Find Your Data Science Degree

Discover Data Science is all about making connections between prospective students and educational opportunities in an exciting new field. We believe that the world can benefit from more well-trained data scientists working across all levels of industry and academia.

Through extensive research we have found a demand from both students and schools for a comprehensive resource that provides information about data science bootcamps and certificate programs, as well as undergraduate and graduate degree offerings. On campus master’s in data science programs continue to be very popular, but online master’s in data science programs are growing in popularity as well.

What is Data Science?

From better dating apps to curing cancer, the ability to access and process good data streams are opening new opportunities across a wide range of career options. It’s hard to come up with a precise definition of what a data scientist is because the raw information that data scientist work with comes in various forms and has many applications. But, at its core, data science is about using traditional scientific methods, like computation, statistics, and mathematics in an interdisciplinary way to help inform, recommend, and advise based on real trends, behaviors, and analytics.

What is a Data Scientist?

A data scientist might be a number-cruncher, a trend observer, or a pattern finder. Data scientists work in all kinds of roles from large corporations to lean startups. Data scientists might help policy makers develop better decisions, or they might work in finance or healthcare. Data scientists are technically proficient and often use a number of computer code languages to compile and synthesize data. A background in advanced statistics and intermediate to advanced levels of programming skill are required. Depending on the field, data scientists also have to be creative to interpret and visualize data expressions.

Related Degree Programs

Maybe you are still undecided if a data science degree is the right fit for you. We have extensive guides covering related programs that might better match your skills and career goals. Here are some of our most popular guides covering closely related degrees:

Related Programs Online

Similarly, perhaps you are interested in exploring related programs where instruction is offered 100% online. We have a collection of guides covering related online programs with comprehensive schools listings:

5 Great Reasons Why You Should Consider a Degree in Data Science

1. Job security: The future digital economy will be built by data. In the next five years alone, there will be job openings for almost 200,000 new data scientists according to a 2016 McKinsey Global Institute study.
2. Career mobility: Besides the projected hiring needs, data scientists will also find opportunities to move up to leadership positions, or find their skills in demand in across a number of different industries.
3. Technical toolbox: Data science, data engineering, business analytics and intelligence are all jobs that require a technical background. Those skills demand higher wages and are prone to more career opportunity. Technical skills can also be leveraged and prepare students for more traditional careers, such as teaching, law, or medicine.
4. Future proof: Data scientists enrolled in undergraduate and graduate programs are preparing for exciting opportunities that are not even well-defined yet. By preparing today they will get to build the future and become leaders in their fields tomorrow.
5. Big earnings: Last, but not least, according to Payscale.com (2017), a data scientist’s salary range is $62,714 – $142,361 with a median salary of $91,000.

Choosing the Right Data Science Program

Not all data science programs are created equal. A number of factors contribute to finding the right educational fit. The good news is that there are plenty of good options. Everyday, new data science bootcamps, certifications, undergraduate, and graduate programs are launching.

What to look for in a Data Science Program

1. Type of program: Data science programs range from weeks-long bootcamps to years-long Ph.D. programs. Bachelor Degrees and certificate programs for post-baccalaureate students are also becoming more common. Explore Data Science Programs by Degree Level
2. Accreditation: Finding out if an institution meets minimum standards is important while investigating different data science programs. Transferring credits is one of the biggest reasons why accreditation is significant.
3. Admission requirements: Many master’s programs require a GRE or GMAT score, in addition to transcripts, letters of recommendations, and personal statements. To learn more about the process of taking the GRE, check out this GRE Prep Guide.
4. Job placement/career services help: Having job prospects after completion of the degree is very important. Check to see if the school provides assistance with career placement.
5. Online versus campus: There are pros and cons for each method. Keep reading below for a breakdown.

Ready to apply. Here’s a quick checklist:

• Call the admission office and ask questions
• Observe a class
• Speak with a current student
• Talk to an alumni
• Look for career services and the job placement of graduates

Prerequisites and skills needed to pursue a Degree Program in Data Science

Each degree or program requires different prerequisites or skills. Some knowledge in the following areas is important:
Courses: statistics, computer science, linear algebra and calculus.
Skills: Computer programming proficiency and experience in one of the following: SQL, Python, R, or Hadoop.

Sample course overview

Each program will vary, but there are some similarities between data science programs. These courses are usually part of the core curriculum:
Foundations of software development
Software engineering
Database management, systems, and design
Foundations of data analysis
Data analytics and visualization
Data warehousing and business intelligence
Data mining and predictive analytics
Big data architecture
Big data management
Machine learning

Delivery Method – Online versus campus versus hybrid

Universities and students are both finding a lot of benefit from new online course platforms. Flexibility and cost are big factors. For students, online courses might be less disruptive to other commitments. Increasingly, online education programs fit into two categories: online and hybrid. It’s important to note that online programs might still require students to visit campus up to two times during the program. Hybrid programs offer a combination of online and campus instruction (in this case requiring a physical meeting more than two times). Many programs will require the student to attend certain classes or seminars during the program for completion. Remember to double-check and verify all the requirements of online and hybrid data science courses. The specific program structure is likely to vary from school to school.

Types of Online instruction:

Synchronous versus asynchronous instruction
• Synchronous instruction: Online courses with synchronous instruction require students to login and attend classes at specific times. Classes are conducted in real time. The students watch lectures through a live stream and can interact with teachers and other students. For courses that have synchronous instruction, a webcam is required. Typically, class times are set before the course starts much like attending class in person. Programs with synchronous instruction are ideal for students who want a classroom environment, but cannot attend on campus.
• Asynchronous instruction: Online courses with asynchronous instruction do not require students to attend classes in real time. Instead, students can login and view lectures and other class materials at times that are convenient for them. Classes with asynchronous instruction still have deadlines for assignments and tests that students must meet in order to pass. Asynchronous instruction is ideal for students who do not have set schedules and need the flexibility to view lecture materials at their convenience.

Fields that Use Data Science

Data scientists are highly sought after across a variety of industries.
• Ecommerce: Data scientists retarget campaigns, predict modeling and help interpret data generated from websites, all of which improve products and increase sales.
• Healthcare: Health informatics. Data scientists help physicians and hospital administrators effectively track drug trials, map human DNA, monitor patients remotely and predict the spread of disease.
• Business and finance: Data Scientists help analyze day-to-day transaction information as well as track inventory, monitor in-store traffic and optimize high frequency trading.
• Social networking: Online behavior is a goldmine for data scientists. Working with data generated from social networking sites, data scientists target advertising by location or personal preferences, and improve customer service.
• Science: Data scientists have more information than ever before, and they’re scrubbing, sorting and synthesizing it at a rapid pace. Nearly every field of science — genetics, biotechnology, particle physics, climatology, and chemistry – relies on large data sets and its analysis to advance its complex research.

Careers in Data Science

Data science programs are helping students train for the careers and opportunities of tomorrow. It’s likely that there will be even more opportunities as data becomes more and more integrated in business intelligence, analytics, and decision-making. Right now, there are many different professions open to a professional with a background in data. A data analyst takes data and uses it to help companies make better business decisions. A data scientist uses data analysis, explore and anticipate which problems and might recommend ways to solve them.

While a data scientist works with information to find patterns and observe behavior, a data engineer is often responsible for building the infrastructure that can capture, store, and access data. Big data requires processing power and massive computational capabilities. The trend towards gathering and synthesizing data will only continue, and data engineers will build the technology needed to make data a useful tool.