Computing & Information Sciences: Data Science
| College: | Computing, Engineering and Construction |
|---|---|
| Degree: | Bachelor of Science |
| Major: | Computing & Information Sciences |
| Concentration: | Data Science |
| Effective Term: | Fall 2023 |
The Osprey Map provides a term-by-term sample schedule for each undergraduate academic program at the University of North Florida. It is designed to guide students in the selection of courses each term. The "Mile Marker" courses are those courses that should be completed before or by the end of that term. Following the sample schedule, and completing the mile marker courses at the appropriate point of your undergraduate career, will facilitate graduation in four years. The term-by-term model assumes full-time enrollment. For students enrolled part-time, the sample schedule and mile markers should be followed based on the number of credits completed. The Osprey Map should be used in consultation with an academic advisor.
The Data Science Concentration of the Computing and Information Sciences Major focuses on studying methods for managing and analyzing large datasets. The Data Science Concentration has a significant component of math and science courses. With courses focused on statistics, database systems, algorithm design and analysis, and data analytics graduates of the program will be able to design, implement, and use methods for the discovery of patterns and prediction of future trends from datasets. Typical first job titles include data scientist, and data analyst.
Career Planning
Engaging in career planning activities is significant to student success. UNF offers customized career centers to deliver targeted programs, events, services and resources. First-Year and Exploratory students should visit the Career Services webpage and Sophomore or higher level students with a declared CCEC major should visit the CCEC Career Development Center.
Students should refer to their personalized Degree Evaluation provided by an academic advisor and to other university resources for additional university and college-specific policies. Visit the undergraduate university catalog for more information.
Summer terms may be utilized to facilitate graduation in four years.
- Visit the university catalog for a list of General Education requirements. An Associate in Arts (AA) degree from a Florida public university, state college or community college satisfies UNF general education and Gordon Rule requirements. Math selection is contingent upon appropriate placement score, the student's major and credit received through accelerated mechanisms (Advanced Placement, International Baccalaureate, Dual Enrollment, etc).
- Visit the School of Computing home page for additional information. Introductory 'C' is the preferred prerequisite programming language and will be used in subsequent courses. Students requiring preparation for MAC 2311 Calculus I should take MAC 1147 Precalculus. For more information consult an academic advisor and visit the School of Computing Advising Office webpage.
- Students must adhere to the School of Computing's Student Attendance Policy.
- Exit Requirements:
- Proficiency in a high-level programming language.
- Proficiency in oral communication.To demonstrate satisfactory oral communication skills, students must deliver up to two presentations in an upper-level course offered by the School of Computing. If the first presentation is satisfactory, the second presentation will be waived.
- School of Computing Satisfactory Progress Policy:
- The School of Computing enforces the "one repeat" rule for all prerequisite and core courses offered by the School for its major programs.
- Students who do not successfully complete a prerequisite, core, or major requirement for a School of Computing major on the first attempt due to earning a grade of D, F, W, WP or WF) will be granted one chance to repeat the course.
- Students who do not successfully complete the aforementioned course on the second attempt will be blocked from registering for courses offered by the School of Computing in future semesters.
- This policy applies whether or not the student has declared a major in a School of Computing program.
- Students who do not obtain B or better grade on both COP 3503 Programming II and STA3032 Probability and Statistics for Engineers will be eligible for suspension from the program.
- CIS3949 Internship may count toward a Data Science Elective for a maximum of 3 credit hours.
- Civic Literacy Requirement: Prior to graduation from a Florida state university, students must demonstrate competency in civic literacy through successful completion of a civic literacy course (AMH 2020 or POS 2041) and by achieving a passing score on an assessment. Consult an academic advisor, the university catalog and visit the Civic Literacy webpage for additional information.
- The Concentration in Data Science requires 120 total semester hours, including 60 upper-level (3000/4000) hours.
