Compare online data science master's degree programs

Admission requirements for a Master of Data Science online degree

Admission requirements for online data science vary by university, but most will require a completed from an accredited institution in fields such as , data analytics, computer science, engineering or mathematics. Other common requirements typically include:

• Official transcripts

• Résumé

• Personal statement

• Letters of recommendation

• Demonstration of industry experience

• GRE scores

Note that some master’s in data science online programs may not require GRE scores to apply, so make sure to check your preferred program’s requirements. It also may be important to note any prerequisite courses on topics such as programming, statistics, or math.

Master’s in data science online curriculum

An online curriculum in data science will be similar — and just as challenging — as a traditional, on-campus curriculum. Coursework will include topics such as programming, data mining, machine learning, database management and data visualization, and will typically include a mix of core courses and electives. Some online master’s degree in data science programs may offer areas of concentration — like language analytics or data platforms — for even greater focus, and certificate options also may be available.

You can expect to participate in classroom discussions, projects, presentations, and breakout groups, just as you would in a traditional program. You also will have access to your instructors in scheduled office hour sessions, as well as opportunities to connect and ask questions as needed. Additionally, many data science programs include an on-campus immersive experience, during which you will visit, in-person, your university’s campus, for a few days of invaluable networking opportunities, engaging group projects, and meaningful real-world problem solving.

One of the most prominent advantages of a master’s in data science online program is flexibility: Students typically complete coursework on their own schedule and earn their degree while continuing to work in their current career and maintaining their personal and/or family responsibilities. To make the data science master’s programs even more flexible, edX offers part- and full-time options, and makes all materials accessible around the world.

Master of data science jobs

There are many different types of career opportunities within the data science field, with many graduates leveraging their new-found technical expertise to succeed in industries that include biotech, energy, finance, government, health care, or manufacturing. This skill set allows organizations to maximize efficiency, grow customer relationships, and reach business goals.

Businesses depend on skilled data scientists to communicate the insights they gather and help develop a plan of action for implementing decisions. They work closely with different stakeholders within an organization, often employing a multifaceted approach to their work that values both quantitative and qualitative abilities.

Employees and other professionals with data science expertise are often among the most valued members for their capability of offering indispensable business and organizational skills, as well as their ability to communicate a possible strategy and a path forward. Possible data science job titles include:

• Business analyst

• Computer information scientist

• Data analyst

• Data architect

• Data engineer

• Operations research and systems analyst

• Software developer

• Technical integration analyst

• Solutions engineer

What are the highest data science salaries?

Data science salaries may vary based on location, employer, job title, and industry. A candidate’s qualifications and collective years of experience may open the door for management positions, which typically command higher pay than more entry-level positions. Depending on the organization, there may also be an available sign-on bonus.

The median annual wage for data scientists was more than $100,000 in 2022. However, the highest 10% of data science professionals earned nearly $175,000.type: embedded-entry-inline id: 1EgaCtqUSBkP2iOiBK9Ahe

How to become a data scientist online

While there is no single pathway to a successful career in data science, the process involves acquiring relevant knowledge and a robust skill set that will enable you to help solve some of the world’s most pressing challenges and communicate solutions in an efficient and engaging manner.

A number of data science professionals enter the workforce with education and training in , statistics, or engineering. Those who study data science specifically, or a related discipline, also may work in healthcare, building statistical models that support clinical decision-making.

While there is no widely accepted minimum education requirement for becoming a data scientist, a combination of technical and non-technical skills may help along the way, and graduate programs can help professionals acquire them. A data science foundation from working on open source or on personal projects, as well as volunteer work, can come in handy, too. Critical thinking is a crucial skill set, especially if rooted in the desire to innovate, solve problems, collaborate, and be productive.

Ultimately, your career path will depend on personal goals, interests, and educational background. Being intellectually curious, attentive to detail, proactive in solving problems, and eager to learn new skills are just a few examples of personal traits that will help professionals be more effective in the workplace, master core competencies, and keep up with new developments in the field.

Find the best online data science program for your career

There are many aspects to consider when choosing an online data science program. Because these programs are indeed online, you can select the one that fits your exact needs and goals, regardless of where it’s located. Here are a few factors you may consider when choosing a Master of Data Science online program:

Length of study: Program length may vary anywhere from a year — often for accelerated programs — to 24 months, part time, depending on program requirements. This often amounts to 48 to 60 credit hours. Additionally, some programs may offer bridge programs — or foundational coursework — for students without a data science background or experience.

Internships/immersions: While most online data science programs do not require an internship, many require one or more on-campus immersion experiences where you may work with students in your cohorts to solve real-world data science problems and network. Students may also be encouraged to attend their graduation ceremonies.

Faculty/university prestige: Often, online instructors are the same faculty who teach in a given university’s on-campus program, and a majority have robust research backgrounds or extensive field experience. Additionally, it is worth checking if the university has won any awards or recognition for its data science program.

Networking: Many data-science schools will typically have virtual networking events, as well as in-person opportunities during immersions on campus. Alumni and faculty also may be great networking resources for your future, and most online graduates have access to the university’s alumni organizations.

Career outcomes: While the data science field is exploding, some programs may offer more focused career resources or job-search support than others. In fact, some universities offer career assistance to both online and on-campus graduates for the duration of their careers. This may include résumé advice, interview tips, or connections to open positions.

Online experience: Quality online data-science master’s programs ensure your experience is just as valuable as on-campus programs. This may include live courses, an accessible platform, faculty connections, and supportive resources.

Admission requirements: The data science programs you are considering should clearly list admission requirements on their websites. These requirements may vary slightly, but generally include letters of recommendation, a personal statement, a résumé, and test scores. While most require an application fee, this fee can often be waived if you apply by certain deadlines or attend a pre-application webinar.

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