Data-Science - B.A. and B.S.
Ning Zhang, Ph.D., Discipline Coordinator
The Fisk University Data Science program is the first dedicated undergraduate data science program at a Historically Black College or University as well as in the state of Tennessee. The Data Science program is one of the newest and fastest growing programs at Fisk University.
The data science program prepares students for inter-disciplinary careers related to computing and big data. Students are trained for both professional careers and graduate studies. A minor in data science may be taken in combination with any other major.
Data Science is an interdisciplinary field of scientific methods, processes, and systems to extract knowledge or insights from data. Virtually every area of our society is generating considerable data: social networking, business platforms, health care and personalized/precision medicine, digital humanities for literature and historical ‘excavation’, defense and security, manufacturing and supply chain, climate change forecasting, as examples. However, without data management, curation, analysis and visualization – these data do not inform new insights.
The Department offers both a Bachelor of Science and Bachelor of Arts degree as well as a Data Science minor. The Data Science program strives to bring Innovation to Passion through partner discipline requirements where students can choose a partner discipline including but not limited to Art, Biology, Chemistry, Physics, Psychology, Political Science or Business. This allows students to use the innovation of data science to explore their passion. The program is also designed to allow students to easily add a joint major within the 4-year degree program. These partnerships allow students to complete degrees in areas like Computational Biology and Computational Social Science.
The Bachelor of Arts degree has the fundamental mathematics and computer science content and allows for 18-20 hours of partner discipline courses. The Bachelor of Science degree has more advanced mathematics and computer science content and allows for 10-12 hours of partner discipline courses.
An integral component of the program is the experiential learning component. The Sophomore, Junior, and Senior seminar courses offer students an opportunity to select projects and follow the data science pipeline with appropriate tools at each level of matriculation through the program. The Data Science Club offers students from any discipline the opportunity to be part of the Data Science family. Members of the Data Science club determine the focus of the club and problems, tools and technologies that the students address and learn. The Data Science Innovation Hub offers students the paid opportunities to contribute to ongoing research projects by Faculty members and students at Fisk University and Beyond. Lastly, our industry partnerships allow industry partners to hold seminars and work sessions with students where they discuss real world problems encountered in industry as well as offering internships. The Fisk University Data Science program offers an innovative approach to Data Science preparing students for a diverse workforce.
Program Goal
The goals of the program in data science are designed to produce graduates who can:
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successfully qualify for positions in data science, computer science, information technology, lab technicians, and research scientists; entry into graduate schools in data science, computer and information sciences and technologies; entry into the workforce as data scientists, software engineers, and data analysts.
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enter other professions that require a background in computer science or computer technology;
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teach computer-related subject matter to individuals at the post-secondary level; and
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become productive citizens who contribute to the welfare and development of their communities through their career activities.
Students who complete the major in data science will:
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Have knowledge of the conceptual framework of the major branches of computer science.
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Be able to apply the computer science theoretical principles to problems using the appropriate data structures and algorithms.
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Be able to work effectively in Windows, Unix/Linux, MacOs operating systems. Be able to program effectively with high-level programming languages.
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Be able to apply the necessary technical skills that are fundamental to experimental data science.
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Have a fundamental understanding of the relationship of computer science and mathematics to another partner discipline.
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Be aware of the role of data and computational thinking in society.
Requirements - B.A.
The CORE Curriculum provides broad understanding of cultures, history, and the development of effective communication skills that are fostered in a liberal arts environment.
In addition to CORE requirements, students complete 58-60 credits in three (3) areas: 1) Computational Thinking, 2) Mathematics-Statistics, and 3) Partner Discipline (e.g. Psychology, Biology, Political Science, or Business Administration).
As Data Science has strong interdisciplinary focus, there are several options for a Joint Major with Data Science: Biology, Business Administration, English, History, Psychology, Political Science, Mathematics, Sociology, etc.
Computer Science Courses
There are 22-24 credits in computational thinking.
