M.S. in Kinesiology - Sport Administration concentration
The M.S. in Kinesiology with a Sport Administration concentration at Montclair State Un...
M.S. in Data Science
Montclair, New Jersey
INTAKE: Jan & Sept
Program Overview
The Master of Science (M.S.) in Data Science at Montclair State University is a rapidly growing and highly relevant program designed to educate students in the methods and algorithms used to analyze, clean, visualize, and interpret large datasets. This interdisciplinary program, typically 30 credits, blends theory and practice to prepare students for top-tier professional careers in the fast-evolving fields of data science and big data. It focuses on equipping students with a strong foundation in statistical approaches, modern machine learning, and industry-popular programming languages. The program offers a flexible structure, accommodating both full-time and part-time enrollment.
STEM Designated: The M.S. in Data Science program at Montclair State University is officially a STEM-designated degree program. This classification holds significant advantages, particularly for international students, as it allows for an extended Optional Practical Training (OPT) period in the United States after graduation. This provides up to three years of work authorization, greatly enhancing opportunities for practical work experience and career development in the in-demand data science sector.
Curriculum (M.S. in Data Science): The curriculum of the M.S. in Data Science is comprehensive, providing students with a blend of theoretical knowledge and hands-on skills essential for the field. Key topics of study include data analytics, machine learning, data visualization, and statistical programming. Core coursework typically covers areas such as Exploratory Data Analysis and Visualization, Data Mining, Computer Algorithms and Analysis, Python for Data Science, and Big Data Analytics. The program also offers flexibility for students to choose a domain area relating to Data Science. Students without a strong undergraduate background in computer science or related quantitative fields may be required to complete prerequisite courses to ensure a solid foundation. The program culminates in a research project, providing valuable practical application experience.
Research Focus: The M.S. in Data Science program at Montclair State University encourages students to engage in cutting-edge research, often collaborating with faculty on various data-intensive projects. The primary mission of the degree includes providing unique opportunities for students to conduct research in data science investigations. Faculty interests span a wide array of topics within data science, including various applications of data analytics, machine learning, and big data. This research-oriented environment ensures that the curriculum remains current with the latest advancements and that students gain experience in contributing to the field's evolving body of knowledge.
Industry Engagement: Montclair State University places a strong emphasis on industry engagement, leveraging its strategic location near major business hubs in New Jersey and New York City. The M.S. in Data Science program aims to prepare students for the next generation data science workforce, aligning its curriculum with current industry demands. This includes focusing on skills sought by employers in diverse sectors like science, healthcare, education, social media, and government. The university highlights excellent placement programs after course completion, and students can benefit from opportunities for internships and connections with professionals in the data science field, as well as a strong alumni network that extends post-graduation support.
Global Perspective: The M.S. in Data Science program inherently incorporates a global perspective by addressing the universal need to understand and gain insights from the massive amounts of data generated worldwide. The principles and methodologies of data science are applicable across international contexts and industries, preparing graduates to work with diverse datasets and global challenges. Montclair State University's commitment to a diverse student body and inclusive learning environment further enhances students' global competencies, making them well-equipped to collaborate in international teams and contribute to data-driven solutions on a global scale.
Location
Montclair, New Jersey
Score
IELTS 6.5
Tuition Fee
USD 19916
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 83 or higher.
- PTE: Overall score of 53 or higher.
- DET (Duolingo English Test): Minimum score of 105.
Montclair State University offers a range of scholarship opportunities specifically designed to support international students in achieving their academic goals while easing the financial burden of studying in the United States.
Presidential Scholarships: The university’s prestigious Presidential Scholarships are awarded to top-performing international students demonstrating exceptional academic records and leadership skills. These scholarships provide substantial financial support and are highly competitive.
Graduate Scholarships and Assistantships: Graduate international students have access to departmental scholarships and graduate assistantships. Assistantships may include tuition waivers and stipends in exchange for research or teaching responsibilities, offering both financial aid and valuable professional experience.
The Master of Science (M.S.) in Data Science at Montclair State University is a highly relevant, 30-credit, STEM-designated graduate program designed to train students in the sophisticated methods and algorithms used to analyze, clean, visualize, and interpret large datasets. This interdisciplinary program, blending theory and practice, prepares graduates for top-tier professional careers in the rapidly evolving fields of data science and big data. Students acquire a strong foundation in statistical approaches, modern machine learning, and industry-standard programming languages.
Data Scientist: This is the most direct career path, involving the collection, analysis, interpretation, and visualization of large datasets to extract actionable insights. Data scientists build predictive models, design experiments, and help organizations make data-driven decisions across all industries.
Machine Learning Engineer: These professionals design, build, and deploy machine learning models and AI systems. They focus on creating algorithms that enable machines to learn from data, leading to applications in areas like recommendation systems, fraud detection, and autonomous vehicles.
Data Engineer: Data engineers are responsible for designing, constructing, installing, and maintaining robust data pipelines and infrastructures. They ensure that data is collected, stored, processed, and made accessible for analysis, forming the backbone for data science initiatives.
Business Intelligence (BI) Analyst: BI analysts use data to help businesses understand past and current performance. They create dashboards, reports, and visualizations to translate complex data into clear insights, supporting strategic decision-making and operational improvements.
Quantitative Analyst (Quant): Often found in the financial sector, quants use advanced mathematical and statistical models to analyze financial markets, assess risk, develop trading strategies, and price complex financial products.
Applied Statistician: Leveraging their strong statistical foundation, applied statisticians design studies, analyze data, and interpret results to solve problems in various fields, including pharmaceuticals, public health, and market research.
Big Data Architect: These high-level specialists design and oversee the entire big data ecosystem within an organization. They determine the optimal strategies for handling massive volumes of data, including storage, processing, and analytics infrastructure.
Deep Learning Engineer: A specialization within machine learning, deep learning engineers work with neural networks to solve highly complex problems in areas like image recognition, natural language processing, and advanced AI.
Research Scientist (Data Science): Graduates can pursue research roles in academia, corporate R&D labs, or government institutions, focusing on developing new data science methodologies, algorithms, and applications, and pushing the boundaries of the field.
Product Analyst/Manager (Data-focused): In tech companies, these roles involve using data to understand user behavior, evaluate product performance, and inform product development strategies. They bridge the gap between technical data insights and business outcomes.
