Data Science Major: Requirements

About the Data Science Major

The goal of data science coursework is to provide students with a foundation in data collection, visualization, modeling, analysis, reporting, and critical evaluation, as well as the “soft skills” in communication, teamwork, self-motivation, leadership, ethics, and an awareness of the impact of a data-rich society.

Our Bachelor of Science in Data Science program incorporates data science courses with work in computer science, statistics, mathematics, and other areas to provide the foundation that students need to pursue careers in data science in many subject realms (health, insurance, ecology, marketing, accounting, engineering, consulting, and more) and are well-prepared for graduate study in data science, statistics, and a variety of analytic areas.

Do you want to make meaning from complex data sets in computationally-rich fields? Data Science lies at the intersection of mathematics, statistics, computer science, and subject matter expertise. With a major in Data Science, you’ll prepare for a career managing and processing large data sets, creating visualizations and models to identify patterns and trends, communicating the results to varied audiences, and making impactful decisions informed by data from diverse fields of knowledge, touching almost every aspect of the modern world.

Graduates should be well-qualified for advanced study in graduate programs or employment in a professional career requiring analytical skills. All data science graduates will obtain a background in mathematics, statistics, and computer science and gain experience with statistical computing, database management, and data manipulation and visualization. With a grounding in the liberal arts, the data science program also teaches critical thinking, problem-solving, effective communication, and consideration of the ethical and moral implications of data science.

Truman’s Data Science degree is consistent with the principles and recommendations contained in the American Statistical Association’s “Curriculum Guidelines for Undergraduate Programs in Data Science,” https://www.amstat.org/asa/files/pdfs/EDU-DataScienceGuidelines.pdf.

All data science majors will develop their skills as speakers and as writers. DATA 344 Data Ethics is a Writing-enhanced course where students develop their data communication skills.  All students complete a capstone experience that includes a presentation and a paper. Many other courses in the major involve less formal writing and speaking experiences in a variety of settings, including longer and shorter forms, formal and informal styles, in-person and online delivery, with a special emphasis on communication products aimed at both technical experts and non-experts.

All Data Science students are encouraged to talk to their advisor about selecting a minor in a related area. Statistics uses tools from the mathematical sciences to examine and address problems in disciplines across campus. Students should also consider an internship, consulting, or other statistics and/or data science field experience, where they can apply their analytical know-how to real-world situations.

On campus, our student-driven statistical consulting center, CASE, gives students a chance to work with clients from across and beyond campus with real data. Data Science contests such as ASA DataFest give students a chance to solve real-world problems in a short time frame.

Students may obtain credit and research experience by participating in a recognized national undergraduate research program in data science, such as an REU site, or in one of several research programs sponsored by Truman.

There are also opportunities to work with faculty and other students as tutors or graders for lower-level statistics, computer science, and/or data science courses.

Truman offers a Master of Science in Data Science and Analytical Storytelling that prepares students for careers in a variety of industries in Data Science, Data Analytics, and related fields. Although this graduate program is open to students of any major, Data Science majors may use some courses to fulfill requirements for both programs. See the graduate section of the catalog for more details on the MS in Data Science and Analytic Storytelling or talk to the department chair or one of our faculty members.

Honors in Data Science may be earned by:

  1. Maintaining an overall grade point average of 3.5 or higher,
  2. Maintaining a major grade point average of 3.5 or higher,
  3. Demonstrating excellence in independent scholarship, and
  4. Receiving the concurrence of the statistics and data science faculty.

Degree Requirements

The Dialogues Requirements: 42-61 Credits

Bachelor of Science Requirement: 7 Credits

Courses listed in the Bachelor of Science Requirement may double count with minors or second majors, but may not double count with Dialogues requirements nor be used elsewhere in this major.

  • MATH 263 – Analytic Geometry and Calculus II Credits: 4 OR
    • STAT 260 – Intermediate Applied Mathematics for Data Analysis Credits: 4
  • MATH 285 – Matrix Algebra Credits: 3 OR
    • MATH 357 – Linear Algebra Credits: 3

Major Requirements:

The Data Science major consists of three (3) parts: Required Support, Core Requirements, and Electives. Each student must complete all parts.

Part I: Required Support: 14-23 Credits

Courses listed in Required Support may double count for Dialogues, minors, or other campus requirements, but may not double count with requirements listed elsewhere in this major. Students should work with their advisor to pick the right set of courses for their future goals.

