MS in Accounting
Northeastern University in Boston offers a robust MS in Accounting pro...
MS in Statistics — Connect (Statistical Machine Learning)
Boston
INTAKE: Jan & Sept
Program Overview
The MS in Statistics — Connect (Statistical Machine Learning) program at Northeastern University focuses on equipping students with a comprehensive understanding of both classical and modern statistical methods, alongside cutting-edge machine learning algorithms. Students gain hands-on experience in data analysis, model building, and computational techniques, with a focus on applying these skills to real-world problems in fields such as finance, healthcare, technology, and beyond. The program integrates theory with practice, providing students the opportunity to work with large datasets and develop predictive models.
STEM-Designated: The MS in Statistics — Connect (Statistical Machine Learning) program is STEM-designated, which reflects its strong focus on science, technology, engineering, and mathematics. This designation benefits international students by allowing them to apply for an extended Optional Practical Training (OPT) period, providing additional time to gain practical experience in the United States post-graduation. The STEM designation also highlights the program’s alignment with the growing demand for data science and machine learning expertise in today’s technology-driven job market.
Curriculum: The curriculum for the MS in Statistics — Connect (Statistical Machine Learning) program is designed to provide students with a solid foundation in statistical theory and machine learning. Core courses cover topics such as probability theory, statistical inference, regression analysis, and multivariate analysis, while specialized courses delve deeper into machine learning algorithms, such as supervised and unsupervised learning, deep learning, natural language processing, and reinforcement learning. Students also engage in advanced computational techniques, including big data analysis, statistical computing, and the use of software tools like R, Python, and Hadoop.
Research Focus: The MS in Statistics — Connect (Statistical Machine Learning) program places a strong emphasis on research in machine learning and its applications. Students are encouraged to engage in research projects that explore emerging topics such as predictive modeling, algorithm development, and the application of machine learning in diverse fields, including healthcare, finance, and artificial intelligence. The program fosters an environment where students can collaborate with faculty on cutting-edge research and contribute to advancements in the field of statistical machine learning. Students also have the opportunity to work on interdisciplinary projects that combine statistics, data science, and machine learning techniques.
Industry Engagement: Northeastern University is known for its strong connections with the tech and data industries, and the MS in Statistics — Connect (Statistical Machine Learning) program is no exception. The program offers extensive opportunities for industry engagement, including internships, co-op programs, and collaborations with industry partners. These opportunities allow students to gain real-world experience, apply machine learning techniques to solve practical problems, and build professional networks. The program’s location in Boston, a hub for technology and innovation, provides students with access to leading tech companies, research institutions, and startups, further enhancing their career prospects.
Global Perspective: The MS in Statistics — Connect (Statistical Machine Learning) program offers a global perspective by preparing students to apply machine learning and statistical methods to solve complex problems in diverse global contexts. The program encourages collaboration with international peers and faculty, providing students with the opportunity to explore the global implications of machine learning, data science, and statistical analysis. Through interdisciplinary research, students gain insight into how machine learning is transforming industries worldwide, from healthcare and finance to energy and technology.
Location
Boston
Score
IELTS 6.5
Tuition Fee
USD 28560
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 with a minimum of 6.0 in each component.
- TOEFL: Overall score of 79 or higher.
- PTE: Overall score of 53 or higher.
- DET (Duolingo English Test): Minimum score of 105.
Northeastern University in Boston offers a range of scholarship opportunities for international students to help alleviate the financial burden of studying in the United States. These scholarships are designed to support academically talented students from around the world, promoting diversity and fostering an inclusive campus environment.
Merit-Based Scholarships: Northeastern University provides merit-based scholarships to international students based on academic excellence and achievements. These scholarships are awarded during the admissions process and are highly competitive. The amount and criteria for these scholarships may vary, but they aim to recognize students who demonstrate exceptional academic performance, leadership potential, and a strong their chosen field of study.
