The term “data science” dates to 1974, when programming pioneer Peter Naur proposed it as an alternative name for “computer science.” The modern sense of the term, however, originated with William S. Cleveland in his 2001 paper, “Data Science: An Action Plan to Expand the Field of Statistics”. In that work, Cleveland addressed academic statistics departments at the university-level to propose a plan to reorient their work.
After Cleveland’s position paper came a wave of academic journals recognizing data science as an emerging discipline. 2002 saw the launch of Data Science Journal and its papers on “the management of data and databases in Science and Technology.” By 2005, the National Science Board was advocating for a data science career path to underscore the importance of data scientists, who “are crucial to the successful management of a digital data collection.”
Fast forward to today, when practically everyone generates massive amounts of data. Shopping, communicating, reading news, listening to music, searching for information, expressing our opinions—all of these are tracked online in specific and intimate ways. It’s not just Internet data, either. Data science experts gather and analyze streams of data in virtually every sector, from finance to healthcare, social welfare, government, education—the list goes on.
As a result, more employers than ever before are searching for qualified data scientists who can help them generate insights from all this data. With demand for the skills they teach soaring, online master’s programs in data science, business analytics, information systems, and other related fields are springing up across the US to offer aspiring students a flexible path to securing the training they need to advance.
Our guide to the best online master’s in data science programs covers the following questions:
- Why do people enroll in data science programs online?
- What do you study in an online master’s program in data science?
- What are the admissions requirements for online master’s in data science programs?
- How long does it take to complete a data science master’s program online?
- Are online data science programs less expensive than traditional programs?
- Which schools have the best online data science programs?
Why do people enroll in data science programs online?
In many industries, a master’s degree promises the skills and knowledge you’ll need to advance your career and move into well-paying or high-level positions. Achieving a master’s can present a formidable challenge, however. Many master’s-age students have careers, families, and previous debts to manage. Attending an on-campus master’s program may be one commitment too many for them.
Unlike traditional on-campus programs, online master’s programs in data science offer students the flexibility to study, complete assignments, and even take exams at their own pace. Rather than leave the office early or skip out on family time to attend classes at a fixed time and location, they can work on their degree where it’s convenient—at times that don’t interfere with other commitments.
Online education is growing in popularity. Consider the Urban Institute’s 2017 report, which indicates that in 2004, 9 percent of part-time master’s students and 5 percent of full-time master’s students engaged in distance learning. In 2016, the proportion of part-time students jumped to 36 percent, and the percent of full-time students to 27 percent. Of the more than 800,000 master’s degrees awarded in 2017, more than 40 percent were awarded through a program that was available either only online or in both in-person and online formats.
What do you study in an online master’s program in data science?
Students in online master’s in data science programs learn different foundational aspects of data science, including big data, the design and use of advanced algorithms, and machine learning for artificial intelligence applications. Core courses typically cover natural language processing, project management, data visualization, and predictive modeling, among other topics.
Online data science programs also offer a tremendous amount of room to develop a specialty. For example, students completing a Master of Science (M.S.) in Data Science at DePaul University have the option to pursue a concentration through online courses in computational methods, healthcare, hospitality, or marketing. Other programs may also include a thesis or capstone project, as well as an optional or required internship or practicum designed to facilitate experiential learning through hands-on experience.
What are the admissions requirements for online master’s in data science programs?
Admission requirements for online master programs in data science vary. Applicants are typically evaluated on a combination of undergraduate academic performance, work experience, and career goals to assess whether they’re prepared for the technical and intellectual challenges of earning a graduate-level data science degree.
Beyond that, most reputable online master’s programs in data science require:
- Bachelor’s degree in a STEM or business discipline from an accredited college or university
- An undergraduate GPA within a prescribed threshold
- Resume or CV
- Letters of recommendation
- Personal goals statement
Some programs also require students to provide GRE and/or GMAT scores with their applications. However, many graduate programs, such as the online Master of Information and Data Science at the University of California – Berkeley (UC Berkeley), don’t require them.
Other programs require the GRE or GMAT conditionally. In these cases, they may waive their GRE/GMAT requirements for applicants who have several years of relevant professional experience, a graduate degree in another field, and/or an undergraduate GPA of 3.0 or higher. The online Master of Science in Data Science program at the University of Denver is one such example.
How long does it take to complete a data science master’s program online?
The length of online master’s programs in data science varies depending on the college or university, the program structure, the number of courses students take per semester, and whether the program offers courses year-round.
Full-time students typically complete their degree in 12 to 16 months. Part-time students can earn the degree in two to three years. Alternatively, some online degree programs offer an accelerated curriculum that allows students to reduce substantially the time it takes to earn their degrees.
