Data Science
4 YEARS | ENGLISH TAUGHT | ON CAMPUS
The importance of data in problem-solving, decision-making, and strategy has become ever more apparent in the last decade. We live in a world of big data. Many of our everyday actions, from watching videos on our phones to purchasing a product online create data. Since the advent of the technological revolution, data has played an increasingly important role in many fields and spheres of life. For this reason, interpreting and understanding data has become a highly-valued skill. Data science is the study and examination of data with the purpose of understanding patterns. It uses tools, techniques, and theories to produce a meaningful interpretation of data for decision-making and development. It is an interdisciplinary field of study that uses scientific methods, programming skills and knowledge of mathematics and statistics to extract insight from data. Degrees in this field allow a diverse range of specializations and prepare students for many different occupations.
AIMS & OBJECTIVES
The importance of data in problem-solving, decision-making, and strategy has gained prominence over the last decade. We live in a world of big data. Many of our everyday actions, from watching videos on our phones to purchasing a product online create data. Since the advent of the technological revolution, data has played an increasingly important role in many fields and spheres of life. That’s why interpreting and understanding data has become such a valuable skill. Data science is the study and examination of data with the purpose of understanding patterns. It uses tools, techniques, and theories to produce a meaningful interpretation of data for decision-making and development. It is an interdisciplinary field of study that uses scientific methods, programming skills, and knowledge of mathematics and statistics to extract insight from data. Degrees in this field allow a diverse range of specializations and occupations.
CURRICULUM
| DATA SCIENCE CURRICULUM | |||||
| First Semester | Credits | Second Semester | Credits | ||
| CS 141 | Intro to Comp Sci I | 4 | CS 142 | Intro to Comp Sci II | 3 |
| MA 131 | Calculus I | 3 | MA 132 | Calculus II | 3 |
| UNIV 190 | Clarkson Seminar | 3 | MA 200 | Math Modelling and Software | 3 |
| FY 100 | First-Year Seminar | 1 | Knowledge Area Course | 3 | |
| Science Elective | 4 | Science Elective | 4 | ||
| Total | 15 | Total | 16 | ||
| Third Semester | Credits | Fourth Semester | Credits | ||
| DS 241 | Intro to Data Science | 3 | CS 344 | Algorithm and Data Structure | 3 |
| IS 314 | Database Design and Management | 3 | IS 415 | Data Warehousing for Analytics | 3 |
| MA 211 | Discrete Math and Proof | 3 | MA 231 | Calculus III | 3 |
| STAT 383 | Probability and Statistics | 3 | MA 339 | Applied Linear Algebra | 3 |
| Knowledge Area Course | 3 | Knowledge Area Course | 3 | ||
| Total | 15 | Total | 15 | ||
| Fifth Semester | Credits | Sixth Semester | Credits | ||
| CS 449 | Computational Learning | 3 | DS 392 | Ethics of Data Analytics | 3 |
| IS 426 | Big Data Architecture | 3 | STAT 382 | Mathematical Statistics* | 3 |
| STAT 381 | Probability | 3 | Knowledge Area/ University Courses | 3 | |
| Knowledge Area/ University Course | 3 | Free Electives | 6 | ||
| Free Elective | 3 | ||||
| Total | 15 | Total | 15 | ||
| Seventh Semester | Credits | Eighth Semester | Credits | ||
| MA 499 | Professional Experience | 0 | STAT 384 | Advanced Applied Statistics | 3 |
| STAT 385 | Bayesian Data Analysis | 3 | STAT 488 | Statistics Projects | 2 |
| Application Elective | 3 | Application Electives | 3 | ||
| Free Electives | 9 | Free Electives | 6 | ||
| Total | 15 | Total | 14 | ||