Business Intelligence & Analytics

What Are the Best Online Big Data Master’s Programs?

What Are the Best Online Big Data Master’s Programs?
There's a growing demand for analytics professionals who can make sense of the vast quantities of data created every day. Image from Unsplash
Christa Terry profile
Christa Terry November 22, 2019

No schools offer an MS in Big Data… yet. The discipline is relatively new, but it won't be long before universities begin creating dedicated big data programs.

Data Science Programs You Should Consider

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There’s a growing demand for analytics professionals who can make sense of the vast quantities of data created every day. These experts help sort through information and develop best practices for interpreting it to solve specific challenges. While there is currently no such thing as a master’s degree in big data, there are a lot of advanced online degree programs designed for analytics professionals across disciplines.

In this article about the best online big data programs, we’ll cover:

  • Why get a big data master’s degree?
  • The top online master’s degree programs for big data professionals
  • Jobs for big data professionals with master’s degrees
  • Big data job outlook
  • Best online big data master’s degree programs

Why get a big data master’s degree?

Simply put, having a master’s degree in big data will boost your employability. Industries large and small are generating vast quantities of data related to everything from consumer behavior to health outcomes. Those industries struggle to make the most of this potential treasure trove of information. Governments and retail companies alike desperately need skilled analysts who have the aptitude and expertise to find, sort, understand, and draw meaningful conclusions from that data. Technology professionals with big data bachelor’s degrees and master’s degrees are in demand, especially in:

  • Finance:
    • Risk Management: Big data analytics help in assessing and managing financial risks by analyzing large datasets for patterns and trends.
    • Fraud Detection: Enables the detection of fraudulent activities through real-time analysis of transactions and behaviors.
    • Investment Strategies: Assists in developing more sophisticated investment strategies by analyzing market data and economic indicators.
  • Insurance:
    • Underwriting and Pricing: Enhances the accuracy of underwriting and pricing models by analyzing vast amounts of data on risk factors.
    • Claims Management: Improves the efficiency of claims processing and helps in detecting fraudulent claims through data analysis.
    • Customer Insights: Provides deeper insights into customer behaviors and preferences, enabling personalized insurance products and services.
  • Information Technology:
    • System Optimization: Helps in optimizing IT infrastructure and improving system performance by analyzing data from network operations.
    • Security: Enhances cybersecurity measures by analyzing patterns of network traffic and identifying potential threats.
    • User Experience: Improves user experience by analyzing user interactions and behaviors to optimize applications and services.
  • Manufacturing:
    • Predictive Maintenance: Utilizes data from sensors and machines to predict equipment failures and schedule timely maintenance.
    • Supply Chain Optimization: Enhances supply chain efficiency by analyzing data on inventory levels, demand forecasts, and supplier performance.
    • Quality Control: Improves product quality by analyzing data from production processes and identifying defects or inefficiencies.
  • Retail:
    • Customer Behavior Analysis: Analyzes customer data to understand purchasing patterns and preferences, enabling personalized marketing.
    • Inventory Management: Optimizes inventory levels and reduces stockouts by analyzing sales data and demand forecasts.
    • Pricing Strategies: Develops dynamic pricing strategies based on real-time analysis of market conditions and competitor prices.
  • Science:
    • Research and Development: Accelerates scientific research by analyzing large datasets to identify patterns, correlations, and new insights.
    • Healthcare Research: Enhances the understanding of diseases and treatment outcomes by analyzing medical and genomic data.
    • Environmental Studies: Improves environmental monitoring and modeling by analyzing data from sensors, satellites, and climate models.
  • Technology:
    • Product Development: Drives innovation in product development by analyzing user feedback and performance data.
    • User Analytics: Enhances understanding of user interactions and behaviors to improve software and hardware products.
    • Operational Efficiency: Optimizes operations and reduces costs by analyzing data from various technology systems and processes.