  • STAT 190 – Basic Statistics Credits: 3 OR
    • STAT 290 – Statistics Credits: 3
  • MATH 198 – Analytic Geometry and Calculus I Credits: 5

Choose and complete one or more of the following Specialization Blocks:

  • Intermediate Coding block: 6 credit hours
    • CS 260 – Object-Oriented Programming and Design Credits: 3
    • CS 310 – Data Structures and Algorithms Credits: 3 OR
      • CS 480 – Artificial Intelligence Credits: 3
  • Data Consulting Block: 6 credit hours
    • STAT 310 – WE/Statistical Communication and Data Collection Credits: 3
    • STAT 392 – Statistical Consulting with Practicum Credits: 3
  • Statistics Block: 6 credit hours
    • STAT 478 – Regression Analysis Credits: 3
    • One of the following 400-level or higher Statistics Courses:
      • STAT 410 Probability Models Credits: 3
      • STAT 430 Bayesian Statistics Credits: 3
      • STAT 571 Mathematical Probability and Statistics II Credits: 3
      • Another 400+ Statistics Course (with approval of the chair) Credits: 3
  • A minor or second major (15+ hours) in any field other than CS, Statistics, or Data Science. Minors in data-driven fields are particularly encouraged, including Astrophysics, Biology, Business, Economics, Health, Linguistics, Physics, Psychology, or Web Design UI/UX.
  • A learning plan of 15 or more credit hours, approved by the advisor and chair, including at least six hours of STEM, broadly defined, and including at least one course at the 300+ level.

Note: The computer science and statistics blocks are sufficient with coursework elsewhere in the major for students to concurrently earn minors in those areas.

Part II: Major Requirements: 40 Credit Hours

  • DATA 222 – Fundamentals of Data Science Credits: 3 OR
    • STAT 220 – Fundamentals of Data Science Credits: 3
  • DATA 322 – Intermediate Data Science Credits: 3 (formerly STAT 322) OR
    • STAT 322 Intermediate Data Science Credits: 3
  • DATA 324 – Data Visualization Credits: 3 (formerly STAT 320) OR
    • STAT 320 /WE Data Visualization Credits: 3
  • DATA 344 – /WE Data Ethics Credits: 3 NEW COURSE!! OR
    • CS 345 /WE Cyberethics Credits 3
  • DATA 520 – Data Mining and Multivariate Statistics Credits: 3 OR
    • STAT 520 – Data Mining and Multivariate Statistics Credits: 3
  • CS 180 – Foundations of Computer Science I Credits: 4
  • CS 181 – Foundations of Computer Science II Credit(s): 4
  • CS 430 – Database Systems Credit(s): 3
  • STAT 250 – Statistical Computing Credits: 3
  • STAT 370 – Probability Credits: 3 OR
    • CS 191 – Computing Structures Credits: 3 OR
    • MATH 347 Discrete Mathematics Credits: 3 OR
    • STAT 570 – Mathematical Probability and Statistics I Credits 3
  • STAT 330 – Statistical Methods Credits: 3 OR
    • STAT 330 – Statistical Methods Credits: 3 OR
    • STAT 331 – Biostatistics Credits: 3 OR
    • STAT 378 – Linear Regression Credits: 3 OR
    • STAT 530 – Applied Statistical Analysis I Credits: 3
  • STAT 101 – New Major Seminar for Statistics and Data Science Credits: 1
  • STAT 398 – Intermediate Seminar in Statistics and Data Science Credits: 1
  • STAT 497 – WE/ Capstone Experience Credits: 2
  • STAT 498 – Senior Seminar in Statistics and Data Science Credits: 1

Part III: Electives: 6 or more Credit Hours

  • At least 6 additional credits from STAT, CS, MATH, or DATA not used elsewhere in this major, including at least 3 credits at the 300+ level, to total 40 total hours in the major.

Electives to Total: 120 Credits

Note: High-Impact Experiences and Capstones

Data Science students are encouraged to consider data-intensive High-Impact Experiences, such as internship and practicum experiences, research experiences, data contests (such as ASA Datafest), and consulting experiences (such as CASE).

Students who complete two or more High-Impact Experiences, including at least one that is data-intensive, may request to have a statistically-intensive High-Impact Experience worth two or more credits, such as Independent Research or an Internship, to substitute for STAT 497. Course credits may only be used once in the Data Science major. All Data Science students must complete STAT 498.

 

Data Science Major