Data Science and Analytics - Program Description
The Bachelor of Science in Data Science and Analytics is an interdisciplinary program that combines data science with data analytics, mathematics, statistics, artificial intelligence and computer science. Students will hone analytical and hands-on skills to recognize value that can be derived from data collection to data analysis. Experiential learning is a core component of the program applying programming languages, predictive and prescriptive statistical models, data mining, and machine learning to data-based decision making. Special emphasis is placed on ethics in data science, data anonymization, security and privacy, with an applied focus on making mathematically valid and ethically sound decisions. Subjects from computer logic, statistical methods, discrete mathematics, data visualization, machine learning, data mining, R-programming, big data and deep-learning are covered. Graduates from the program will be prepared to work as data scientists and analysts or can continue their education in graduate level data science and analytics programs.
Students in this Major
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Apply statistical and computational techniques to analyze and interpret large and complex datasets.
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Will be proficient in data collection, cleaning, storage, and management using various tools and technologies, ensuring data integrity and accessibility.
- Will be skilled in programming languages such as Python, R, and SQL, and will be able to develop software solutions and applications tailored to data science problems.
- Are encouraged to pursue Data Science internships during their senior year.
Admission Requirements
- Refer to the table of high school course prerequisites for admission.
- Students must be qualified to enter at least College Algebra (MATH 121) and Composition and the Spoken Word (ENGL 101).
- Computer or technology courses are strongly recommended.
- Transfers into this program must have a 2.0 GPA for admission. Students from other institutions and majors may have to complete certain bridge courses that could extend their graduation date.
Students who do not meet necessary prerequisites may be admitted to the College. However, completing the program may require more than four years.
PROGRAM REQUIREMENTS* (Curriculum 3262)
The following shows the typical course sequence and program requirements.
Semester I Credits
| Prefix | Course Name | Credits |
|---|---|---|
| CYBR 153 | Computer Logic & Algorithms | 3 |
| DATA 155 | Survey of Data Science | 3 |
| ENGL 101 | Composition and the Spoken Word (GER 1/2) | 3 |
| Mathematics Elective1 (GER 4) | 3-4 | |
| Natural Science Elective (GER 5) | 3-4 | |
| TOTAL CREDITS | 15-17 |
Semester II Credits
| Prefix | Course Name | Credits |
|---|---|---|
| CYBR 172 | Computer Fundamentals | 3 |
| MATH 123 | Pre-Calculus (GER 4) | 3 |
| MATH 141 | Statistics (GER 4) | 3 |
| Liberal Arts Elective (GER 3) | 3 | |
| Liberal Arts Elective (GER 6, 8, 9, 10, 11) | 3 | |
| TOTAL CREDITS | 16 |
Semester III Credits
| Prefix | Course Name | Credits |
|---|---|---|
| CYBR 181 | Programming Fundamentals | 4 |
| CYBR 216 | Database Fundamentals | 3 |
| DATA 230 | Applied Data Science R-Programming | 3 |
| MATH 161 | Calculus I | 3 |
| Liberal Arts Elective (GER 6, 8, 9, 10, 11) | 3 | |
| TOTAL CREDITS | 17 |
Semester IV Credits
| Prefix | Course Name | Credits |
|---|---|---|
| DATA 240 | AI Fundamentals (WI)** | 3 |
| DATA 315 | Data Mining & Machine Learning | 3 |
| Liberal Arts Elective (GER 7) | 3 | |
| Liberal Arts Elective (Any GER) | 3 | |
| Liberal Arts Elective | 3 | |
| TOTAL CREDITS | 15 |
Semester V Credits
| Prefix | Course Name | Credits |
|---|---|---|
| DATA 320 | Data Security and Privacy | 3 |
| DATA 371 | Methods of Data Representation | 3 |
| MATH 351 | Discrete Mathematics | 3 |
| General Elective | 3 | |
| Liberal Arts Elective | 3 | |
| TOTAL CREDITS | 15 |
Semester VI Credits
| Prefix | Course Name | Credits |
|---|---|---|
| DATA 415 | Ethics in Data Science | 3 |
| DATA 420 | Advanced Data Mining and Machine Learning | 3 |
| DATA 421 | Deep-Learning Fundamentals | 3 |
| General Elective | 3 | |
| Liberal Arts Elective | 3 | |
| TOTAL CREDITS | 15 |
Semester VII Credits
| Prefix | Course Name | Credits |
|---|---|---|
| DATA 321 | Big Data Fundamentals | 3 |
| U/L Liberal Arts Elective | 3 | |
| U/L Liberal Arts Elective | 3 | |
| U/L Liberal Arts Elective | 3 | |
| TOTAL CREDITS | 12 |
Semester VIII Credits
| Prefix | Course Name | Credits |
|---|---|---|
| DATA/CYBR 485 | Cybersecurity/Data Practice OR U/L Program Elective2 U/L Program Elective2 |
6 |
| U/L General Elective | 3 | |
| U/L Liberal Arts Elective | 3 | |
| U/L Liberal Arts Elective | 3 | |
| TOTAL CREDITS | 15 |
** Fulfills writing intensive requirement.
U/L = Upper Level Courses (300/400)
GER = General Education Requirement
LA = Liberal Arts and Sciences
Students in this program must take at least 45 UD credits and a minimum of 60 LA credits.
Students need to pass a total of 30 GER credits with a course in at least 7 of the 10 GER categories.
Review the Index of Course Descriptions
Cybersecurity Department Chair
Kambiz Ghazinour
Professor
ghazinourk@canton.edu
Mehdi Ghayoumi
Assistant Professor
ghayoumi@canton.edu