Take the Accelerated Master’s in Data Science program at Northwestern University. Compared to the school’s twelve-course MS in Data Science program, this track consists of just nine courses. Caveat: it’s open only to graduates of the Micromasters in Statistics and Data Science program at the Massachusetts Institute of Technology.
Are online data science programs less expensive than traditional programs?
Cost can be a significant factor in choosing a master’s degree. Be aware that there is no hard-and-fast rule on how graduate schools price their online programs—and how their price compares to the master’s programs they offer on-campus.
While searching for schools, prospective students may find plenty that charge higher tuition rates to online learners. Others charge the same rates, or lower. Some schools may also assess a technology or online access fee on a per-credit or per-semester basis.
Additionally, when a state-run graduate school is located in a different state, students may find themselves burdened by expensive out-of-state tuition. But the concept of in-state and out-of-state tuition doesn’t apply to all online master’s degrees in data science. Many schools, including public schools, offer similar or identical rates to both resident and non-resident students.
Which schools have the best online data science programs?
Each of the online data science master’s degrees featured below includes the information you need to find a program that suits your needs, interests, and career goals. We’ve chosen programs based on:
- Degree type
- Graduation requirements
- Core courses
- Concentrations (when available)
To assemble this list, we consulted US News & World Report school rankings as well as program websites and graduate education catalogs.
CUNY City College (also referred to as the City College of New York)__
Graduation requirements: 30 credits
Core courses: Advanced Programming Techniques, Fundamentals of Computational Mathematics, Statistics and Probability for Data Analytics, Data Acquisition and Management, Knowledge and Visual Analytics, Business Analytics and Data Mining, Analytics Master’s Research Project
Electives: Simulation and Modeling Techniques, Mathematical Modeling Techniques for Data Analytics, Recommender Systems, Quantitative Finance, Web Analytics, Machine Learning and Big Data, Predictive Analytics, Data Structures and Algorithms for Distributed Systems, Current Topics in Urban Sustainability: Energy, Special Topics in Data Analytics, Current Topics in Urban Sustainability: Complex Systems, Independent Study
Features: Students who have completed credit-bearing courses or professional experience in data science disciplines but are no longer proficient have the option to take bridge courses in R Programming, SQL, and Data Science Math to refresh their knowledge and skills before enrolling.
Features: Lectures are recorded and posted each week for students to watch at their convenience. Faculty also host virtual office hours, group chats, and guided discussions to ensure that the learning is interactive and that students are engaged and connected. Optional Online Immersion Weekend (OIW) is hosted for online students each spring on the IU Bloomington campus.
Concentrations: Data Analytics and Visualization, Intelligent Systems Engineering, Precision Health, Cybersecurity
Graduation requirements: 30 credits cover 9 credits of core courses, 9 credits of prescribed courses, 9 elective credits, and a 3-credit culminating capstone experience
Core courses: Foundations of Predictive Analytics, Data Mining, Applied Statistics
Electives: Principles of Demography, Data, GIS, and Applied Demography, Applications in Applied Demography, Data Collection and Cleaning, Large-Scale Databases for Real-Time Analytics, Network and Predictive Analytics for Socio-Technical Systems, Analytics Programming in Python, Data Visualization, Enterprise Analytics Strategies, Demographic Techniques, Statistical Analysis System Programming, Regression Methods, Applied Time Series Analysis Technical Project Management, Decision and Risk Analysis Engineering
Concentrations: Business Analytics, Marketing Analytics
- Degree: Master of Science in Data Science
- Tuition: $508.82 per credit for Indiana residents; $812.71 per credit for non-residents
- Graduation requirements: 30 credit hours of graduate-level coursework, capstone project that applies the knowledge and skills learned to solve real-world problems for a company, organization, or individual
- Core courses: Statistical Analysis for Effective Decision-Making, Introduction to Statistics, Elements of Artificial Intelligence, Machine Learning for Signal Processing, Deep Learning Systems, Applied Machine Learning
- Electives: Students must complete two elective courses within their specific concentration:
- Data Analytics and Visualization: Engineering Cloud Computing, Deep Learning Systems, Information Visualization, Search, Big Data Applications and Analytics, Management, Access, and Use of Big and Complex Data, Introduction to Business Analytics Modeling, Data Visualization, Network Science
- Intelligent Systems Engineering: Internet of Things, Autonomous Robotics
- Precision Health: Simulating Cancer as an Intelligent System, Data Science for Drug Discovery, Health, and Translational Medicine, Real-World Data Science, Semiparametric Regression with R
- Cybersecurity: Security in Networked Systems, Organizational Informatics and Economics of Security, Systems and Protocol Security and Information Assurance
- Degree: Master of Science in Data Science
- Tuition: $820 per credit hour, $75 technology fee per course
- Graduation requirements: 36 credits including data science practicum
- Core courses: Introduction to Data Science, Data Engineering, Ethics, Privacy, and Social Justice in Data Science, Data Analytics, Statistical Methods and Experimental Design, Information Technology Research Methods
- Electives: Data Collection and Preparation, Business Intelligence, Exploratory Data Analysis, Statistical Inference and Predictive Analytics, Visualization, Geographic Information Systems, Machine Learning, Text Analytics, Reinforcement Learning, Deep Learning, Artificial Intelligence
- Features: Classes are taught in an 8-week format, either online or on-campus during the evenings. All classes taken as part of Regis University’s Data Science graduate certificate can be applied toward this program so students can continue their education going seamlessly.