Term 1: Attempted Hours 0-15
| Schedule | Credit Hours | |
|---|---|---|
| ENC1101 (GW) Audience and Purpose | 3 | |
| MAC2311 (GM) Calculus I (Program Prerequisite) | 4 | |
| IDC2000 Beauty and Joy of Computing (Recommended Free Elective) | 3 | |
| Select two General Education Courses (See Degree Evaluation) | 6 | |
| Total Hours: | 16 | |
| Mile Marker(s) MAC2311 (GM) Calculus I Minimum Grade C |
||
Term 2: Attempted Hours 16-30
| Schedule | Credit Hours |
|---|---|
| ENC1143 (GW) Evidence and Style OR IDS1932 First Year IDS Writing Seminar | 3 |
| COP2220 Programming I (Program Prerequisite) (Recitation with a Teacher Assistant or Instructor is required for COP2220) |
3 |
| MAC2312 (GM) Calculus II (Program Prerequisite) | 4 |
| Select a General Education Course (See Degree Evaluation) | 3 |
| Select a Public Speaking Course (SPC 4064 is preferred) | 3 |
| Total Hours: | 16 |
| Mile Marker(s) COP2220 Programming I Minimum Grade C |
|
Summer 1
| Schedule | Credit Hours | |
|---|---|---|
| NOTE: Use Summer Term to catch-up on Math or Science Courses, if necessary. Or get ahead by taking COT 3100 Computational Structures or MAD3107 Discrete Mathematics OR CIS3253 Legal Ethical Issues in Computing (online). | ||
| Mile Marker(s) MAC2312 (GM) Calculus II Minimum Grade C |
||
Term 3: Attempted Hours 31-45
| Schedule | Credit Hours | |
|---|---|---|
| ENC2210 (GW) Technical Writing (Satisfies third General Ed Written Communication Course) | 3 | |
| Select first course in your Science sequence of choice: BSC1010C or CHM2045/L or PHY2048C (See Degree Evaluation) |
4-5 | |
| Select one: COT3100 Computational Structures OR MAD3107 Discrete Mathematics | 3 | |
| STA3163 (GM)Statistical Methods I | 4 | |
| Total Hours: | 14 - 15 | |
| Mile Marker(s) COT3100 Comp Structures OR MAD3107 Discrete Math |
||
Term 4: Attempted Hours 46-60
| Schedule | Credit Hours | |
|---|---|---|
| Select second course in your Science sequence of choice: BSC1011C or CHM2046/L or PHY2049/L (See Degree Evaluation) |
4-5 | |
| STA3164 (GM)Statistical Methods II | 3 | |
| COP3503 Programming II | 3 | |
| Select a General Education Course (See Degree Evaluation) | 3 | |
| Total Hours: | 13 - 14 | |
| Mile Marker(s) COP3503 Programming II Minimum Grade C |
||
Summer 2
| Schedule | Credit Hours | |
|---|---|---|
| Summer term course options include: CIS3949 Internship, a Data Science Elective, or get ahead by taking COP3703 Intro to Databases or CNT4504 Computer Networks or STA4321 Prob/Statistics for Engineers | ||
Term 5: Attempted Hours 61-75
| Schedule | Credit Hours | |
|---|---|---|
| CIS3253 GW-Legal Ethical Iss in Comput | 3 | |
| MAS3105 (GM) Linear Algebra | 4 | |
| COP3703 Introduction to Databases | 3 | |
| COP3530 Data Structures | 3 | |
| Total Hours: | 13 | |
| Mile Marker(s) COP3530 Data Structures Minimum Grade C General Education Requirements |
||
Term 6: Attempted Hours 76-90
| Schedule | Credit Hours | |
|---|---|---|
| CNT4504 Computer Networks | 3 | |
| CAP3784 Introduction to Data Analytics (Spring Term only) | 3 | |
| Select one course: STA4502 Non-parametric Methods in Statistics (Offered Spring Term) OR STA4504 Categorical Data Analysis (Offered Fall Term). If selecting STA4504, take in Term 7. | 3 | |
| Select a Data Science Major Elective | 3 | |
| CAP4651 Machine Learning | 3 | |
| Total Hours: | 15 | |
| Mile Marker(s) COP3703 Introduction to Databases Minimum Grade C Civic Literacy Requirement |
||
Summer 3
| Schedule | Credit Hours | |
|---|---|---|
| Summer term course options include: CIS3949 Internship, or a Data Science Elective, or get ahead by taking MAS 3105 Linear Algebra | ||
Term 7: Attempted Hours 91-105
| Schedule | Credit Hours | |
|---|---|---|
| CAP4770 Data Mining (Offered Fall term only) | 3 | |
| STA3032 (GM) Probability and Statistics for Engineers | 3 | |
| COT4400 Design and Analysis of Algori (Offered Fall term only) | 3 | |
| Select a Data Science Major Elective | 3 | |
| Select Free Electives (If needed for 120 total hours) | 2 | |
| Total Hours: | 15 | |
| Mile Marker(s) CAP4770 Data Mining Minimum Grade C |
||
Term 8: Attempted Hours 106-120
| Schedule | Credit Hours |
|---|---|
| CAP4773 Data Science Capstone | 3 |
| Select a Data Science Major Elective | 3 |
| Select Free Electives (If needed for 120 total hours) | 9 |
| Total Hours: | 15 |
Total Program Hours: |
120 |