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Academic Course Map for Statistics and Data ScienceÌý(B.A.)Ìý

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ÌýÌýProgram OverviewÌý

The Bachelor of Arts degree in Statistics and Data Science (STAT) offered by the Department of Applied Mathematics at CU ÀÖ²¥´«Ã½ is designed with an emphasis on inter- and cross-disciplinary training. Courses are designed to provide students with skills in both traditional statistical methods and cutting-edge data analysis techniques, and the degree requires an in-depth knowledge of either science, engineering, social science or liberal arts that uses statistics to solve important problems. This knowledge prepares graduates to successfully communicate and collaborate with practitioners in these fields. A capstone course in statistical collaboration provides the opportunity for students to synthesize their previous course work.

Skills acquired through a Statistics and Data Science Bachelor’s degree are in high demand in the current job market and prepare students for careers in areas such as, statistics, data analytics, data science, business, engineering, economics, public health, epidemiology, insurance, forestry, psychology, social justice and human rights. Students choose an area of emphasis/application outside of statistics and data science to acquire knowledge in a discipline-specific area, where statistical applications are prevalent. ÌýThe Department of Applied Math offers a broad range of undergraduate research opportunities funded by a variety of federal agencies.

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Admission Requirements

Students who begin at a Colorado Community College can transfer directly to CU ÀÖ²¥´«Ã½ to finish their degree in any of the programs offered by the College of Arts and Sciences.ÌýPlease see the CU ÀÖ²¥´«Ã½ College of Arts & Sciences’ÌýColorado Community College webpageÌýfor more information detailed information admissionsÌýrequirements for students transferringÌýfrom a Colorado Community College.Ìý

Transfer Recommendations

To graduate in a 4-year time frame, it is important that students follow the recommended schedule. Students who wish to continue their education at the community college beyond the number of credits specified below should explore with both community college and CU ÀÖ²¥´«Ã½ staff how their graduation timeline, COF stipend, and financial aid will be affected.Ìý

Suggested Four-Year Course Plan for the Statistics and Data ScienceÌý(STAT) major (B.A. degree)

This is a suggested guide of coursework only and is subject to change. Always consult your academic advisor for graduation planning purposes.ÌýDepending on a student’s situation, a different plan might be more applicable.Ìý

Students must be ready for Calculus 1 to use this guide.Ìý

The department now also offers a more credit-intensive Bachelor of Science (B.S.) degree in Statistics and Data Science. ÌýTo complete the new B.S. degree in 120 credits, students would need to transfer to CU ÀÖ²¥´«Ã½ after completing just the first two semesters of this guide and before earning an associate degree. Ìý

Community College (first two years)

Fall Semester 1

Spring Semester 1

Fall Semester 2

Spring Semester 2

Total credits at community college: 60*

Completion of the four semesters of coursework above fulfills the requirements for an AssociateÌýof Science Degree at your community college, which fulfills the General Education requirements at CU ÀÖ²¥´«Ã½.

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CU ÀÖ²¥´«Ã½ (last two years)

Fall Semester 3

Spring Semester 3

Fall Semester 4

Spring Semester 4

Total credits at CU ÀÖ²¥´«Ã½: 60

*One approved elective credit might be needed to reach 60 credits if all three of the GT-SC1 courses taken are just 4 credits each.

**Students must choose an outside area of emphasis to acquire knowledge in a discipline-specific area, where statistical applications are prevalent. Students will take at least 18 credits in one department or certificate program, at least 6 of which need to be upper division.Ìý Selection of the outside area and courses in that area is made in consultation with advisors and faculty.Ìý Non-Math gtPathways courses from the AS degree might count toward the outside area of emphasis and thus lower the number of courses in this category needed at CU ÀÖ²¥´«Ã½.

This material was supported by NSF grant #1649201. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not nec​essarily reflect the views of the National Science Foundation.