(This section is written by Tom Meltzer)

In its 2017 report Investing in America’s Data Science and Analytics Talent: The Case For Action, PricewaterhouseCoopers predicted 2.72 million Big Data job postings in the year 2020. The same report foresaw “a significant shortfall in the number of data scientists and ‘data-enabled’ professionals” to take those jobs. Average advertised salaries for these positions range from just over $69,000 (functional analysts; 770,441 job postings) to over $91,000 (data-driven decision makers; 812,099 job postings). If PwC is to be believed—and the consulting firm has a pretty reliable track record—Big Data stands to be one of the major growth areas in business employment for years to come.

Data jobs require training, which come primarily through academia (there may be a few intrepid savants out there willing to learn stochastic calculus on the job, but probably not so many employers willing to let them). Universities have established a number of disciplines to address the need: the primary fields are business analytics, business intelligence, operations research, data science, and statistics. All of these disciplines employ practices and principles of computer science, mathematics, engineering, and business in some combination; in some instances, the name of the discipline has more to do with which academic department offers the degree than with actual differences among the disciplines.

What should you look for in a quality Big Data program?

Once you decide on a Big Data discipline and a degree, you’ll want to learn more about various programs’ resources, faculty, curricular balance, and reputation. Finally, you’ll want to calculate your return on investment.

Resources

In order to study Big Data effectively, you need access to: huge, rich databases to analyze; the apps businesses typically use to sort through and make sense of all the data; and, computers powerful enough to handle the workload placed on them by the databases and apps. If you’re considering a full-time program, make sure the program you choose is outfitted with the resources you’ll need to learn. The quality and cost of the resources provisioned to a program also provide a telling indication of how strongly the university supports it.

If you’re currently employed, consider giving preference to programs that utilize the same apps you use at work. It’s not absolutely necessary—other strengths of the program you ultimately choose may be more important in your decision—but it certainly would be a nice perk. Also look to see whether the university has an institute or center dedicated to Big Data studies. If it does, that means forward-looking research is among the school’s priorities, something to consider, especially if it is among your top priorities as well.

If you’re planning to study online, your access to computing power will obviously be limited to what is available at home or at work. Still, you want to make sure that your program will expose you to the most popular analytics apps. You’ll also want to find out how you’ll access these apps, what technical requirements your setup will have to meet, and how you’ll be delivering assignments that require work with big databases and powerful apps.

Online programs can handle all of these challenges but it does complicate things, and you’ll want to make sure the program you choose has effective solutions. Sometimes this information will appear on the program website, but if not, most online programs will assign you an admissions counselor/recruiter who will answer your questions.

Faculty

Besides the resources mentioned above, you’ll also need someone to teach you how to use them. Unless you’re in a PhD program, it’s not that important that your teachers be superstars in the field. Not that there’s anything wrong with being taught by thought leaders, but unless your aim is to be on the cutting edge of theory and research, what’s more important is finding teachers who are committed to and capable of teaching.

Once you’ve narrowed your choice of programs down to a few, find the names of key faculty members online and start googling. Search for their names, restricting your search to sites like reddit.com and quora.com. These threaded-discussion sites may contain some conversations about specific faculty members’ teaching skills. Sites like ratemyprofessors.com sound promising but in fact are mostly used by undergraduates and are therefore less helpful to potential graduate students.

Curricular balance

Big Data programs typically include instruction in advanced mathematics, statistics, computer science, engineering, and business, and most are team-taught by faculty from several (if not all) of these disciplines. How these disciplines are weighted in the curriculum—e.g. whether the curriculum emphasizes computer science over mathematics; whether it includes no, some, or a lot of business instruction—can vary depending on several factors. One is the type of degree: business analytics degrees tend to include more business courses, while business intelligence, operations research, data science, and statistics degrees tend to lean more heavily on advanced mathematics and computer science. Another is the school’s strength(s): if the school is a computer science powerhouse, for example, computer science is likely to feature more prominently in the curriculum.

Academic programs list their complete curricula—required courses and electives—on their websites, nearly always with useful descriptions of each course. Review the curriculum of each program you’re considering for its balance and also to see whether the course descriptions appeal to you. Course descriptions don’t always reflect course content with 100 percent accuracy but they’re usually close to the mark and they’re certainly better than nothing at all. Course descriptions will also give you a good sense of the program’s academic approach (passive vs. hands-on learning, theory vs. practical application, etc.).