| CSCI 110 | INTRODUCTION TO COMPUTER SCIENCE I | 3 |
| CSCI 110L | INTRODUCTION TO COMPUTER SCIENCE I LABORATORY | 1 |
| CSCI 120 | INTRODUCTION TO COMPUTER SCIENCE II | 3 |
| CSCI 120L | INTRODUCTION TO COMPUTER SCIENCE II LABORATORY | 1 |
| CSCI 241 | DATA STRUCTURES AND ALGORITHMS | 3 |
| CSCI 241L | DATA STRUCTURES AND ALGORITHMS LAB | 1 |
| CSCI 312 | DATABASE MANAGEMENT | 3 |
| CSCI 312L | DATABASE MANAGEMENT LAB | 1 |
| | |
| CSCI 280 | MATHEMATICAL MODELLING OF BIOLOGICAL SYSTEMS | 3 |
| CSCI 280L | MATHEMATICAL MODELLING OF BIOLOGICAL SYSTEMS LAB | 1 |
| Or | |
| PHYS 117 | PHYSICS FOR THE LIFE SCIENCES I | 3 |
| PHYS 117L | LABORATORY AND RECITATION FOR PHYSICS FOR THE LIFE SCIENCES I | 1 |
| Or | |
| MGT 330 | PRODUCTION AND OPERATIONS MANAGEMENT | 3 |
| | |
| CSCI 380 | MACHINE LEARNING | 3 |
| CSCI 380L | MACHINE LEARNING LAB | 1 |
| Or | |
| CSCI 390 | SPECIAL TOPICS | 3 or 4 |
| Total Credit Hours: | 22-24 |
Seminar Courses
Mathematics courses
| MATH 120 | CALCULUS I | 4 |
| Math course approved by the Advisor | |
| Total Credit Hours: | 4 |
Statistical Courses
At least one (1) Statistics course is required.
Partner Discipline Courses
18-20 hours of any major (approved by the advisor)
Requirements - B.S.
Computer Science Courses
There are 22-24 credits in computational thinking.
| CSCI 110 | INTRODUCTION TO COMPUTER SCIENCE I | 3 |
| CSCI 110L | INTRODUCTION TO COMPUTER SCIENCE I LABORATORY | 1 |
| CSCI 120 | INTRODUCTION TO COMPUTER SCIENCE II | 3 |
| CSCI 120L | INTRODUCTION TO COMPUTER SCIENCE II LABORATORY | 1 |
| CSCI 241 | DATA STRUCTURES AND ALGORITHMS | 3 |
| CSCI 241L | DATA STRUCTURES AND ALGORITHMS LAB | 1 |
| CSCI 312 | DATABASE MANAGEMENT | 3 |
| CSCI 312L | DATABASE MANAGEMENT LAB | 1 |
| | |
| CSCI 280 | MATHEMATICAL MODELLING OF BIOLOGICAL SYSTEMS | 3 |
| CSCI 280L | MATHEMATICAL MODELLING OF BIOLOGICAL SYSTEMS LAB | 1 |
| Or | |
| PHYS 117 | PHYSICS FOR THE LIFE SCIENCES I | 3 |
| PHYS 117L | LABORATORY AND RECITATION FOR PHYSICS FOR THE LIFE SCIENCES I | 1 |
| Or | |
| MGT 330 | PRODUCTION AND OPERATIONS MANAGEMENT | 3 |
| | |
| CSCI 380 | MACHINE LEARNING | 3 |
| CSCI 380L | MACHINE LEARNING LAB | 1 |
| Or | |
| CSCI 390 | SPECIAL TOPICS | 3 or 4 |
| Total Credit Hours: | 22-24 |
Seminar Courses
Mathematics courses
Statistical Courses
At least one (1) Statistics course is required.
Partner Discipline Courses
- 10-12 hours of any major (approved by the advisor)
CORE Curriculum Requirements
The CORE curriculum is required for all majors.
Suggested Program Plan for Data Science Majors:
First Year, Fall Semester
First Year, Spring Semester
| CSCI 120 | INTRODUCTION TO COMPUTER SCIENCE II | 3 |
| CSCI 120L | INTRODUCTION TO COMPUTER SCIENCE II LABORATORY | 1 |
| Math course approved by the Advisor | |
| CORE 120 | CRITICAL THINKING | 2 |
| CORE 160 | COMPOSITION II | 3 |
| | |
| SPAN 102 | ELEMENTARY SPANISH II | 3 |
| Or | |
| FREN 102 | ELEMENTARY FRENCH II | 3 |
| Total Credit Hours: | 16 |
Second Year, Fall Semester
| CSCI 210 | SOPHOMORE SEMINAR | 1 |
| CSCI 241 | DATA STRUCTURES AND ALGORITHMS | 3 |
| CSCI 241L | DATA STRUCTURES AND ALGORITHMS LAB | 1 |
| MATH 125 | DISCRETE MATHEMATICS | 3 |
| CORE | 3 |
| CORE | 3 |
| GENERAL ELECTIVE | 3 |
| Total Credit Hours: | 17 |
