Statistics (BS): Data Science Concentration
The Bachelor of Science in Statistics curriculum provides foundational training for careers in statistics and data science, and also prepares students for graduate study in statistics or related fields such as analytics. The Data Science Concentration adds to that strong foundation with courses designed to prepare graduates for careers in the rapidly evolving Data Science sector. While our curriculum is centered on statistics, mathematics, and computer programming, it is also designed to have a flexible interdisciplinary flavor. Each statistics major works with their advisor to formulate an individualized plan for the use of "Advised Electives” that typically leads to a minor or second major in fields including business and finance, agriculture and life sciences, computer science, industrial engineering, or the social sciences.
Plan Requirements
| Code | Title | Hours |
|---|---|---|
| Orientation | ||
| COS 100 | Science of Change (verify requirement) | 0 |
| Communication & Advanced Writing | ||
| Select one of the following Communications courses: | 3 | |
| Public Speaking | ||
| Interpersonal Communication | ||
| Argumentation and Advocacy | ||
| Select one of the following Advanced Writing courses: | 3 | |
| Communication for Engineering and Technology | ||
| Communication for Business and Management | ||
| Communication for Science and Research | ||
| ENG 101 | Academic Writing and Research 1 | 4 |
| Mathematics & Sciences | ||
| MA 141 | Calculus I 1 | 4 |
| MA 241 | Calculus II 1 | 4 |
| MA 242 | Calculus III 1 | 4 |
| MA 225 | Foundations of Advanced Mathematics 1 | 3 |
| MA 305 | Introductory Linear Algebra and Matrices 1 | 3 |
| or MA 405 | Introduction to Linear Algebra | |
Students considering graduate school are strongly encouraged to select MA 405 | ||
| GEP Natural Sciences | 11 | |
Selected courses must include (i) at least two laboratory classes and (ii) at least three 3- or 4-credit courses. | ||
| Data Science and Statistical Computing | ||
| PHI 227 | Data Ethics | 3 |
| DSC 202 | Introduction to Data Visualization | 1 |
| DSC 405 | Data Wrangling and Web Scraping | 1 |
| ST 114 | Statistical Programming 1 | 3 |
| ST 307 | Introduction to Statistical Programming- SAS 1 | 1 |
| ST 308 | Introduction to Statistical Programming - R 1 | 1 |
| ST 445 | Introduction to Statistical Computing and Data Management 1 | 3 |
| Introduction to Data Science: Select one of the following 1 | 3 | |
| Mathematical Foundations of Data Science I | ||
| Introduction to Data Science | ||
| Introduction to Data Science | ||
| Statistical Data Science Electives. Select two of the following courses: 1 | 6 | |
| Intermediate SAS Programming with Applications | ||
| Statistical Learning and Data Analytics | ||
| Advanced Computing for Statistical Reasoning | ||
| General Data Science Electives. Select 2 credits of DSC courses at any level. 1 | 2 | |
| Advanced Data Science Electives. Select 2 credits of DSC courses at the 400 level. 1 | 2 | |
| Statistics | ||
| ST 311 | Introduction to Statistics 1 | 3 |
| ST 312 | Introduction to Statistics II 1 | 3 |
| ST 421 | Introduction to Mathematical Statistics I 1 | 3 |
| ST 422 | Introduction to Mathematical Statistics II 1 | 3 |
| ST 430 | Introduction to Regression Analysis 1 | 3 |
| ST 431 | Introduction to Experimental Design 1 | 3 |
| ST 432 | Introduction to Survey Sampling 1 | 3 |
| ST Electives 400 Level 1 | 3 | |
| Advised Electives | ||
| Advised Electives 1,2 | 6 | |
A documented plan for the 6 credits of the Advised Electives will be created in conjunction with the student’s academic advisor. These courses may or may not be statistics courses. Students are encouraged to use Advised Elective credits to pursue a minor or second major. Note that many courses used as Advised Electives might have prerequisites or other restrictions. | ||
| GEP Courses | ||
| GEP Humanities | 3 | |
| GEP Social Sciences | 6 | |
| GEP Health and Exercise Studies | 2 | |
| GEP Elective | 3 | |
| GEP Interdisciplinary Perspectives | 5 | |
| GEP Global Knowledge (verify requirement) | ||
| GEP Foundations of American Democracy (verify requirement) | ||
| World Language Proficiency (verify requirement) | ||
| Free Electives | ||
| Free Electives (12 Hr S/U Lmt) 2 | 6 | |
| Total Hours | 120 | |
- 1
A grade of C- or higher is required.
