Master’s in Data Science

2 Years On Campus Masters Program

New York University

Program Overview

New York University's Master of Science in Data Science is a highly selective interdisciplinary programme designed for students with strong foundations in mathematics, computer science, and applied statistics. The programme develops advanced expertise in machine learning, data engineering, statistical analysis, and computational methods through rigorous coursework, practical projects, and industry collaborations, while allowing students to pursue specialized interests in Data Engineering, Mathematics and Data, Natural Language Processing, or Biomedical Informatics.

Curriculum Structure

Year 1: Foundational Knowledge and Core Data Science Skills

During the first year, students develop essential knowledge of data science, mathematical statistics, programming, and computational techniques. In the first semester, students study Introduction to Data Science, Probability and Statistics for Data Science, and a general elective or track course. In the second semester, they progress to Machine Learning and Data Engineering, formerly known as Big Data, while selecting additional electives to strengthen their technical and analytical abilities.

Year 2: Advanced Specialization and Practical Application

In the second year, students develop advanced expertise through specialized electives aligned with their chosen interests. Available subjects include Deep Learning, Fundamentals of Natural Language Processing, Large Language Models: Evaluation and Applications, Optimization and Computational Linear Algebra, and Mathematical Tools for Data Science. Students also complete the Capstone Project and Presentation, where they collect and process real-world datasets, develop suitable analytical methods, and implement solutions to practical problems encountered in industry, academia, or government.

Focus Areas

Data Science and Advanced Analytics
Machine Learning and Artificial Intelligence
Data Engineering and Big Data
Probability and Statistical Modelling
Natural Language Processing
Deep Learning and Large Language Models
Mathematical Modelling and Optimization
Computational Statistics
Biomedical Informatics
Data Mining and Knowledge Discovery
Predictive Analytics
Responsible Data Science

Learning Outcomes

Graduates of this programme will be able to:

Apply mathematical, statistical, and computational techniques to solve complex data science problems.
Develop and implement machine learning models for predictive analysis and decision-making.
Collect, process, manage, and analyse large-scale datasets.
Design efficient data engineering solutions and data processing systems.
Apply deep learning and natural language processing techniques to real-world applications.
Interpret statistical results and communicate meaningful insights from complex datasets.
Develop computational methods for analysing structured and unstructured data.
Apply optimization techniques and mathematical models to practical challenges.
Evaluate ethical considerations and responsible practices in data science.
Design and implement data-driven solutions through independent research and capstone projects.

Professional Alignment (Accreditation)

The official university programme information does not specify a separate professional accreditation for the MS in Data Science. However, the programme's curriculum, industry concentration, practical training, internship opportunities, and capstone project provide relevant preparation for professional careers in data science, machine learning, artificial intelligence, and data analytics.

Reputation (Employability Rankings)

Graduate Alumni Salary: According to NYU Center for Data Science's graduate exit survey, the median salary reported for master's alumni is $120,000.
Technology Sector: 38.46% of the reported MS alumni work in the technology sector, with employers including Amazon, Meta, Microsoft, and Verisk.
Financial Sector: 24.36% of reported MS alumni work in financial services, with organisations including American Express, TD Bank, and Morgan Stanley.
Consulting Sector: 5.13% of reported MS alumni work in consulting, with employers including Boston Consulting Group, McKinsey & Company, and QuantumBlack.
Industry Connections: NYU provides career advising, company information sessions, career fairs, and internship-related professional development opportunities.

Employment figures are based on the university's published graduate exit survey data; they are not a guarantee of individual employment outcomes.

Experiential Learning (Research, Projects, Internships etc.)

At New York University, the MS in Data Science program is designed to provide students with hands-on experience that bridges the gap between theoretical knowledge and real-world application. Students engage in practical learning through various projects, internships, and access to cutting-edge facilities and tools that enhance their educational journey. The program emphasizes collaboration and innovation, ensuring that graduates are well-prepared for the demands of the data science industry.

Here are some key aspects of experiential learning in the MS Data Science program at NYU:

- Data Science Labs: Students have access to specialized labs equipped with advanced computing resources and software tools essential for data analysis and machine learning.

- Group Projects: Collaborative projects are a core component of the curriculum, allowing students to work in teams to solve real-world data challenges, fostering teamwork and communication skills.

- Internship Opportunities: The program encourages students to pursue internships with leading tech companies and organizations, providing invaluable industry experience and networking opportunities.

- Field Trips: Students often participate in field trips to tech companies and data-driven organizations, gaining insights into industry practices and trends.

- Research Institutes: NYU is home to several research institutes, such as the Center for Data Science, where students can engage in cutting-edge research and collaborate with faculty on innovative projects.

- Digital Tools: Students gain proficiency in industry-standard software and programming languages, including Python, R, SQL, and various data visualization tools, preparing them for diverse roles in data science.

- Libraries and Resources: The NYU library system offers extensive resources, including access to databases, journals, and research materials that support students' academic and project work.

These experiential learning opportunities not only enhance students' technical skills but also prepare them for successful careers in data science.

Embarking on this journey at NYU means you’ll be equipped with the practical skills and experiences that employers are looking for in today’s data-driven world.

Progression & Future Opportunities

Pursuing an MS in Data Science at New York University opens up a world of opportunities, with graduates finding success in various high-demand roles. Typical job positions include Data Scientist, Machine Learning Engineer, Data Analyst, and Business Intelligence Analyst. The program equips students with the skills and knowledge needed to thrive in these roles:

- University Services: NYU offers robust career services, including personalized career coaching, resume workshops, and networking events with industry leaders. The Wasserman Center for Career Development is particularly valuable, providing access to job postings and internship opportunities tailored to data science students.

- Employment Stats and Salary Figures: Graduates from the MS in Data Science program have an impressive employment rate, with over 90% securing jobs within six months of graduation. The average starting salary for graduates is around $100,000, reflecting the high demand for skilled data professionals.

- University–Industry Partnerships: NYU has established partnerships with leading tech companies and organizations, such as Google, IBM, and Deloitte. These collaborations often lead to internship opportunities and real-world projects that enhance the learning experience.

- Long-term Accreditation Value: The program is accredited by the Middle States Commission on Higher Education, ensuring that the education you receive meets high standards and is recognized globally.

- Graduation Outcomes: Alumni of the program have gone on to work at prestigious companies and have reported significant career advancement, with many moving into leadership roles within just a few years of graduation.

Further Academic Progression: After completing the MS in Data Science, students have the option to further their studies by pursuing a PhD in Data Science or related fields, which can lead to academic positions or advanced research roles. Additionally, graduates may choose to specialize further by enrolling in certificate programs or professional development courses that focus on emerging technologies and methodologies in data science. This continuous learning path not only enhances expertise but also keeps graduates competitive in the ever-evolving job market.

Choosing NYU for your MS in Data Science is a step toward a fulfilling and prosperous career in one of the most exciting fields today.