Data Science

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)

Info Sheet

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

 

 

 

 

 

 

Math 630 Linear Algebra and Applications
CS 602 Java Programming
CS 610 Data Structures and Algorithms
CS 631 Data Management System Design
CS 632 Advanced Database System Design
CS 634 Data Mining
CS 670 Artificial Intelligence
CS 732 Advanced Machine Learning
CS 735 High Performance Analytics for Data Science
IS 601 Web Systems Development
IS 631 Enterprise Database Management
IS 634 Information Retrieval
IS 665 Data Analytics for Information Systems
IS 687 Transaction Mining and Fraud Detection
IS 688 Web Mining


 


 

 

 

 

 

 

 

 

 

 

 

 

 

 

To be eligible for admission, a student must have a B.S. degree with a minimum GPA of 2.8 on a 4.0 scale and have the following background (typically obtained through a BS in a STEM field):

  • Calculus: Derivatives, integrals, applications
  • Linear Algebra: Vector spaces, dot products, Euclidean norm, matrices
  • Probability and Statistics: Random variables, probability distributions, basic statistics
  • Programming: Basic programming constructs, writing and debugging programs, iteration, recursion, arrays, lists
  • Data Structures and Algorithms: Basic data structures, search and sort, algorithm analysis


Applicants lacking this background may take the Refresher boot camp to acquire it.

A GRE score is not required
 

  • Be able to acquire, clean, and manage massive data sets.
  • Play an analytical role in your company where you design, implement, and evaluate advanced statistical models and approaches for application to your company’s most complex problems.
  • Be able to provide econometric and statistical models for a variety of problems including projections, classification, clustering, pattern analysis, sampling and simulations.
  • Research new ways for predicting and modeling end-user behavior as well as investigating data summarization and visualization techniques for conveying key applied analytics findings.
  • Apply modern artificial intelligence and deep learning methods to complex prediction and recognition tasks.

 

Tuition and fees for ALL non-F1 students (independent of residency and visa status) at 2019-20 rates, assuming two courses per consecutive semester:

  • Fall semester: $6,366
  • Spring semester: $6,366
  • Summer semester: $5,421


Total tuition and fees for degree:

  • Fall start: $30,885
  • Spring start: $29,850
  • Summer start: $29,850

 

Program Contact:
Tim Hart
Enrollment Services Manager
973-596-2911
862-234-5706
hart@njit.edu

Jersey City Class Tour Contact:
Serena Branson
branson@njit.edu

Stephanie D. Smith
stephanie.d.smith@njit.edu

For more information visit jerseycity.njit.edu