Why Elms for your Data Science and AI degree?
- This program can be completed either in person or online
- Join the cutting-edge rapidly growing AI field
- Combine your interests in interdisciplinary fields such as Business, Biology, Computer Science, Math, Psychology, etc.
- Analyze and learn from data to predict the future!
Data Science and Artificial Intelligence
Elms College is excited to introduce its new Data Science and Artificial Intelligence major (DS & AI) designed to equip students with the cutting-edge skills needed for the digital age. In our internet connected world, large volumes of data are being generated every day, and data scientists are needed to analyze and extract knowledge from this data. This interdisciplinary major uses scientific, mathematical, computational, and business analytic methods to process and analyze data.
Data Science skills serve as the foundational bridge to the world of Artificial Intelligence (AI) and its transformative subfield, machine learning (ML), where mathematical and programming skills are used to learn from data and predict future trends. AI will shape the future of technology and drive innovation to new horizons.
The new DS-AI major will provide students with a strong foundation in data science and AI machine learning techniques which can be applied to many different fields including Biology, Biotechnology, Business, Finance, Health Sciences, Psychology, and Social Sciences. Students in this major can choose a focus area in one of these fields to apply the skills they learn in the core data science and AI courses. Students will gain practical experience through hands-on projects and internships in focus areas tied to their fields of interest.
Join a Cutting Edge Profession
A degree in Data Science and AI opens up a wealth of exciting career opportunities in today’s data-driven world. Graduates can pursue roles such as Data Analyst, Business Analyst, Data Scientist, Machine Learning Engineer, AI Engineer, among others. These professionals play pivotal roles in various industries, including healthcare, finance, e-commerce, and technology, by leveraging data to inform decision-making, develop predictive models, and drive innovation. With the ever-increasing demand for data-driven insights, the career prospects for individuals with expertise in Data Science and AI are boundless.
The U.S. Bureau of Labor has predicted a 34% growth in jobs for Data Scientists and a 21% growth in Computer and Information Research Scientists jobs from 2021 to 2031. Glassdoor.com has listed Data Scientist and Machine Learning Engineer among the top 6 best jobs in the U.S. for the last couple years based on salary ($120,000-130,000 median salary) and job satisfaction with 10,000+ job openings.
According to a report by LinkedIn in 2020, the number of AI job postings on LinkedIn has grown by 74% in the past four years. Massachusetts is one of the top states for AI related job postings. This trend is expected to continue as the use of AI and machine learning becomes more widespread across various industries.
This program can be completed either in person or online.
Join Elms College in shaping the future through the power of Data Science and AI!
Quick Info
- Cutting-edge field
- Small class sizes
- Career-oriented curriculum
- Internships at organizations throughout the New England region. Students have recently interned at Station1, Quest Global, and Mass Mutual.
Joe Gaszi ’17
“Knowing that people across the country are seeing my work, and that it’s helping them get in contact with the company I work for, is awesome.”
Joe Gaszi ’17 got a job as a software developer at 1st Alliance Lending, a mortgage loan company, after graduating in only three years. His first project involved redesigning the company’s consumer portal to make it easier for homebuyers to obtain a mortgage loan online. Read Joe’s story.
Megan Keyes, Fall ‘22
“You learn a lot in the classes. The classes are well put-together and based on things you actually use on the job, which is not necessarily the case with other computer science programs. Dr. Hoffman’s programming classes emphasize skills you need in the industry – and I know this because I have a software engineering job.” Read Megan’s story.
Curriculum
Click to view course requirements for the Data Science and Artificial Intelligence (B.S.).
Take 10 CORE courses, and then choose ONE FOCUS AREA, made up of 4 courses.
