The most successful businesses employ professionals to analyze data and find more efficient ways to operate. From global corporations and government agencies to small businesses, retail stores and nonprofit organizations, data analysis is core to daily function. The master’s in data analytics online program at SNHU can help you gain specialized skills in data analytics to fill these crucial roles.
Thanks to the explosion in the amount of data businesses use, the field of data analysis is among the fastest growing, with a large number of exciting, high earning job openings such as:
SNHU's MS analytics degree is recognized by the Professional Science Master's (PSM) national office as a Professional Science Master's program. The PSM is an innovative degree designed to allow students to pursue advanced training in science or mathematics while developing workplace skills valued by employers.
In as fast as 15 months, the 12-course master's in analytics core covers everything from Foundations of Data and Decision Methods and Modeling, to Presentation and Visualization of Data, Optimization and Risk Assessment, and Predictive Analytics.
SNHU’s master’s in analytics program goes beyond the traditional view of data collection and interpretation. You’ll develop concrete skills to apply to any work setting. Upon completion of the capstone in data analytics, you’ll come away knowing how to leverage data quickly and effectively, plus something even more valuable: how to help a wide variety of industries make mission-critical decisions, assess risk, operate more efficiently and compete more successfully.
As a private, nonprofit university, SNHU has one mission – to help you see yourself succeed. The benefits of earning your master’s in data analytics at SNHU include:
Acceptance decisions are made on a rolling basis throughout the year for our five graduate terms. You can apply at any time and get a decision within days of submitting all required materials. To apply, simply contact an admission counselor, who can help you explore financial options. Your counselor can also walk you through the application process, which involves completing a graduate application ($40 fee) and providing undergraduate transcripts.
Candidates must also submit a professional resume.
The demand for professionals with degrees in data analytics is growing every day. A study by IBM and Business-Higher Education Forum (BHEF) projects that the number of U.S. positions for data and analytics talent will increase by 15% by 2020, with average salaries of just over $80,000. The U.S. Bureau of Labor Statistics projects demand for market research analysts at 19 percent and operations research analysts at 30 percent through 2024.*
In addition to becoming a data scientist or analytics manager, graduates can look forward to a potential career as a:
In industries like:
Our master’s in data analytics online program focuses on the strategic and advanced uses of data analytics across a broad range of industries. Courses cover data mining, visualization, modeling, optimization and the ethical uses of data.
We live in a world where substantial amounts of data are available at the touch of a button. While this may be a very empowering prospect, it can also be overwhelming. In this course, students will examine the status of Big Data and its impact on the business world, framing analytics challenges using a structured life cycle approach to data analytics problems. Emphasis will be placed upon the verification of data, analytic techniques and open source tools for analyzing data, the role of regulatory organizations, and the privacy and ethics issues that surround its use.
Understanding the complexity of current data management systems and the ever evolving technology necessary to leverage such data is essential in making sound data-driven decisions. In this course, students will examine the issues in managing data and information from an enterprise perspective, and explore data management as an essential resource to organizational success through a deeper understanding of the concepts and techniques for managing the design, development, and maintenance of all the components of enterprise information management. The course will examine the roles and responsibilities of the various professionals that manage data and information in an organization.
The role of many analysts is as much about interpreting the results of data analysis as it is about gathering the data and "crunching the numbers." In this course, students will learn how to evaluate data in context, interpret data trends, and receive an overview of decision support management techniques such as predictive modeling, risk assessment and optimization, and analytics algorithms, which will set the stage for more advanced study in subsequent courses. Concepts from enterprise data management, including data warehousing and business intelligence, will provide a foundation for examining the topics of data mining, advanced and dimensional data modeling, and decision support system development as techniques for an organization's competitive advantage.
In addition to the gathering and interpretation of data, today's business environment calls upon the analyst to communicate the results of data analysis to a variety of audiences. In this course students will learn how to synthesize the technical components of data analysis into reports, presentations, and visual dashboards that are meaningful for the intended audience and deliver those components in a coherent, convincing format.
In the competitive business world, using data to its best advantage becomes all the more crucial. In this course, students will learn how to discern the levels of relevancy of data and the impact it has on operations as well as hone their ability to identify macro and micro level risk and evaluate risk management programs, policies, and strategies.
Building on prior coursework in decision methods and modeling, students will get a deeper understanding of the art and science of predictive analysis. Students will examine the elements that contribute to building reliable predictive models that result in actionable performance predictions such as identifying the variables that have the most predictive power and developing and deploying predictive models currently in use.
This course will emphasize the employment of advanced analytic strategies over the entire life cycle of the data analysis process. Using a comprehensive case-studies approach, students will logically extend and add definition to their existing analytic skill set, resulting in the development of a project proposal that will serve as preparation for the capstone experience.
This course includes the study of concepts, tools, and practices of project management. The course adopts a managerial process approach to Project Management, which consists of initiating, planning, executing, controlling and closing the project. Major topics will include project scope, project time, project cost, project quality, project risk, project resources, project communications and how to be an effective project manager. Cases are utilized to integrate the learning in the course and provide decision- making experience for the student.
This capstone course is the culminating experience for the M.S. in Data Analytics program. The aim of the capstone is to assess students' ability to synthesize and integrate the knowledge and skills they have developed throughout their coursework, rather than introducing new concepts. This course is structured to support student success in fulfilling program requirements.
Select any three graduate-level IT or QSO courses within the course numbers 500 to 799
This course provides students with an introduction to key concepts and tools in data and information management. Basic database administration tasks, file processing, file organization, data storage, and conceptual, logical, and physical data models will be introduced as a foundation to advanced database, data analysis, and information management skills. Students will gain exposure and be able to differentiate among common data and information management technologies that provide decision support capabilities to organizations.
The focus of this course is to enable students to develop a foundation of basic statistical literacy. Students will be able to assess the role of statistics in quantitative research and mixed methodologies, as well as develop the competency to perform basic statistical calculations. An awareness of the relationship between computation and interpretation will be addressed. Students will focus on the analysis of real-world data and research situations to illustrate the process of interpreting the meaning underlying the data, and how statistics can be utilized to address important questions.
Tuition rates for SNHU's online degree programs are among the lowest in the nation. We offer a 25 percent tuition discount for U.S. service members, both full and part time, and the spouses of those on active duty.
*Tuition Rates are subject to change and are reviewed Annually.
$40 Application Fee, $150 Graduation Fee, Course Materials ($ varies by course)
Southern New Hampshire University is a private, nonprofit institution accredited by the New England Association of Schools and Colleges as well as several other accrediting bodies. More...
*The Harvard Business Review - October 2012 Edition, on the Internet at https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century (viewed online August 24, 2017).
*CareerCast - "The Best Jobs of 2017", on the Internet at http://www.careercast.com/jobs-rated/best-jobs-2017?page=4 (viewed online September 13, 2017).
*CNN Money - "Fastest Growing Jobs", on the Internet at http://money.cnn.com/gallery/pf/2017/01/05/fastest-growing-jobs-2017/2.html (viewed online September 13, 2017).
*Glassdoor - "50 Best Jobs in America", on the Internet at https://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm (viewed online September 13, 2017).
*Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, 2016-17 Edition, on the Internet at https://www.bls.gov/ooh/business-and-financial/market-research-analysts.htm & https://www.bls.gov/ooh/math/operations-research-analysts.htm (viewed online August 2, 2017). Cited projections may not reflect local and/or short-term economic or job conditions and do not guarantee actual job growth