Data Science, M.S.

Program Overview

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Marshall University's Master of Science in Data Science equips students with the technical expertise to analyze large datasets, design data-driven solutions, and make informed decisions. The program offers a flexible curriculum with three Domain Emphasis options, preparing graduates for high-demand careers in data science, including Data Analyst, Data Scientist, and Data Engineer. With an emphasis on hands-on learning and real-world projects, the program ensures graduates are highly sought-after in the rapidly growing data science field.

Program Outline

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Degree Overview:

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The Master of Science in Data Science (MSDS)

at Marshall University equips students with the technical expertise to extract meaningful insights from diverse data sources. The program aims to prepare graduates for successful careers in this rapidly growing field.

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Objectives:

• Provide students with the technical expertise to analyze large datasets.
• Enable students to design and implement data-driven solutions.
• Foster critical thinking skills for data interpretation and decision-making.

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Description:

The MSDS curriculum focuses on computational modeling, data collection and integration, data storage and retrieval, data processing, modeling and analytics, and data visualization.

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Outline:

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Program Content:

• Advanced Programming
• Machine Learning
• Big Data Systems
• Visual Analytics
• Statistical Data Mining
• Statistical Methods for Researchers

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Structure:

• 30 credit hours (10 courses)
• 18 credit hours of required core courses
• 12 credit hours of Domain Emphasis (Computing, Information Systems, or Predictive Analytics)

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Course Schedule:

• Courses offered every semester (core courses)
• Some elective courses offered once a year
• Full-time study plan (9 credit hours per semester)

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Individual Modules:

• Computing Domain:
• High-performance computing
• Cloud computing
• IoT
• Artificial Intelligence
• Cybersecurity
• Bioinformatics
• Information Systems Domain:
• Health Care Data Analytics
• Management Information Systems
• Systems Quality Assurance
• Information Structures I
• Emerging Tech in Info Systems
• Database Management
• Data Warehousing
• Health Care Enterprise Info Syst
• Predictive Analytics Domain:
• Regression Analysis
• Experimental Designs
• Nonparametric Statistics
• Statistical Computing
• Applied Survival Analysis
• Applied Multivariate Statistical Methods
• Time Series Forecasting
• Bayesian Statistics
• Advanced Statistical Learning

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Assessment:

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Assessment Methods:

• Exams
• Assignments
• Projects
• Thesis (optional)

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Assessment Criteria:

• Understanding of concepts
• Problem-solving skills
• Data analysis and interpretation skills
• Communication skills

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Teaching:

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Teaching Methods:

• Lectures
• Tutorials
• Hands-on laboratory sessions
• Independent study

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Faculty:

• Experienced and qualified faculty with expertise in data science
• Active research involvement

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Unique Approaches:

• Emphasis on hands-on learning
• Opportunity to work on real-world projects
• Thesis option for in-depth investigation

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Careers:

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Career Paths:

• Data Analyst
• Data Scientist
• Data Engineer
• Data Architect
• Business Intelligence Developer
• Information Systems Manager
• Machine Learning Engineer

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Opportunities:

• Government
• Health industries
• IT
• Banking
• Engineering
• Education
• Various other sectors

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Outcomes:

• Graduates are highly sought-after in the job market
• Average salary of data scientists in the US is $130,000 (2016)
• Data Scientist has been named the best job in America for three years running (Glassdoor)

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Other:

• The program prepares students for the rapidly growing data science field.
• Data science is a concept that unifies statistics, data analysis, data mining, and machine learning.
• Marshall University's MSDS program is designed to equip graduates with the essential skills: programming, quantitative skills, technologies, domain knowledge, and critical thinking.
• Students in the program have the opportunity to tailor their studies to specific areas of interest through the Domain Emphasis options.

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Note:

This is an extensive response based on the information found in the context. Some sections may be incomplete due to limited information available in the text.