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Online Master of Science in Data Analytics

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Take Charge as an Organizational Leader with our Data Analytics Master's Program

From business development to marketing, IT, healthcare, or human resources -- as an authority in data analytics, you will inform crucial business decisions. Depending on your area of interest, responsibilities might include the following:

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Use cutting-edge tools like SAS, Tableau, RapidMiner, SQL, and ETL.

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Develop competitive business strategies and analyze their results.

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Present conclusions visually and in writing to inform expert opinions.

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Direct company initiatives based on analytics.

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Introduction to Data Analytics

In this CSU Global mini-course, you will get an introduction to our Data Analytics program and explore introductory data analytics ideas and career paths. You’ll also explore the learning platforms that CSU Global uses to help students succeed in taking 100% online courses.

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CSU Global and SAS have teamed up to create three Joint Certificates. Expand your opportunities with SAS skills demonstrating to employers that your knowledge of analytics goes beyond theory and into application.



What is a Master’s in Data Analytics and what will I learn?

Students prepare to be organizational leaders through the use of business intelligence and data analytics. As a Master of Science in Data Analytics (MSDA) student at CSU Global, you build confidence for improved decision-making skills related to processes in a variety of core business functions -- accounting, finance, logistics, management, and strategy.

Master the application of business intelligence solutions and data analytic principles to become an asset in a range of industries. Technical topics prepare you for a number of data-based professions and include…

  • Data warehousing
  • Data mining and visualization
  • Business analytics
  • Predictive analytics
  • Enterprise performance management

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Courses in this Degree

Core Degree Courses (24 Credits)

  • RES500* Fundamentals of Quantitative Analysis
    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. Course not eligible for Prior Learning Assessment (PLA) credit.
  • MIS500 Foundations of Data Analytics
    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.
  • MIS540 Introduction to Business Intelligence
    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.
  • MIS510 Data Mining and Visualization
    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
  • MIS530 Predictive Analytics
    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.
  • MIS541 Data Warehousing in Enterprise Environments
    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.
  • MIS542 Business Analytics
    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.
  • MIS543 Enterprise Performance Management
    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.
  • MIS581 Capstone - Business Intelligence and Data Analytics
    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. Prerequisite: All courses in the major. Course not eligible for Prior Learning Assessment (PLA) credit.
  • MIS595 Data Analytics Practicum
    This course is designed for students as an opportunity to demonstrate program outcomes learned in the Masters of Science in Data Analytics under the supervision of both faculty and organizational personnel. Assignments are designed to combine theory and professional practice and include weekly journals, a mid-term conference with the instructor, faculty practicum (coordinator, and on-site supervisor), as well as a final report reviewing the practicum experience. This course may not be available in all states, see the State Specific Authorization Policy under Admissions Policies. Prerequisite 3 Core Courses. Course not eligible for Prior Learning Assessment (PLA) credit.

Prerequisites If you do not meet those conditions, you may qualify for provisional admittance. If so, you may be required to complete one or more of the courses below as prerequisites to the MSDA program coursework.

  • MIS470 Data Science Foundation
    This course provides an overview of the tools and techniques for analyzing data using statistics, R Programming, and SQL. Topics include data storage, linear regression, classification, linear models, tree-based learning, R programming, and SQL basic commands. (This course is also offered through SSA. Credits earned using this option will appear on transcripts with an “S” suffix.)
  • MIS407 Database Concepts
    The course covers the basics of relational databases, including basic terminology and concepts, database integrity, and normalization. The relational model will be examined in detail in order to appreciate database structure, integrity, and manipulation. Current relational database management systems will be explored and contrasted, as will basic relational database design and SQL programming. (This course is also offered through SSA. Credits earned using this option will appear on transcripts with an “S” suffix.)

*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.

**MIS595 is an optional course that provides students with practical data analytics experience. This course may not be available in all states; see the State Specific Authorization Policy under Admissions Policies.

Data Analytics Master's Degree Specialization (12 Credits)

When you earn your master's degree through CSU Global, you will customize it with a graduate specialization in a subject that matches your professional goals or personal interests. 

Most Popular Specializations:

Additional Available Specializations for this Program:

Download the Specializations Chart
View a list of all CSU Global specializations and the degree programs they can be paired with.


In addition to meeting CSU Global graduate admission standards, MSDA students must have an undergraduate degree with a major or concentration in one of the following:

  • Information systems
  • Information technology
  • Computer Science
  • Database management

Additionally, you will need to demonstrate that you have taken at least introductory courses in these three areas:

  • Computer programming
  • Database management
  • Statistics


If you do not meet those conditions, you may qualify for provisional admittance. If so, you may be required to complete one or more of the courses below as prerequisites to the MSDA program coursework.

  • MIS470 – Data Science Foundation
  • MIS407 – Database Concepts

To gain full admission, provisionally admitted students must complete their assigned prerequisite courses within 12 months of starting and achieve a minimum cumulative grade point average of 3.0. All coursework must also be completed with a grade of “C” or higher.

How much will my degree cost?

Tuition Rates

Your education should increase your earning potential, not your monthly bills.

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Employer Discount

Your employer may be one of 3,000+ we partner with to offer a 10% discount on tuition.

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Financial Aid

You may be eligible to receive financial aid to help cover the cost of your education.

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Best Masters Programs ranked CSU Global #3 for Best Online Degree in Data Analytics.


Regional accreditation by the Higher Learning Commission (HLC) means your degree comes from a top quality, trusted university.

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Prove your value to employers and stay competitive in the data analytics field as a SAS-certified professional.

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#10 Best Online Master's in Data Science

Best Colleges ranked CSU Globals as #10 Best Online Master's in Data Science Programs

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In Best Colleges’ Schools of Distinction standings, CSU Global earns one of only 15 gold rankings, out of more than 2,000 online degree programs.

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Guide to Online Schools identifies CSU Global as one of the top military-friendly online institutions.

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““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.” ”



Why You Should Earn Your Online Master’s Degree

Earning your online master’s degree through CSU Global provides you with more than just the diploma you need to get noticed. With an emphasis on real world concepts and career-relevant skills, you get training and experience along with your credit hours. Additionally, CSU Global strives to make your education as affordable as possible. With our Tuition Guarantee, your tuition rate won’t increase over time! Instead, you’ll enjoy the same low rate for the duration of your enrollment at CSU Global.

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Learn Valuable Skills

CSU Global’s MSDA students develop both hard and soft skills to get the career they deserve, while also gaining the knowledge, insight and confidence necessary for long-term growth as a leader. After completing the program successfully, you will be equipped to...

  • Practice ethical standards of data handling in analytics.
  • Apply policies and procedures to ensure the privacy and security of data.
  • Analyze large data sets.
  • Employ data analytic solutions for business intelligence and forecasting.
  • Evaluate principles of predictive analytics to address business challenges.
  • Articulate analytical conclusions and recommendations in written, verbal, and visual formats.

Career Outlook

Career Ladder

Download the MSDA Career Ladder to see the job title and responsibility progressions from Entry Level to Executive/C-level.

19% Growth Projected within the Field

Employment for computer and information research scientists is expected to grow significantly faster than the all-occupation average.

$86,873 Average Salary

According to the BLS, the average annual salary for computer information research scientists and database professionals is more than $86k.

Database Scientist, Operations Analyst, Database Architect

Some job titles you might obtain with a MSDA. Others include Enterprise Performance (CRM) Analyst, Fraud Analyst, or Database Analyst.

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