Data Science and Analytics 60+ Transfer, BS

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60+ Transfer Program

Bachelor of Science • DSAN TRN BS

Program Requirements:

Eligibility for this program requires completion of an associate degree prior to enrollment.
Residency Requirement: A minimum of 30 credits including 50% of the major must be completed at DU.

The BS in Data Science and Analytics Degree Program equips students with requisite skills for the in-demand careers in the field of data science and analytics. The program uniquely combines tools from computer science and statistics to prepare graduates who are competent in gathering, manipulating, and transforming data into understandable information used by organizations to make data-driven decisions. The focus of the program is exposure to practical use of data science methods with hands-on experience in analyzing large complex datasets, drawing inferences, and telling stories with data. The program draws coursework from multiple disciplines and includes data mining, statistics, machine learning, deep learning, artificial intelligence, sport analytics, and customer relationship management (CRM) using predictive analytics. Graduates will be prepared to work in many positions including data visualization developers, data mining analysts, data analyst, statistical analysts, market research analysts, and business intelligence analysts.

Associate Degree on File - 60 Credits

Foundational Courses - 3 Credits

MATH 120 - College Mathematics 3 Credits
Students must have completed a college level math course equivalent to MATH 120 - College Mathematics or Intermediate Algebra as a part of this program. Course fulfillment or requirement will be determined at time of enrollment. Not included in credit totals required for this degree.

Foundations of Excellence - 9 Credits

DATA 275 - Introduction to Data Analytics 3 Credits
STAT 220 - Introduction to Statistics 3 Credits
ENGL 311 - Professional Writing 3 Credits
Recommendation: It is assumed that students have at least 2 semesters of English, composition, or literature in their associate’s degree as preparation for ENGL311. Other associate’s level courses with strong writing components may have prepared the student for success. Students that feel unprepared for ENGL311 should discuss options with admissions and/or advisor.

Foundations for the Major - 29 Credits

MATH 205 - Applied Linear Algebra 4 Credits
MATH 215 - Calculus I 4 Credits
MATH 250 - Discrete Structures 3 Credits
STAT 322 - Inferential Statistics 3 Credits
CISP 253 - Python Scripting 3 Credits
CISP 309 - Database Systems 3 Credits
CISP 446 - Data Warehousing 3 Credits
CSCI 280 - Artificial Intelligence 3 Credits
CSCI 325 - Deep Learning 3 Credits

Major - 27 Credits

DATA 288 - Introduction to R Programming 3 Credits
DATA 312 - Data Visualization and Communication 3 Credits
DATA 342 - Advanced R Programming 3 Credits
DATA 356 - Applied Regression Analysis 3 Credits
DATA 374 - Classification Models 3 Credits
DATA 432 - Data Mining 3 Credits
DATA 444 - Essentials of Business Analytics 3 Credits
DATA 490 - Data Science Internship 3 Credits
DATA 495 - Data Science Capstone 3 Credits

Total Credits: 125

This degree requires a minimum of 120 credit hours. To meet this minimum, Open Electives may be required. Non-developmental level courses taken at your previous college(s) will be applied in this area. An official evaluation of transfer credit will determine the need for electives.