

course - Adv Software Construction 4.0

course - Database Modeling Concepts 3.0

Requirement 5 — Complete 17 hours

Take the following courses in the data science core:

course - Intro to Machine Learning 3.0

course - Ethics & Computers in Society 2.0

course - Mathematics of Data Science 3.0

course - Physical Reasoning With Data 3.0

course - Data Visualization 3.0

course - Data Science Process 3.0

Requirement 6 — Complete 12 hours

Take 4 of the following classes: Classes taken here will not double count in Requirement 7

course - Bioinformatics Algorithms 3.0

BIO 462 - Computational Cancer Biology 3.0 - This course is no longer offered.

course - Distributed System Design 3.0

course - Intro to Machine Learning 3.0

course - Deep Learning 3.0

course - Data Science Capstone 1 3.0

course - Data Science Capstone 2 3.0

course - Intro to Graph Data Science 3.0

course - Theory of Predictive Modeling 3.0

course - Applied Econometrics 3.0

course - Machine Learning for Econ 3.0

course - Advanced Econometrics 3.0

course - Earth Data Visualization 3.0

course - Geoscience Data Analysis 3.0

course - Model Uncertainty & Data 2 3.0

course - Num Methods for Linear Algebra 3.0

course - Mathematics of Deep Learning 3.0

course - Theory of Predictive Modeling 3.0

course - Applied R Programming 3.0

course - Probability and Inference 2 3.0

course - Predictive Analytics 3.0

course - Machine Learning 3.0

course - Academic Internship - You may take once 0.5v

Some courses may require prerequisites outside of program requirements.

Requirement 7 — Complete 3 hours

Note: Courses used to fulfill this requirement will not double count in Requirement 6.

course - Advanced Machine Learning 3.0

course - Deep Learning 3.0

course - Data Science Capstone 2 3.0

course - Undergraduate Research - You may take once 3.0

course - Academic Internship - You may take once 0.5v

course - Model Uncertainty & Data 2 3.0

course - Mathematics of Deep Learning 3.0

course - Capstone in Applied Phscs - You may take once 0.5v

course - Research in Physics - You may take once 0.5v

course - Machine Learning 3.0

course - Academic Internship - You may take once 0.5v

course - Intro to Research - You may take once 0.5v