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M.S. in Data Science
Provides students a strong understanding of basic and advanced methods in statistical inference, machine learning, data visualization, data mining, and big data, all of which are essential skills for a high-performing data scientist.
M.S. in Data Science (30 Credits)
In today’s AI-driven economy, there is a strong demand for data scientists equipped with computational skills to develop, design and apply models and tools for data-driven decision making. Companies use data science and AI for marketing decisions, targeted customer recommendations, determination of profitable insurance coverage as well as for providing personalized financial advice.
The M.S. in Data Science covers basic and advanced methods in statistical inference, machine learning, data visualization, data mining, and big data, all of which are essential skills for a high-performing data scientist. To be admitted to the program, we require a basic background in Mathematics (calculus, linear algebra), Statistics (probability and basic stats) and Software Development (programming, data structures and algorithms). A GRE score is not required. This part-time degree program involves 10 courses of 3 credits each, taught over 5 semesters of 15 weeks each (including summer). Courses consist of formal lectures as well as hands-on programming projects.
The program curriculum uses the Python programming language with its data science libraries and features tools like R for statistical analysis and Tableau for data visualization. Students work on homework assignments and projects covering both theory and applications on real data with guidance from the professor and teaching assistants.
Recommended part-time credit schedule: Two courses (6 credits) per semester over five consecutive semesters, including Summer. Start is possible in Fall, Spring or Summer semesters.
Required Courses
| Math 661 | Applied Statistics |
| CS 644 | Introduction to Big Data |
| CS 636 | Data Analytics with R Programming |
| CS 675 | Machine Learning |
| CS 677 | Deep Learning |
Master of Information Technology and Analytics
In today’s world, the ability to combine technical knowledge with business management strategies is crucial to continued success. The Master of Information Technology and Analytics (MITA) is designed to help you do just that.
Our program produces leaders who are capable of managing significant software development projects and leading teams of information technology professionals. Understanding the languages of business and IT puts you in a prime position for career advancement and opens doors for opportunities in various industries.
Program Highlights:
- 30 credits
- Full-time or part-time study
- Finish in as little as one year (full-time)
- STEM qualified program for international students considering Optional Practical Training (OPT)
- Based out of Newark campus
Select Event – Select -04/08/2021 Master of Information Technology and Analytics Information Session – 9 am Eastern05/07/2021 Master of Information Technology and Analytics Information Session – 7 pm EasternFirst Name Last Name Email Address Mobile Number By submitting this form, you agree to receive emails, text messages, telephone calls, and prerecorded messages from Rutgers Business School regarding educational programs. You understand that such calls, emails, and messages may be sent using automated technology. You may opt out at any time. Please view our Privacy Policy or Contact Us for more details.“This program provided the perfect platform to steer my career towards data analytics. The course structure offers an ideal mix of both management theories and practical case studies. It has been the most unique and cherished year of my life.”— Divya Venkata, Deloitte
STEM Designation- International Students
The MITA program qualifies as a Science, Technology, Engineering, or Mathematics (STEM) program for international students that are considering Optional Practical Training (OPT). Enrollment in a STEM program may qualify you for a 36 month OPT which allows you to work in the US in a job related to your field of study during the program or after graduation.
Computer Science
- COMS W4121 Computer Systems for Data Science
- COMS W4721 Machine Learning for Data Science
- CSOR W4246 Algorithms for Data Science
Engineering
- ENGI E4800 Data Science Capstone and Ethics
Statistics
- STAT GR5701 Probability and Statistics for Data Science
- STAT GR5702 Exploratory Data Analysis and Visualization
- STAT GR5703 Statistical Inference and Modeling
Electives
- Details
- COMS W4995 Topics in Computer Science: Applied Machine Learning
- COMS W4995 Topics in Computer Science: Applied Deep Learning
- COMS W4995 Topics in Computer Science: Causal Inference for Data Science
- COMS W4995 Topics in Computer Science: Data Analytics Pipeline
- COMS W4995 Topics in Computer Science: Elements of Data Science
- COMS E6998 Topics in Computer Science: Machine Learning with Probabilistic Programming
- COMS E6998 Natural Language Processing: Computational Models of Social Meaning
- EECS E6894 Topics in Information Processing: Deep Learning for Computer Vision, Speech, and Language
- IEOR E4571 Topics in Operations Research: Personalization Theory & Application
- IEOR E4721 Topics in Quantitative Finance: Big Data in Finance
- STATS GR5293 Topics in Modern Statistics: Applied Machine Learning for Financial Modeling and Forecasting
- STATS GR5293 Topics in Modern Statistics: Applied Machine Learning for Image Analysis
- Cross-Registration Instructions for Non-Data Science Students
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