Required 10 Core Courses (30 credits):
| Course # | Course Name | # of Credit Hours |
|---|---|---|
| MAT 1010 | Computational Statistics or MAT 1009 or MAT 1101 | 3 |
| MAT 1301 | Differential Calculus 1 | 3 |
| MAT 2005 | Linear Algebra | 3 |
| CIT 1006 | Cyber Ethics or ETH 3101 Ethics of AI | 3 |
| CIT 2103 | Intro to Programming in Python | 3 |
| CIT 3100 | Intro Data Analytics & Visualization | 3 |
| CIT 2100 | Artificial Intelligence | 3 |
| CIT 3710 | Advanced Data Analytics & Modeling | 3 |
| CIT 3720 | Machine Learning | 3 |
| CIT 4105 | Data Analytics Capstone | 3 |
| Course # | Course Name | # of Credit Hours |
|---|---|---|
| Focus Areas (12 credits): Students should choose 12 credits from the following courses to create a Focus Area. Courses from different Focus Areas may also be combined for these 12 credits. |
Computer Science and Machine Learning Focus Area (Choose 12 credits/4 courses from the following):
| Course # | Course Name | # of Credit Hours |
|---|---|---|
| CIT 2105 | Database Design and Management | 3 |
| CIT 2210 | Advanced Python | 3 |
| CIT 3102 | Advanced Programming | 3 |
| CIT 3302 | Data Structures and Algorithms | 3 |
| CIT 3420 | Virtualization & Cloud Computing | 3 |
| CIT 4203 | Prof. Programming Project (using ML) | 3 |
| CIT 4802 | Internship | 3 |
| MAT 3700 | Discrete Math | 3 |
Mathematical and Statistical Analysis Focus Area (Choose 12 credits/4 courses from the following):
| Course # | Course Name | # of Credit Hours |
|---|---|---|
| MAT 1302 | Integral Calculus 2 | 3 |
| MAT 2003 | Vector Calculus | 3 |
| MAT 3009 | Differential Equations | 3 |
| MAT 3100 | Numerical Analysis | 3 |
| MAT 3105 | Probability and Statistics | 3 |
| MAT 3205 | Actuary Exam Prep | 3 |
| MAT 3605 | Actuarial/Engineering Statistics | 3 |
| MAT 3700 | Discrete Math | 3 |
| MAT 4300 | Actuarial Math | 3 |
| MAT 4802 | Internship | 3 |
| New courses may be developed in Math in advanced statistics | 3 |
Biology and Biotechnology Focus Area (Choose 12 credits, including 3 lab credits from the following)
| Course # | Course Name | # of Credit Hours |
|---|---|---|
| BIO 2330 and L | Biotechnology and lab | 3 |
| BIO 3101 and L | Ecology and lab | 3 |
| BIO 3201 and L | Genetics and lab | 3 |
| BIO 3206 and L | Molecular Biology | 3 |
| BIO 3330 and L | Advanced Biotechnology and lab | 3 |
| BIO 4009 | Research and Independent Study | 3 |
| BIO 4010 | Research Methods | 3 |
| BIO 4011 | Research Experience | 3 |
| BIO 4306 and L | Biochemistry and lab | 3 |
| BIO 4330 | Biotechnology Capstone | 3 |
| BMS 5009 | Epidemiology & Biostatistics | 3 |
Business Analytics Focus Area (Choose 12 credits/4 courses from the following where 2 are required)
| Course # | Course Name | # of Credit Hours |
|---|---|---|
| ACC 2001 | Accounting I (required) | 3 |
| ACC 2002 | Accounting II (required) | 3 |
| ACC 3001 | Intermediate Accounting I | 3 |
| ACC 3500 | Accounting Information Systems | 3 |
| BUS 2201 | Computer Applications | 3 |
| BUS 2409 | Operations Management | 3 |
| BUS 3101 | Principles of Financial Management | 3 |
| BUS 3109 | Personal Finance | 3 |
| BUS 3605 | Marketing Research | 3 |
| BUS 4802 | Internship | 3 |
Social Sciences Focus Area (Choose 12 credits/4 courses from the following)
| Course # | Course Name | # of Credit Hours |
|---|---|---|
| PSY 1001 | Introduction to Psychology | 3 |
| PSY 2002 | Statistics for the Behavioral Science | 3 |
| PSY 2100 | Research Methods | 3 |
| PSY 3007 | Psychological Testing | 3 |
| PSY 3008 | Cognitive Psychology | 3 |
| PSY 4000 | Independent Study | 3 |
| SOC 1001 | Introduction to Sociology | 3 |
| SOC 3005 | Social Research | 3 |
| SOC 4004 | Internship | 3 |
| SWK 2004 | Human Behavior and the Social Environment | 3 |
Click to view course requirements for the Data Analytics minor.
Required Courses (9 credits)
| Course # | Course Name | # of Credit Hours |
|---|---|---|
| MAT 1009 | Statistics (or, variations of the course offered by the Division of Business) | 3 |
| CIT 3100 | Data Analytics | 3 |
| CIT 4105 | Data Analytics Capstone | 3 |
Elective Courses (9 credits)
| Course # | Course Name | # of Credit Hours |
|---|---|---|
| Choose 3 courses from the following electives, which may be courses in your major. | 3 | |
| BIO 3201 | Genetics | 3 |
| BIO 5201 | Genetics | 3 |
| BIO 3330 | Biotechnology | 3 |
| BIO 5330 | Biotechnology | 3 |
| BMS 5009 | Epidemiology & Biostatistics | 3 |
| BUS 2201 | Computer Applications | 3 |
| BUS 5002 | Excel Foundations/MBA | 3 |
| CIT 2103 | Introduction to Programming | 3 |
| CIT 2105 | Database Design | 3 |
| CIT 2100 | Artificial Intelligence | 3 |
| CIT 3102 | Advanced Programming | 3 |
| CIT 4205 | Special Topics: Python | 3 |
| MAT 2005 | Linear Algebra | 3 |
| MAT 3105 | Probability and Statistics | 3 |
| MAT 3700 | Discrete Math | 3 |
| MAT 4300 | Actuarial Math | 3 |
| PSY 2002 | Statistics for Behavioral Science | 3 |
| PSY 2100 | Research Methods | 3 |
| PSY 4000 | Independent Study | 3 |