Northeastern International Scholars Program: This prestigious program offers scholarships to outstanding international students who exhibit leadership, academic excellence, and a desire to contribute to the global community. The Northeastern International Scholars Program provides both financial support and opportunities for personal growth through networking events, leadership training, and involvement in community service initiatives.
Need-Based Financial Aid: Although Northeastern University does not offer need-based financial aid to international students, there are still many merit-based scholarships available that can significantly reduce tuition costs. However, international students are encouraged to seek external scholarships or funding from their home country to help cover the remaining costs.
External Scholarships and Funding: In addition to institutional scholarships, international students at Northeastern University can explore a variety of external scholarship opportunities from governments, foundations, and organizations in their home countries. Many international students receive funding from these sources to support their education at Northeastern.
Graduates of the MS in Statistics — Connect (Statistical Machine Learning) program at Northeastern University are equipped with advanced skills in statistical analysis, machine learning algorithms, and data science, making them highly sought after by a wide array of industries. The program provides the foundational knowledge and practical expertise necessary for students to pursue career opportunities in diverse sectors such as technology, finance, healthcare, and research.
Data Scientist: Data scientists leverage statistical methods and machine learning techniques to analyze large datasets and extract meaningful insights. They work in a variety of industries, from technology and healthcare to retail and government, helping organizations make data-driven decisions, develop predictive models, and improve operational efficiency.
Machine Learning Engineer: Machine learning engineers design and implement machine learning algorithms and systems that process and analyze data. These professionals apply statistical and computational techniques to create models that can be deployed for various applications, such as recommendation systems, natural language processing, and autonomous vehicles.
Statistical Analyst: Statistical analysts focus on applying statistical methods to analyze data and generate actionable insights. They play a key role in business intelligence, market research, and risk analysis, helping organizations make informed decisions based on empirical data. These professionals are commonly employed in finance, healthcare, and marketing sectors.
Quantitative Analyst (Quant): Quantitative analysts, or "quants," apply statistical models and machine learning algorithms to financial data to predict market trends and inform investment strategies. These professionals are highly valued in the finance industry for their ability to model complex financial systems, manage risks, and optimize portfolios.
Business Intelligence (BI) Analyst: BI analysts use statistical and machine learning tools to interpret business data and support decision-making processes. They analyze trends, patterns, and data from multiple sources to help organizations optimize their strategies and improve performance across different departments.
Data Engineer: Data engineers are responsible for designing, building, and maintaining the data infrastructure that supports data science and machine learning projects. They work with large datasets, databases, and data pipelines, ensuring that data is properly stored, processed, and ready for analysis.
Health Data Scientist: Health data scientists apply machine learning techniques to healthcare data, such as patient records, clinical trials, and epidemiological studies. They work in hospitals, pharmaceutical companies, and public health organizations to improve patient outcomes, optimize treatment plans, and support healthcare decision-making.
Artificial Intelligence (AI) Researcher: AI researchers use statistical machine learning and advanced algorithms to develop new AI models and systems. They contribute to research and development in areas such as natural language processing, robotics, computer vision, and cognitive computing. AI researchers typically work in academia, research labs, or tech companies.
Operations Research Analyst: Operations research analysts apply statistical techniques and optimization methods to improve business operations. They use machine learning models to solve problems in logistics, supply chain management, and resource allocation, helping companies improve efficiency and reduce costs.
Data Analyst in Marketing/Advertising: Data analysts in marketing and advertising use machine learning algorithms and statistical methods to analyze customer behavior, identify trends, and optimize marketing strategies. They help companies target the right audiences, predict customer preferences, and improve return on investment (ROI) for marketing campaigns.
Risk Analyst: Risk analysts use statistical models to assess potential risks and uncertainties within an organization. They work in sectors like finance, insurance, and government, applying machine learning to predict financial risks, market fluctuations, and other potential threats, aiding in the development of risk management strategies.
Research Scientist (Statistical Machine Learning): Research scientists in this field focus on advancing the theoretical foundations and practical applications of statistical machine learning. They work in academic institutions, research labs, and technology companies, contributing to innovations in AI, machine learning, and data science.