Reputation

Most students pursue advanced degrees in order to improve their career prospects. A degree from a highly regarded university—say, California Institute of Technology—is guaranteed to impress employers more than a degree from a school they’ve never heard of. That’s one reason why students are willing to spend so much more to attend CalTech.

The list of schools with national reputations is relatively short. Much longer is the list of regional/local universities that are respected in their hometown, home state, or geographic region. For example, a Big Data degree from Stony Brook University will open doors for you in the Northeast, but maybe not in the Southwest. You should be familiar with the names of the schools in your region that employers love.

Speaking of programs and employers, most program websites have a careers section that lists employers of recent graduates. These lists often tell you who recruits program graduates most aggressively, and can give you some idea of the types of job offers a degree from that institution will bring your way. Have a close look before you commit to a program.

Return on investment

Most Big Data graduate degrees will improve your earning potential in the near and long term. Some will do that better than others, but of course may cost significantly more as well. For example, an online Big Data MBA from North Carolina State University will cost you a North Carolina resident a little over $51,000 in tuition; that same student will pay over $125,000 in tuition for an online MBA from University of North Carolina-Chapel Hill 30 miles away. Both are well-respected schools in their region and beyond. Is there enough of an advantage in attending UNC over NC State to justify the cost differential? Is there any advantage at all?

The problem in answering that question is (paradoxically given the subject of this article) the lack of reliable data. Most available data about the salaries of program graduates are reported by students themselves to the schools, which in turn use the results to promote their programs. Even assuming the data are accurate, they are at best incomplete (because they represent only those students who report their income). Such data can be helpful but isn’t determinative.

In the end you’ll have to decide on your own whether the ROI justifies the expense. Forbes’ Laura Shin offers a seven-step method for calculating ROI on a graduate degree that you might find helpful. The seventh step is “intangibles,” which brings us back to our UNC-NC State example: a true Tar Heel fan might conceivably pay the extra $75,000 rather than root for the Wolfpack.

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The top online master’s degree programs for big data professionals

Big data pros can choose from several online master’s degree options. As you explore them, remember that different universities use different naming conventions. The business analytics program at one school may be virtually identical to the data analytics engineering program at another. Read program guidelines carefully and look for online degree programs that include coursework specifically focused on big data and technical data analysis. Search for these degrees:

Master of Science in Data Analytics (or Data Analytics Engineering)

This degree is quant-heavy, and it’s a great choice if you want to work in data mining or business intelligence. Students in these programs learn:

  • Data extraction techniques: Methods and tools for retrieving data from various sources, ensuring data is gathered efficiently and accurately for analysis.
  • Predictive modeling: Techniques for creating models that can predict future outcomes based on historical data, using statistical methods and algorithms to forecast trends and behaviors.
  • Machine learning: The study and application of algorithms that enable computers to learn from and make decisions based on data, including supervised, unsupervised, and reinforcement learning techniques.
  • Data validation: Processes to ensure data quality and accuracy, including techniques for checking data integrity, consistency, and completeness before analysis.
  • Linear and quadratic analysis: Mathematical methods for analyzing relationships between variables, where linear analysis involves straight-line relationships and quadratic analysis involves parabolic relationships.
  • Other ways to use data to make strategic decisions: Various data-driven approaches to inform business strategy, including data visualization, business intelligence, and advanced analytics to support decision-making processes.

More importantly, what you’ll learn in a Master of Science in Data Analytics program will be applicable across all industries that generate large bodies of data, from healthcare to manufacturing.

Master of Business Administration in Big Data

Big data literacy is a highly sought-after competency in the business world. An MBA is first and foremost a business degree, but some schools have tracks, specialties, or concentrations related to big data. This degree is a wise choice if you want a solid business education plus a foundational understanding of data science, machine learning, and automation. Just be aware that some MBA programs dive deeper into big data than others.

Master of Science in Data Science

This highly technical data science program is probably the best choice for the professional who wants to become an expert in large-scale data analysis. In these programs, data science professionals use or hone programming skills such as SQL and Python and learn more about using data mining, data visualization, and applied machine learning to identify trends and create compelling stories with data.