Second Year, Spring Semester
| CSCI 312 | DATABASE MANAGEMENT | 3 |
| CSCI 312L | DATABASE MANAGEMENT LAB | 1 |
| CORE | 3 |
| GENERAL ELECTIVE | 3 |
| GENERAL ELECTIVE | 3 |
| Partner Discipline Elective | 3 |
| Total Credit Hours: | 16 |
Third Year, Fall Semester
| CSCI 310 | JUNIOR SEMINAR | 1 |
| | |
| CSCI 280 | MATHEMATICAL MODELLING OF BIOLOGICAL SYSTEMS | 3 |
| CSCI 280L | MATHEMATICAL MODELLING OF BIOLOGICAL SYSTEMS LAB | 1 |
| Or | |
| PHYS 117 | PHYSICS FOR THE LIFE SCIENCES I | 3 |
| PHYS 117L | LABORATORY AND RECITATION FOR PHYSICS FOR THE LIFE SCIENCES I | 1 |
| | |
| NSCI 360 | STATISTICS | 3 |
| Or | |
| BAD 260 | APPLIED STATISTICS | 3 |
| Or | |
| HSS 290 | BEHAVIORAL STATISTICS | 3 |
| Or | |
| MATH 390B | SPECIAL TOPICS IN MATHEMATICS: BIOSTATISTICS | 3 |
| | |
| CORE | 3 |
| Partner Discipline Elective | 3 |
| Total Credit Hours: | 14 |
Third Year, Spring Semester
| CSCI 380 | MACHINE LEARNING | 3 |
| Partner Discipline Elective | 3 |
| CORE | 3 |
| CORE | 3 |
| GENERAL ELECTIVE | 3 |
| Total Credit Hours: | 15 |
Fourth Year, Fall Semester
| CSCI 411 | SENIOR SEMINAR I | 2 |
| CORE | 3 |
| Partner Discipline Elective | 3 |
| Partner Discipline Elective | 3 |
| GENERAL ELECTIVE | 3 |
| Total Credit Hours: | 14 |
Fourth Year, Spring Semester
| CSCI 412 | SENIOR SEMINAR II | 2 |
| CORE | 3 |
| Partner Discipline Elective | 3 |
| Partner Discipline Elective | 3 |
| GENERAL ELECTIVE | 3 |
| Total Credit Hours: | 14 |
Suggested Program - B.S.
First Year, Fall Semester
First Year, Spring Semester
Second Year, Fall Semester
| CSCI 210 | SOPHOMORE SEMINAR | 1 |
| CSCI 241 | DATA STRUCTURES AND ALGORITHMS | 3 |
| CSCI 241L | DATA STRUCTURES AND ALGORITHMS LAB | 1 |
| MATH 125 | DISCRETE MATHEMATICS | 3 |
| CORE | 3 |
| GENERAL ELECTIVE | 3 |
Second Year, Spring Semester
| CSCI 312 | DATABASE MANAGEMENT | 3 |
| CSCI 312L | DATABASE MANAGEMENT LAB | 1 |
| MATH 240 | LINEAR ALGEBRA | 3 |
| CORE | 3 |
| GENERAL ELECTIVE | 3 |
| GENERAL ELECTIVE | 3 |
| Total Credit Hours: | 16 |
Third Year, Fall Semester
| CSCI 310 | JUNIOR SEMINAR | 1 |
| | |
| CSCI 280 | MATHEMATICAL MODELLING OF BIOLOGICAL SYSTEMS | 3 |
| CSCI 280L | MATHEMATICAL MODELLING OF BIOLOGICAL SYSTEMS LAB | 1 |
| Or | |
| PHYS 117 | PHYSICS FOR THE LIFE SCIENCES I | 3 |
| PHYS 117L | LABORATORY AND RECITATION FOR PHYSICS FOR THE LIFE SCIENCES I | 1 |
| | |
| NSCI 360 | STATISTICS | 3 |
| Or | |
| BAD 260 | APPLIED STATISTICS | 3 |
| Or | |
| HSS 290 | BEHAVIORAL STATISTICS | 3 |
| Or | |
| MATH 390B | SPECIAL TOPICS IN MATHEMATICS: BIOSTATISTICS | 3 |
| | |
| CORE | 3 |
| CORE | 3 |
| Total Credit Hours: | 14 |
Third Year, Spring Semester
| CSCI 380 | MACHINE LEARNING | 3 |
| Partner Discipline Elective | 3 |
| CORE | 3 |
| CORE | 3 |
| GENERAL ELECTIVE | 3 |
| Total Credit Hours: | 15 |
Fourth Year, Fall Semester
| CSCI 411 | SENIOR SEMINAR I | 2 |
| CORE | 3 |
| Partner Discipline Elective | 3 |
| Partner Discipline Elective | 3 |
| GENERAL ELECTIVE | 3 |
| Total Credit Hours: | 14 |
Fourth Year, Spring Semester
| CSCI 412 | SENIOR SEMINAR II | 2 |
| CORE | 3 |
| Partner Discipline Elective | 3 |
| Partner Discipline Elective | 3 |
| GENERAL ELECTIVE | 3 |
| Total Credit Hours: | 14 |
Total Credit Hours: 120