- 2
Students should consult their academic advisors to determine which courses fill this requirement.
- *
No more than 6 total credits from undergraduate research, independent study, credit by examination, or other similar types of courses may be used to meet program requirements (credit from AP exams or transfer credits is not included under this restriction). If you are unsure if a course falls into this category, please confer with your advisor.
ST Electives 400 Level
| Code | Title | Hours |
|---|---|---|
| ST 404 | Epidemiology and Statistics in Global Public Health | 3 |
| ST 405 | Applied Nonparametric Statistics | 3 |
| ST 412 | Long-Term Actuarial Models | 3 |
| ST 420 | Statistical Principles of Clinical Trials | 3 |
| ST 413 | Short-Term Actuarial Models | 3 |
| ST 421 | Introduction to Mathematical Statistics I | 3 |
| ST 422 | Introduction to Mathematical Statistics II | 3 |
| ST 430 | Introduction to Regression Analysis | 3 |
| ST 431 | Introduction to Experimental Design | 3 |
| ST 432 | Introduction to Survey Sampling | 3 |
| ST 433 | Applied Spatial Statistics | 3 |
| ST 434 | Applied Time Series | 3 |
| ST 435 | Statistical Methods for Quality and Productivity Improvement | 3 |
| ST 437 | Applied Multivariate and Longitudinal Data Analysis | 3 |
| ST 440 | Applied Bayesian Analysis | 3 |
| ST 442 | Introduction to Data Science | 3 |
| ST 445 | Introduction to Statistical Computing and Data Management | 3 |
| ST 446 | Intermediate SAS Programming with Applications | 3 |
| ST 451 | Sports Analytics | 3 |
| ST 452 | Statistical Learning and Data Analytics | 3 |
| ST 453 | Advanced Computing for Statistical Reasoning | 3 |
| ST 491 | Statistics in Practice | 3 |
| ST 495 | Special Topics in Statistics | 1-6 |
| ST 497 | Professional Experience in Statistics | 1-3 |
| ST 498 | Independent Study In Statistics | 1-6 |
| ST 499 | Research Experience in Statistics | 1-3 |
General Data Science Electives
| Code | Title | Hours |
|---|---|---|
| DSC 205 | Data Communication | 1 |
| DSC 225 | Data Science for Social Good | 1 |
| DSC 235 | Introduction to Data Science for Cybersecurity | 1 |
| DSC 295 | Introductory Special Topics in Data Science | 1-3 |
| DSC 406 | Exploratory Data Analysis for Big Data | 1 |
| DSC 410 | Data Internship Preparation for Social Impact | 1 |
| DSC 412 | Exploring Machine Learning | 1 |
| DSC 495 | Special Topics in Data Science | 1-3 |
| DSC 595 | Graduate Special Topics in Data Science | 1-3 |
Advanced Data Science Electives
| Code | Title | Hours |
|---|---|---|
| DSC 406 | Exploratory Data Analysis for Big Data | 1 |
| DSC 410 | Data Internship Preparation for Social Impact | 1 |
| DSC 412 | Exploring Machine Learning | 1 |
| DSC 495 | Special Topics in Data Science | 1-3 |
| DSC 595 | Graduate Special Topics in Data Science | 1-3 |
| First Year | ||
|---|---|---|
| Fall Semester | Hours | |
| COS 100 or E 115 | Science of Change or Introduction to Computing Environments | 2 |
| ST 311 | Introduction to Statistics 1 | 3 |
| MA 141 | Calculus I (CP) 1 | 4 |
| Select one of the following: 1 | 3 | |
| Statistical Programming (CP) | ||
| Introduction to Computing: Python | ||
| Introduction to Computing - Java | ||
| GEP Health and Exercise Studies | 1 | |
| Hours | 13 | |
| Spring Semester | ||
| Select one of the following: | 3 | |
| Public Speaking | ||
| Interpersonal Communication | ||
| Argumentation and Advocacy | ||
| MA 241 | Calculus II (CP) 1 | 4 |
| ENG 101 | Academic Writing and Research | 4 |
| ST 312 | Introduction to Statistics II (CP) 1 | 3 |
| ST 307 | Introduction to Statistical Programming- SAS (CP) 1 | 1 |
| Hours | 15 | |
| Second Year | ||
| Fall Semester | ||
| MA 242 | Calculus III (CP) 1 | 4 |
| MA 225 | Foundations of Advanced Mathematics (CP) 1 | 3 |
| ST 445 | Introduction to Statistical Computing and Data Management 1 | 3 |
| PHI 227 | Data Ethics Provides 3 credits of Humanities GEP credit | 3 |