Master of Science in Business Analytics

This is one of those degrees that varies widely by school. One university’s business analytics degree will include tech-heavy comp sci coursework related to business intelligence and information systems (like the one at the Massachusetts Institute of Technology, which is essentially a data science degree). Another school’s business analytics program might have a lot more in common with an MBA. Reading a program’s course list is the easiest way to determine whether the focus on that program is data science, math and statistics, or business.

Jobs for big data professionals with master’s degrees

Data figures more prominently in business decision-making and growth every day, and jobs in data are among some of the highest-paying around. According to an IBM report, job listings that ask for machine learning skills pay an average of $114,000, job listings for data scientists pay an average of $105,000, and data engineering jobs pay an average of $117,000. After earning a big data master’s degree, you might work as a:

  • Data scientist: Analyzes complex data sets to uncover patterns and insights, using statistical methods, programming, and machine learning to inform business decisions.
  • Big data architect: Designs and manages the architecture for big data solutions, ensuring the infrastructure can handle large-scale data processing and storage needs.
  • Information systems manager: Oversees the development, implementation, and maintenance of an organization’s information systems, ensuring they meet business needs and security standards.
  • Business intelligence analyst: Interprets data to provide actionable insights for business decisions, using tools and techniques to analyze data trends and patterns.
  • Business intelligence developer: Creates and manages BI solutions, including data visualization dashboards, reports, and data integration processes to support decision-making.
  • Applications architect: Designs and develops software applications, ensuring they meet user requirements and integrate seamlessly with other systems.
  • Machine learning scientist: Develops and implements machine learning models and algorithms to solve complex problems and improve business processes.
  • Statistician: Applies statistical methods and techniques to collect, analyze, and interpret data, helping organizations make informed decisions.
  • Solutions architect: Designs comprehensive IT solutions to address specific business problems, ensuring all components work together effectively and efficiently.
  • Visualization specialist: Creates visual representations of data to help stakeholders understand complex information and make data-driven decisions.
  • Big Data engineer: Develops, constructs, tests, and maintains big data architectures, ensuring efficient data processing and storage.
  • Data modeler: Designs and creates data models that define the structure, relationships, and constraints of data in a database or data warehouse.
  • Data warehouse manager: Manages the storage, retrieval, and analysis of large amounts of data, ensuring data integrity and performance of the data warehouse.
  • Enterprise data manager: Oversees data governance and management across an organization, ensuring data accuracy, availability, and security.
  • Analytics consultant: Provides expert advice on data analysis and interpretation, helping organizations use data to solve problems and achieve business goals.

Big data job outlook

The ability to leverage big data has become hugely important to success in a wide variety of industries. A big data master’s degree gives you the skills you need to work in nearly any of them. With this versatile degree, you will be able to find work in healthcare, finance, manufacturing, communications, technology, or public policy—and you’ll be able to find it relatively easily, according to IBM. The report linked above predicts that by 2020, the number of positions for data and analytics professionals in the US will jump by 364,000 openings to 2,720,000. Demand for data scientists and big data engineers is expected to grow by 39 percent. That may lead to a skills gap that you can capitalize on by getting a big data master’s degree.

Best online big data master’s degree programs

For each of the master’s degree programs below, we’ve included the most relevant information you should consider when looking for the best program for your needs:

  • Degree type
  • 2023-2024 tuition
  • Graduation requirements
  • Core courses
  • Concentration courses
  • Special program features

Brandeis University

  • Degree: Master’s of Science in Strategic Analytics
  • Tuition: $1,179 per credit hour
  • Graduation requirements: 30 credit hours (21 hours required courses, 9 hours electives)
  • Core courses: Foundations of Data Science and Analytics; Business Intelligence, Analytics and Strategic Decision Making; Statistics and Data Analysis; Strategic Analytics and Visualization for Big Data; Predictive Analytics; Data Quality and Governance; Analytics Strategy and Management
  • Electives: Three electives, select from: Communication for Effective Leadership; Data Analysis and Decision Support for Health Informatics; Advanced Healthcare Data Analytics; Cloud Security; Marketing and Customer Analytics; Data Security, Privacy and Ethics; Project Manager for Analytics; Special Topics in Strategic Analytics; Database Management; Data Warehousing and Data Mining; Cloud Computing
  • Features: Class size capped at 20; the average class size is 12; official STEM designation

