Your master’s degree in Data Analytics puts you at the forefront of industry trends and gives you the authority to inform crucial business decisions.
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No matter your area -- from business development or marketing to IT, healthcare, or human resources-- earning your M.S. in Data Analytics prepares you to become the best at what you do.
This program has prerequisites of a statistics, programming, and database background and is designed to thoroughly prepare graduates for careers in data science, business intelligence, and data analysis. You will learn to use such cutting-edge tools as SAS, Tableau, RapidMiner, SQL, and ETL -- used to collect and dissect research, develop competitive business strategies and analyze their results, present your conclusions visually and in writing, and inform expert opinions to direct company initiatives.
Some job titles you might obtain with a Master of Science in Data Analytics include…
Learn to be an organizational leader by mastering the many facets of business intelligence.
Among the technical topics you’ll cover in the Master of Science in Data Analytics degree program are…
Each course in the program presents a rigorous curriculum that not only teaches students leading industry topics but also requires them to demonstrate their mastery of each concept.
The Master of Science in Data Analytics program prepares students to be organizational leaders through the use of business intelligence and data analytics. Students learn to improve decision- making and business processes in core business functions such as accounting, finance, logistics, management, and strategy through the application of business intelligence solutions and data analytics principles. Technical topics include data warehousing, data mining and visualization, business analytics, predictive analytics, and enterprise performance management. The program prepares students for careers such as business/systems analysts, business intelligence developers/analysts, ETL developers, data analysts, data architects, and data scientists.
Program Specific Admission Requirements
In addition to the institutional graduate admission standards, students seeking admission to the MSDA program must have an undergraduate degree with a major or concentration in management information systems, information technology, computer science, or database management and demonstrate that they have taken at least introductory courses in the following three areas: computer programming, database management, and statistics.
Students who do not meet the above conditions may be admitted provisionally to the program. One or more of the courses below may be required to be completed as prerequisites to the MSDA coursework to build up background knowledge in these areas:
To gain full admission to the MSDA program and begin graduate level coursework, provisionally admitted students must complete assigned prerequisite courses within 12 months of starting with a minimum cumulative grade point average of 3.0. All coursework must also be completed with a grade of ‘C’ or higher.”
All courses are taught by qualified instructors with current industry experience. Our instructors hold advanced degrees in their fields, and are specially trained to teach online, so you and your fellow students can think more critically about how the subject matter applies to real-world situations.
EXPLORE CAREER OPTIONS IN DATA ANALYTICS
See the requirements for entry, junior, senior, and executive positions that CSU-Global can help you attain.
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Employment for computer and information research scientists is expected to grow 19% within the next 8 years, significantly faster than the all-occupation average. More businesses are discovering that advanced data-mining plays a large role in maintaining marketplace relevance, increasing their demand for experts who collect data and translate it into information that will give them a competitive edge. Qualified professionals with advanced degrees and a working knowledge of industry tools will find more opportunities open to them.
Although growth can vary by industry, job candidates with a demonstrable mastery of the field are expected to have excellent job prospects, according to the Bureau of Labor Statistics. Earning your degree and having knowledge of a specialized field, such as cyber security or finance, may give you a competitive edge in the job market.
You’d be an ideal computer and information research scientist if you enjoy working directly with…
Successful computer and information research scientists are discerning, analytical, and technical people with a creative approach to strategizing and problem-solving.
If this sounds like you, fill out the form to your right and get in touch today.
This course explores four major components of research for graduate program: business research and descriptive statistics, fundamentals of accounting analysis, fundamentals of financial analysis, and decision making through statistical and logical methods. The course includes quantitative methods to support research methodologies. The course is not intended to provide an in-depth study of these components but provides basic knowledge to support intensive learning that will occur in the core courses of their programs.
This course introduces students to the methodologies, techniques, and tools most commonly used in data analytics. These foundational methods are from the fields of statistics, operations research, and information systems. Topics include techniques and tools for visualization, inference, forecasting, optimization, simulation, and data mining. The emphasis is on exposing the student to the techniques and software tools that are used in the industry and will be used in the rest of the program.
This course provides students with an overview of Business Intelligence (BI) for an enterprise, establishing the foundation for using data in cross-functional key areas, such as accounting, sales, production, customer data, and other elements, to assist with generating actionable intelligence data for decision making in order to achieve or maintain competitive advantage in the workplace and evaluate how well corporate key performance indicators are being met.
This course provides students with an overview of data warehouses in an enterprise and how data is captured, analyzed, and translated into information to assist executives with streamlined information from disparate systems for effective decision making based on data for strategic and operational objectives in an enterprise.
This course will provide the basic framework for conducting various data and text mining methodologies, including logistic regression analyses, classical discriminant analyses, association rule, decision tree, support vector machine, neural networks, variable reduction, cluster analysis, text analytics, and web mining. In addition, this course teaches the essential and practical skills in visualization, including computer graphics, visual data representation, physical and human vision models, numerical representation of knowledge and concepts, pattern analysis, and computational methods. Recommended Prior Course: MIS500
This course covers the fundamental predictive analytics and data mining approaches applied in business. It introduces basic concepts and techniques to discover patterns in data, identify variables with the most predictive power, and develop predictive models.
This course provides an overview of the tools and techniques for analyzing business data in order to develop comprehensive and functional solutions. Topics include forecasting, simulation, and data modeling for complex problem analysis in medium to large organizations.
This course provides an in-depth understanding of how business intelligence aligns with the realization of organizational strategy. Topics include key performance indicators, organizational goals, and the role of effective management in meeting enterprise objectives.
This capstone course provides students with the opportunity to demonstrate competency on the key domains of business intelligence and data analytics. Students will learn to integrate concepts learned throughout the entire program and develop a comprehensive project in a specific domain of analytics, such as web analytics, social media analytics, big data analytics, or healthcare analytics.
*Master's Plus - Some M.S. in Data Analytics students may be required to take RES500 as part of their program if they have an undergraduate GPA below 3.00. In this case, the degree is 39 credits.
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Earning your master’s degree shows you have excellent critical thinking and decision making skills. Earning it at CSU-Global means you're already pretty good at it. Our promise to you is an affordable education with low tuition rates guaranteed not to increase for as long as you are enrolled, with no student fees or out-of-state tuition up charges.
We know you’re busy, but school can fit into your professional, family, and other life obligations. Our year-round, 100% online classes give you the flexibility to learn and study when it’s most convenient for you.
"The pride I have in achieving a Masters Degree from CSU-Global provided me with the confidence to attain the career plan I had envisioned."
- Scott Ryder, Student
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* Bureau of Labor Statistics