- Optional concentration: Data Engineering
Residents of western states may qualify for in-state tuition
Graduation requirements: 30 credit hours
Core courses: Database Management, Information Systems Management and Strategy, Security and Privacy, Programming with Python, Analysis Modeling and Design, Network Design, Business Intelligence and Analytics, Project Management, Governance and Service Management
Electives: Up to six credit hours of Business School or Computer Science (CSCI) courses
Features: Students have full access to CU Denver’s Business Career Connections team, who can help with career planning, keep them up-to-date on the latest internship opportunities, and offer connections to over 300 business partners from top companies.
Concentrations: Business Intelligence Systems, Cybersecurity and Information Assurance
- Degree: Master of Science in Applied Data Science
- Tuition: $1,782 per credit plus $75 technology fee per course
- Graduation requirements: 36 credits with a portfolio milestone exit requirement
- Core courses: Introduction to Data Science, Data Administration Concepts and Database Administration, Data Analytics, Data Analysis and Decision Making, Business Analytics, Big Data Analytics
- Electives: Scripting for Data Analysis, Natural Language Processing, Information Visualization, Data Warehousing, Text Mining, Advanced Database Management, Information Policy, Introduction to Information Security
- Features: Online courses are delivered with four starts per year, where courses run for 11 weeks through a mix of asynchronous and synchronous course interaction. Class sizes run from 12-18 students.
- Concentrations: Accounting Analytics, Financial Analytics, Marketing Analytics, Supply Chain Analytics
Electives: Interactive Computer Graphics, Programming Languages and Compilers, Software Engineering I, Numerical Analysis, Parallel Computing Internet of Things, Methods of Applied Statistics
Features: Students receive lectures through Coursera’s massive open online course (MOOC) platform but are advised and assessed by Illinois faculty and teaching assistants on the more rigorous set of assignments, projects, and exams required for university degree credit.
Graduation requirements: 36 credits with up to 6 transfer credits accepted
Core courses: Decision Management Systems, Data Management and Visualization, Machine Learning, Predictive Modeling, Big Data Analytics, Data Analytics Capstone
Features: 2016 Distance Education Innovation Award Finalist (National University Technology Network), No. 2: 2017 Watson Analytics Global Competition
Graduation requirements: 34 credit units completed in 27 + courses, includes culminating capstone learning experience
Core courses: Essential subjects of applied data science with an emphasis on an end-to-end, applied approach to data
Features: Students have access to career and professional development support from UMSI’s Career Development Office, which includes use of the school’s online job board and alumni networks. Full-time and part-time options available. All applicants are automatically considered for scholarships up to and including the full cost of tuition. Flexible four-week, one-credit module scheduling; students may take one to three credits per month.
Military/Homeland Security Non-Resident: $362.45 per credit hour after Military Waiver
Graduation requirements: 33 credit hours
Core courses: Computing Structures, Database Management Systems, Algorithm Analysis, Fundamentals of Engineering Statistical Analysis, Intelligent Data Analysis, Advanced Analytics and Metaheuristics
Electives: Data Analytics and Media, Introduction to R, Data Analytics and Meteorology, Advanced R, Data Visualization, Python for Data Science, Energy Analytics, Time Series Analysis, Financial Engineering Analytics, Bayesian Statistics, Machine Learning Practice, Text Analytics, Data Mining, Visual Analytics, Systems Optimization, Supply Chain Management and Transportation, Quality and Reliability Engineering
Features: Offers various training opportunities and collaboration with industry partners in the data science field. Can be completed in as few as 24 months.
- Degree: Master of Computer Science in Data Science
- Tuition: $670 per credit hour
- Graduation requirements: 32 credit hours of graduate coursework, completed through eight graduate-level courses each at the four-credit hour level
- Core courses: Students must complete one course each (with a grade of B- or higher) from the following four subject areas:
- Machine Learning: Applied Machine Learning, Computational Photography
- Data Mining: Text Information Systems, Database Systems, Introduction to Data Mining
- Data Visualization: Data Visualization
- Cloud Computing: Cloud Computing Concepts, Cloud Computing Applications, Cloud Networking
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