George Mason University

  • Degree: MS in Data Analytics Engineering
  • Format: Online
  • Tuition: $930 per credit hour
  • Graduation requirements: 30 credit hours over less than two years
  • Core courses: Data Analytics Foundation; Big Data to Information; Applied Statistics and Visualization for Analytics; Analytics and Decision Analysis; Principles of Data Management and Mining; Data Analytics Project
  • Electives: Big Data Essentials; Database Management Systems; Bayesian Inference and Decision Theory; Applied Predictive Analytics; Information Representation, Processing and Visualization; Decision Support Systems Engineering; Applications of Metadata in Complex Big Data Problems; Knowledge Mining from Big-Data; Decision and Risk Analysis; Heterogeneous Data Fusion
  • Features: Course includes special workshops and lectures; rated one of the top schools to study big data analytics by TechRepublic; George Mason’s Volgenau School of Engineering has worked with big data and cybersecurity for more than 25 years

Georgia Institute of Technology

  • Degree: Master of Science in Analytics with a Computational Data Analytics Track
  • Format: Online
  • Tuition: $275 per credit hour
  • Graduation requirements: 36 credit hours (11 courses) including the completion of a capstone project with an external firm over one to three years
  • Core courses: Introduction for Computing for Data Analytics; Introduction to Analytics Modeling; Business Fundamentals for Analytics; Data and Visual Analytics; Data Analytics in Business
  • Electives: Machine Learning/Computational Data Analytics; Time Series Analysis; Nonparametric Data Analysis; Design and Analysis of Experiments; Regression Analysis; Computational Statistics; Bayesian Statistics; Data Mining and Statistical Learning; Simulation; Probabilistic Models; Deterministic Optimization; Nonparametric Data Analysis; Machine Learning/Computational Data Analytics; Design and Analysis of Experiments; Computational Statistics; Deterministic Optimization; Database Systems Concepts and Design; Information Visualization; Computational Science and Engineering Algorithms; Big Data Analytics in Healthcare; Web Search and Text Mining
  • Features: Has one of the lowest online tuition rates for US students; access to experts in the fields of business intelligence, statistics, and operations research, big data, and high-performance computing; no on-campus time is required; fall and spring admission; six-hour applied analytics practicum

Georgia Institute of Technology

  • Degree: MS in Business Analytics
  • Format: On-campus and online
  • Tuition: $1,230 per credit hour for residents; $1,698 per credit hour for non-residents (on-campus); $275 per credit hour for the online master’s degree program
  • Graduation requirements: 30 credit hours over one to two years
  • Core courses: Introduction for Computing for Data Analytics; Introduction to Analytics Modeling; Business Fundamentals for Analytics; Data and Visual Analytics; Data Analytics for Business; an applied analytics team practicum or internship
  • Electives: There are more than 50 courses that students can take to fulfill elective slots covering topics like forecasting, regression analysis, data mining, statistical learning, machine learning, computational statistics, simulation, optimization, probabilistic models, data analytics, data visualization, databases, web and text mining, algorithms, high performance computing, parallel computing, graph analytics, business intelligence, pricing analytics, revenue management, business process analysis, financial analysis, decision support, privacy and security, and risk analytics
  • Features: Has one of the lowest online tuition rates for US students; faculty includes experts in business intelligence, machine learning, statistics and operations research, and big data; analytics students participate in Georgia Tech’s Big Data Industry Forum; the school offers students robust career support; each student receives a conference travel budget for each student

