M.S. in Data Science and Analytics

The M.S. in Data Science and Analytics at NEC is a 36-credit program that can be completed efficiently, often within 18 months, depending on the chosen pathway. It is designed to provide students with a robust framework for critically examining, interpreting, and visualizing data, and then applying this knowledge to solve complex 21st-century business challenges. The curriculum emphasizes turning raw information into valuable insights and solid strategies that drive organizational growth and stability. NEC offers both on-campus and online formats for this program, including an Executive MS in Data Science and Analytics with a hybrid delivery that combines online coursework with intensive weekend residencies.

STEM-Designated: A significant advantage for both domestic and international students, particularly F-1 visa holders, is that NEC's M.S. in Data Science and Analytics program is STEM-designated. This designation allows international graduates to apply for an Optional Practical Training (OPT) extension of up to 24 months beyond the initial 12 months, providing a total of up to 36 months of work authorization in the U.S. post-graduation. This significantly enhances career opportunities and professional development for international students.

Curriculum: The curriculum is comprehensive and highly practical, comprising a core set of courses and a selection of electives. Core courses typically cover foundational topics such as Introduction to Artificial Intelligence, Data Mining for Data Science and Analytics, Database Design, Information Visualization, Python Programming, and Machine Learning for Data Science and Analytics. Electives allow students to delve deeper into specialized areas like Cloud Computing Concepts, Object-Oriented Programming (Java), IT Project Management, Information Security, Text Analytics and Natural Language Processing, Big Data Tools and Architecture, Web Analytics, and Spreadsheets for Business Analysis. The program also offers an internship component, allowing students to gain valuable real-world experience. The Executive hybrid program has a slightly different core focusing on SQL, data science tools, data mining, visual analytics, Python, web analytics, spreadsheets for business analysis, text analytics, machine learning, and big data tools.

Research Focus: While primarily an applied program, the M.S. in Data Science and Analytics at NEC integrates a strong emphasis on research methodologies through its practical, project-based approach. Students learn to effectively evaluate data from acquisition to cleansing, warehousing, and final analysis. The curriculum encourages the application of methodologies and tools for collecting data, designing databases, and interpreting collected data using various statistical tools. The program aims to develop professionals who can execute real-time analytical methods on living datasets, demonstrating a focus on extracting meaningful insights and driving evidence-based decision-making. Students are expected to develop solutions that address real-world business problems and contribute to a portfolio of work samples demonstrating their skills.

Industry Engagement: The program is designed with a keen eye on industry demands, preparing graduates for high-demand roles in various sectors. The curriculum incorporates in-demand areas like machine learning, big data, natural language processing, and web analytics, ensuring students gain proficiency in tools and techniques widely used by employers. The inclusion of an internship component allows students to overlay academic study with practical field experience, applying concepts to solve real-world business problems and gaining insight into professional organizational cultures. This strong practical orientation, combined with training in industry-standard tools like Python and SQL, directly addresses the needs of the job market for data-driven professionals.

Global Perspective: New England College fosters a globally diverse learning environment, attracting international students and enriching the academic experience for all. The university provides support services for international applicants, including guidance on English language proficiency requirements and visa processes. The skills acquired in the Data Science and Analytics program—such as interpreting global data trends, understanding the ethical implications of data use across different cultures, and leveraging data for international business strategy—are inherently global. The STEM designation further supports international students in gaining practical work experience in the U.S., enhancing their global career prospects and allowing them to contribute to diverse economies.

Postgraduate Entry Requirements

Academic Qualifications: Applicants for postgraduate programs typically require a minimum academic achievement of 70% or above in their bachelor's degree.

English Language Proficiency:

• IELTS: Overall band score of  6.5 or 7.0 with a minimum of 6.0 in each component.
• TOEFL: Overall score of 61 or higher.
• PTE: Overall score of 43 or higher.
• DET (Duolingo English Test): Minimum score of 105.