| GEP Health and Exercise Studies | 1 | |
| Hours | 14 | |
| Spring Semester | ||
| ST 308 | Introduction to Statistical Programming - R 1 | 1 |
| GEP Requirement | 3 | |
| ST 431 | Introduction to Experimental Design 1 | 3 |
| MA 305 or MA 405 | Introductory Linear Algebra and Matrices (CP) 1 or Introduction to Linear Algebra | 3 |
| DSC 202 | Introduction to Data Visualization 1 | 1 |
| DSC 405 | Data Wrangling and Web Scraping 1 | 1 |
| Free Elective | 3 | |
| Hours | 15 | |
| Third Year | ||
| Fall Semester | ||
| ST 421 | Introduction to Mathematical Statistics I (CP) 1 | 3 |
| ST 430 | Introduction to Regression Analysis (CP) 1 | 3 |
| GEP Requirement | 3 | |
| Advised Elective 1 | 3 | |
| Introduction to Data Science 1 | 3 | |
| General Data Science Elective 1 | 1 | |
| Hours | 16 | |
| Spring Semester | ||
| ST 422 | Introduction to Mathematical Statistics II (CP) | 3 |
| GEP Requirement | 3 | |
| GEP Natural Sciences | 4 | |
| Statistics Elective 1 | 3 | |
| Statistical Data Science Elective 1 | 3 | |
| Hours | 16 | |
| Fourth Year | ||
| Fall Semester | ||
| Select one of the following: | 3 | |
| Communication for Engineering and Technology | ||
| Communication for Business and Management | ||
| Communication for Science and Research | ||
| GEP Requirement | 3 | |
| Advised Elective 1 | 3 | |
| GEP Natural Sciences | 3 | |
| Statistical Data Science Elective 1 | 3 | |
| General Data Science Electives 1 | 1 | |
| Hours | 16 | |
| Spring Semester | ||
| ST 432 | Introduction to Survey Sampling 1 | 3 |
| GEP Natural Sciences | 4 | |
| Free Electives | 3 | |
| Advanced Data Science Electives 1 | 2 | |
| GEP Requirement | 3 | |
| Hours | 15 | |
| Total Hours | 120 | |
- 1
At most one D level grade is permitted in Advised Electives, Statistics Electives, or required MAT, ST, or CSC courses. C- or better is required in ST 307 Introduction to Statistical Programming- SAS, ST 311 Introduction to Statistics, ST 312 Introduction to Statistics II and ST 421 Introduction to Mathematical Statistics I.
Career Opportunities
The importance of sound statistical thinking in the design and analysis of quantitative studies is reflected in the abundance of job opportunities for statisticians. Because one can improve the efficiency and use of increasingly complex and expensive experimental and survey data, statisticians are in demand wherever quantitative studies are conducted. Statisticians are highly valued members of teams working in such diverse fields as biomedical science, global public health, weather prediction, environmental monitoring, political polling, crop and livestock management, and financial forecasting. Statistics is at the core of Data Science and Analytics, and our department provides an outstanding environment to prepare for careers in these areas. In addition to finding exciting careers in industry and government, our graduates are also very successful moving on to graduate programs in statistics and related fields at top universities around the globe.
Career Titles
- Actuary
- Aeronautical & Aerospace Engineer
- Aerospace Engineering Technician
- Air Traffic Controller
- Astronomer
- Atmospheric and Space Scientist
- Bank and Branch Managers
- Biopsychologist
- Budget Analyst
- Buyer
- Compensation Administrator
- Computer and Information Scientists
- Computer Programmer
- Database Administrator
- Financial Aid Counselor
- Financial Analyst
- Government Budget Analyst
- High School Teacher
- Market Research Analysts and Marketing Specialists
- Math Professor
- Mathematical Technician
- Mathematician
- Meteorologist
- Middle School Teacher
- Operations Research Analyst
- Physicist
- Psychometrist
- Purchasing Manager
- Securities and Commodities Sales Agent
- Social Science Research Assistants
- Statistical Assistants
- Statistician
- Technical Publications Writer
Learn More About Careers
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