Pennsylvania State University

  • Degree: Master’s Degree in Data Analytics
  • Format: Online
  • Tuition: $1,046 per credit/$12,552 for 12 or more credits per semester
  • Graduation requirements: 30 credit hours
  • Core courses: Foundations of Predictive Analytics; Data Mining; Applied Statistics; Large-Scale Database and Warehouse; Data-Driven Decision Making; Database Design Concepts
  • Electives: Principles of Demography; Data, GIS, and Applied Demography; Applications in Applied Demography; Data Visualization; Data Collection and Cleaning; Large-Scale Databases for Real-Time Analytics; Analytics Programming in Python; Enterprise Analytics Strategies; Network and Predictive Analytics for Socio-Technical Systems; Demographic Techniques; Statistical Analysis System Programming; Regression Methods; Applied Time Series Analysis; Technical Project Management; Decision and Risk Analysis Engineering
  • Features: Highly customizable via elective course choices; this degree path culminates in a capstone experience in which students design and implement analytics systems; geared toward data scientists, data engineers, data architects, research analysts, and data analysts

Slippery Rock University of Pennsylvania

  • Degree: Master of Science in Data Analytics
  • Format: Online, full-time and part-time
  • Tuition: $516 per credit in-state, $526 per credit out-of-state
  • Graduation requirements: 33 credit hours that can be completed in 10 months full-time or in two years by part-time learners (includes a capstone project)
  • Core courses: Statistical Methods; Introduction to Regression; Data Mining; Optim Models; Advanced Statistical Methods; Forecasting & Time Series; Big Data Analytics; Model Analysis; Statistical Computing
  • Electives: No electives listed
  • Features: Students have the option of pursuing an internship; curriculum includes the training necessary for the Certified Analytics Professional exam; students who complete the program also receive a joint certificate in statistical applications and data analytics from SRU and the SAS Institute Inc.

Syracuse University

  • Degree: MS in Applied Data Science
  • Format: Online, full-time or part-time
  • Tuition: $64,152 plus a per-semester technology fee
  • Graduation requirements: 36 credit hours
  • Core courses: Data Administration Concepts and Database Management; Introduction to Data Science; Data Analytics; Big Data Analytics; Data Analysis and Decision Making; Business Analytics; Accounting Analytics; Marketing Analytics
  • Electives: Linear Statistical Models I: Regression Models; Time Series Modeling and Analysis; Cloud Management; Information Policy; Introduction to Information Security; Scripting for Data Analysis; Natural Language Processing; Information Visualization; Data Warehouse; Text Mining; Advanced Database Administration Concepts and Database Management
  • Features: Offered in collaboration with Syracuse University’s Whitman School of Management; opportunities for students to join a research lab or collaborate with faculty on their academic work for hands-on experience; some elective credits can also be used toward a CAS in Information Security Management; online students have access to the same faculty and degree as campus students

University of California

  • Degree: Master of Information and Data Science (MIDS)
  • Format: Online, synchronous and asynchronous format
  • Tuition: $2,573 per course credit
  • Graduation requirements: 27 credits over 12 months, including a capstone project
  • Core courses: Introduction to Data Science Programming; Research Design and Applications for Data and Analysis; Statistics for Data Science; Fundamentals of Data Engineering; Applied Machine Learning; Data Visualization; Behind the Data: Humans and Values; Experiments and Causal Inference; Deep Learning in the Cloud and at the Edge; Machine Learning at Scale; Natural Language Processing; Statistical Methods for Discrete Response, Time Series, and Panel Data
  • Electives: Statistics for Data Science; Fundamentals of Data Engineering; Deep Learning in the Cloud and at the Edge
  • Features: One required three-to-four-day on-campus immersion experience; curriculum touches on ethical and legal requirements of data privacy and security; live online classes capped at 18 students; students receive Global Access membership to WeWork workspaces

University of California

  • Degree: Master of Science in Engineering With Certificate of Specialization in Data Engineering
  • Format: Online
  • Tuition: $36,000 for full nine-course, 36-credit program ($4,000 per course)
  • Graduation requirements: 36 credit hours within two academic years and one quarter, including two summer sessions including the completion of a capstone project OR three written exams
  • Core courses: Big Data Analytics; Large-Scale Data Mining: Models and Algorithms; Database Systems; Current Topics in Data Structures; Matrix Analysis for Scientists and Engineers; Machine Learning Algorithms; Large-Scale Social and Complex Networks: Design and Algorithms; earning and Reasoning with Bayesian Networks
  • Electives: Probability and Statistics; Digital Speech Processing; Advanced Topics in Speech Processing; Mathematical Foundations of Data Storage Systems; Web Information Systems
  • Features: Students learn to design and build big data systems and learn necessary data-mining and machine-learning techniques