New England College (NEC) is supporting talented international students by offering a range of merit-based scholarships designed to make quality education more accessible and affordable. These scholarships recognize academic excellence, leadership, and other achievements, helping to reduce the financial burden of studying in the United States.

Merit-Based Scholarships: All international applicants to NEC are automatically considered for merit scholarships during the admission process. Awards vary based on the student’s academic record, test scores, extracurricular involvement, and leadership qualities. 

Presidential Scholarship: One of the most prestigious awards, the Presidential Scholarship, is granted to exceptional international students with outstanding academic credentials. 

Athletic Scholarships: NEC offers athletic scholarships to international students who demonstrate exceptional skill and commitment in NCAA Division III sports. These scholarships are awarded based on athletic performance, team needs, and coach recommendations, providing support while balancing academics and athletics.

Specialized Program Scholarships: Certain academic departments at NEC may offer scholarships targeting high-achieving students pursuing degrees in fields such as Business, Cybersecurity, Education, Creative Writing, and Health Sciences. These targeted awards help attract top talent and support focused areas of study.

New England College's Master of Science (M.S.) in Data Science and Analytics program positions graduates for highly sought-after careers in a data-driven world. The program's blend of statistical analysis, programming, machine learning, and data visualization skills prepares students to extract meaningful insights from complex datasets and translate them into strategic business advantages across diverse industries.

Data Scientist: This is often the primary target role for graduates. Data Scientists are responsible for collecting, cleaning, analyzing, and interpreting large, complex datasets. They use advanced statistical methods, machine learning algorithms, and programming languages (like Python and R) to uncover hidden patterns, build predictive models, and provide actionable insights that drive business strategy.

Machine Learning Engineer: With a strong emphasis on machine learning in the NEC curriculum, graduates are well-prepared for this role. Machine Learning Engineers design, build, and deploy AI and machine learning models into production systems. They focus on the engineering aspects of machine learning, ensuring models are scalable, efficient, and integrated into existing applications.

Business Intelligence (BI) Developer/Analyst: BI professionals transform raw data into accessible and understandable reports, dashboards, and visualizations. They help organizations make data-driven decisions by creating tools that allow business users to easily monitor performance, identify trends, and gain insights into operations and customer behavior.

Data Analyst: Data Analysts focus on collecting, processing, and performing statistical analysis on data. They are crucial for interpreting data results and presenting them in a clear, concise manner to various stakeholders, often using tools for data visualization and reporting to highlight key trends and support decision-making.

Data Engineer: These professionals are the architects of an organization's data infrastructure. Data Engineers design, build, and maintain robust data pipelines that extract, transform, and load (ETL) data from various sources, ensuring that data is readily available, clean, and optimized for data scientists and analysts to use.

Quantitative Analyst (Quant): Particularly in finance and related fields, Quantitative Analysts use advanced mathematical and statistical models, often incorporating machine learning, to analyze financial markets, price securities, develop trading strategies, and manage risk. This role demands strong analytical skills and programming proficiency.

Marketing Analyst/Marketing Data Scientist: These specialists apply data science techniques to marketing challenges. They analyze customer behavior, campaign performance, market trends, and product effectiveness to optimize marketing strategies, personalize customer experiences, and forecast future sales.

Operations Research Analyst: Drawing on mathematical modeling, statistical analysis, and optimization techniques, Operations Research Analysts help organizations make better decisions and improve efficiency. They use data to solve complex problems related to logistics, scheduling, resource allocation, and workflow optimization.

Database Administrator (DBA): While more focused on the underlying infrastructure, a strong understanding of database design and management gained in the program is valuable. DBAs manage and maintain an organization's databases, ensuring data integrity, security, and performance.

Product Analyst (with Data Science focus): In tech companies, Product Analysts leverage data to understand user behavior, product performance, and market opportunities. They work closely with product managers and engineers to inform product development, identify areas for improvement, and measure the success of new features.