Master’s in Applied Statistics or Data Science

1 Year On Campus Masters Program

Cornell University

Program Overview

Cornell University's Master of Professional Studies in Applied Statistics and Data Science is a professionally focused graduate programme designed to develop advanced expertise in statistical analysis, machine learning, programming, and computational data science. Students can choose between two specialized options, Applied Statistics and Data Science, combining theoretical statistical foundations with practical training in Python, databases, big data management, and real-world analytical projects to prepare for professional careers in technology, finance, business, research, and government.

Curriculum Structure

The programme generally takes one year to complete and offers two academic options. The following curriculum progression is based on Cornell's published required courses and electives.

Year 1: Statistical Foundations and Core Data Science Skills

During the first semester, students develop a strong foundation in statistical modelling, probability, and professional project development. Core modules include Linear Models with Matrices (STSCI 5030), Probability Models and Inference (STSCI 5080), and Project Development and Professional Communication (STSCI 5954). Students choosing the Data Science option also study Python Programming and its Applications in Statistics (STSCI 5045) and Database Management and SAS High Performance Computing with DBMS (STSCI 5060), developing programming and data management skills.

Year 1: Advanced Data Analysis and Practical Application

During the second semester, students strengthen their applied analytical and project management capabilities through Realtime Project Management (STSCI 5955) and the Applied Statistics MPS Data Analysis Project (STSCI 5999). Data Science students additionally complete Big Data Management and Analysis (STSCI 5065), gaining experience in handling and analysing large datasets. Students can also select electives in machine learning, Bayesian statistics, regression modelling, time-series analysis, neural networks, and financial engineering, allowing them to tailor their studies to specific career interests. The two-semester project provides an opportunity to apply statistical and computational techniques to a substantial real-world data analysis problem, often in collaboration with a private company.

Focus Areas

Applied Statistics and Data Science
Statistical Modelling and Inference
Probability and Mathematical Statistics
Linear Models and Regression Analysis
Python Programming and Statistical Computing
Database Management Systems
Big Data Management and Analytics
Machine Learning and Predictive Analytics
Bayesian Statistics
Neural Networks and Deep Learning
Time-Series Analysis and Forecasting
Financial Engineering and Risk Modelling
Data Mining and Computational Statistics
Statistical Consulting and Professional Communication
Data Science Project Management

Learning Outcomes

Graduates of this programme will be able to:

Apply statistical and mathematical methods to solve complex analytical problems.
Develop statistical models for prediction, estimation, and data-driven decision-making.
Use Python, R, and SAS for programming, statistical computing, and data analysis.
Design and manage databases for efficient data storage and retrieval.
Process, manage, and analyse large-scale datasets using advanced computational techniques.
Apply machine learning and predictive analytics to real-world problems.
Interpret statistical findings and communicate analytical results effectively.
Apply probability models and statistical inference to evaluate uncertainty.
Develop solutions for business, financial, scientific, and industrial data challenges.
Manage data science projects and collaborate with professional teams.
Conduct independent statistical analysis projects using practical datasets.
Apply ethical principles and responsible practices in statistical analysis and data science.

Professional Alignment (Accreditation)

The official university programme information does not specify a separate professional accreditation for the MPS in Applied Statistics and Data Science. However, the programme provides professional preparation through its applied statistical coursework, programming training, database management, and industry-oriented data analysis project. Students also develop professional communication and project management skills, while the programme includes SAS-related computing and certification opportunities.

Reputation (Employability Rankings)

Industry-Oriented Education: The programme is specifically designed to prepare graduates for professional careers in business, industry, and government.
Industry Collaboration: Students complete a two-semester data analysis project that often involves collaboration with private companies.
Technical Skill Development: The curriculum includes practical training in Python, R, SAS, databases, big data management, and machine learning.
Career Opportunities: Graduates can pursue careers in data science, statistical analysis, quantitative research, financial analytics, business intelligence, and predictive modelling.
Employment Statistics: Cornell does not publish a specific programme-level graduate employment rate, median salary, or QS/Guardian employability ranking for this degree.

Experiential Learning (Research, Projects, Internships etc.)

At Cornell University, the Master’s in Applied Statistics or Data Science program is designed to provide students with hands-on experience that is essential for success in the data-driven world. Students engage in practical learning through a variety of projects, internships, and collaborative opportunities that enhance their analytical skills and prepare them for real-world challenges. The program leverages state-of-the-art facilities and tools, ensuring that students are well-equipped to tackle complex data problems.

Here are some key aspects of experiential learning within the program:

- Collaborative Projects: Students work on group projects that simulate real-world data science challenges, fostering teamwork and communication skills.
- Internship Opportunities: The program encourages internships with industry partners, allowing students to apply their knowledge in professional settings and gain valuable work experience.
- Access to Cutting-Edge Software: Students utilize industry-standard software such as R, Python, and SQL, as well as tools like Tableau for data visualization and machine learning frameworks.
- Research Laboratories: The program provides access to dedicated research labs where students can engage in innovative projects and collaborate with faculty on cutting-edge research.
- Field Trips and Workshops: Students participate in field trips to tech companies and workshops led by industry experts, providing insights into current trends and practices in data science.
- Libraries and Resources: Cornell’s extensive library system offers a wealth of resources, including access to academic journals, databases, and research materials relevant to data science and statistics.
- Institutes and Centers: Students can connect with various institutes, such as the Cornell Institute for Social and Economic Research (CISER), which provides additional research opportunities and resources.

These experiential learning opportunities not only enhance your academic experience but also significantly boost your employability in the competitive field of data science.

Embarking on this journey at Cornell will empower you with the skills and experiences needed to excel in your career. I encourage you to apply and take the next step toward your future in data science!

Progression & Future Opportunities

Pursuing a Master’s in Applied Statistics or Data Science at Cornell University opens the door to a wealth of career opportunities. Graduates from this program often find themselves in high-demand roles such as Data Scientist, Statistician, Data Analyst, and Machine Learning Engineer. The skills and knowledge gained here not only prepare you for immediate employment but also set a strong foundation for long-term career growth:

- University Services: Cornell offers robust career services, including personalized career coaching, resume workshops, and networking events with industry leaders. The Cornell Career Services team is dedicated to helping you connect with potential employers and refine your job search strategies.

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

- University–Industry Partnerships: Cornell has established strong partnerships with leading companies in technology, finance, and healthcare, such as Google, IBM, and Johnson & Johnson. These collaborations often lead to internship opportunities and real-world projects that enhance your learning experience.

- Long-term Accreditation Value: The program is accredited and recognized nationally, ensuring that your degree holds significant value in the job market. This recognition can enhance your employability and career advancement prospects.

- Graduation Outcomes: Alumni from this program have gone on to work at prestigious organizations and have made significant contributions to their fields, often taking on leadership roles within just a few years of graduation.

Further Academic Progression: After completing your Master’s in Applied Statistics or Data Science, you may choose to further your studies by pursuing a Ph.D. in Data Science, Statistics, or a related field. This advanced degree can open doors to academic positions, research opportunities, and specialized roles in data science that require a deeper level of expertise. Additionally, you could explore certifications in emerging technologies or specialized areas within data science to enhance your skill set and career trajectory.

Choosing Cornell for your graduate studies means investing in a future filled with possibilities. The combination of a strong curriculum, supportive services, and a vibrant alumni network will empower you to achieve your career goals and make a meaningful impact in the world of data.