University of Denver

  • Degree: Master of Science in Data Science
  • Format: Online, flexible
  • Tuition: $1,042 per credit
  • Graduation requirements: 48 to 60 credit hours completed over 18 to 24 months, including a capstone project
  • Core courses: Algorithms for Data Science; Machine Learning; Parallel and Distributed Computing for Data Science Introduction to Probability and Statistics for Data Science; Python Software Development; Database Organization & Management; Algorithms For Data Science; Data Science Tools; Data Mining; Data Visualization; Machine Learning; Parallel and Distributed Computing
  • Electives: No electives listed
  • Features: Faculty includes top researchers in areas like databases, algorithms and statistics; three bridge courses prepare students without computer science degrees for the program; students receive Global Access membership to WeWork workspaces

University of Maryland

  • Degree: Master’s in Business Analytics
  • Tuition: $1,566 per credit hour
  • Graduation requirements: 30 credit hours
  • Core courses: Data, Models, and Decisions; Data Mining and Predictive Analytics; Database Management Systems; Decision Analytics; Social Media and Web Analytics; Big Data and Artificial Intelligence; Strategy Analytics; Data Processing and Analysis in Python
    Specialization Courses: Intro to Financial Accounting; Corporate Finance; Financial Analytics; Healthcare Operations Management and Marketing; Marketing Management; Consumer Behavior; Advanced Marketing Analytics; Health Informatics and Information Technologies; Supply Chain Risk Management; Leadership and Teamwork
  • Electives: None
  • Features: Students can graduate in as few as 20 months; specializations in finance analytics, marketing analytics, and healthcare analytics are available; flexible curriculum blends synchronous and asynchronous content; 100 percent online; STEM-eligible

University of Missouri

  • Degree: Master of Science in Data Science and Analytics
  • Format: Hybrid (students are required to spend one week per year on campus)
  • Tuition: $1,075 per credit hour
  • Graduation requirements: 34 credit hours completed over two years plus a big data analysis case study and a big data capstone
  • Core courses: Introduction to Data Science and Analytics; Statistical and Mathematical Foundations for Data Analytics; Database and Analytics; Data Analytics from Applied Machine Learning; Big Data Security; Big Data Visualization; Data and Information Ethics
  • Electives: Streaming Social Media Data Management and Analysis; Communication Networks/Analytics; Spatial and Geostatistical Analysis; Data Mining and Information Retrieval; Cloud Computing for Data Analytics; Advanced Visualization and Communication; Geospatial Data Engineering; Remote Sensing Data Analytics; Parallel Computing for Data Analytics; Genomics Analytics; Data Science for Healthcare
  • Features: Students get real-world experience in applying state-of-the-art data science tools and techniques; the program includes work in five different disciplines (biotech, geospatial, high-performance computing, human-centered data design, and data journalism)

University of North Texas

  • Degree: M.S. in Advanced Data Analytics
  • Format: Online
  • Tuition: $13,300 for in-state residents; out-of-state students pay $450 per credit
  • Graduation requirements: 30 credit hours in 10 classes plus a capstone experience
  • Core courses: Introduction to Data Analytics; Data Analytics; Harvesting, Storing and Retrieving Data; Discovery and Learning with Big Data; Large Data Visualization
  • Electives: Related elective options include Deep Learning with Big Data and Recurrent Neural Networks for Sequence Data. Specialty elective courses are selected with the help of a program advisor in areas like health services, statistics, management, digital retailing, and sports entertainment
  • Features: Accelerated online option takes less than two years to complete; curriculum includes real business case studies; students can create a custom specialization with an advisor; five start times per year; hybrid format available

(Updated on June 18, 2024)

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Tom Meltzer spent over 20 years writing and teaching for The Princeton Review, where he was lead author of the company's popular guide to colleges, before joining Noodle.

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