What Is a Bachelor's Degree in Data Science?
Data science programs combine training disciplines in statistics and mathematics with computer science. Students gain the skills to help businesses, governments, and other organizations develop strategies and make informed decisions. As the data available to industries and organizations increases, so does demand for data science specialists with the skills to utilize that information. As a result, schools continue developing programs to meet this need, training students to use data effectively and responsibly.
Data science is one of the nation's fastest-growing industries. According to the U.S. Bureau of Labor Statistics (BLS), mathematicians and statisticians represent two of the top 20 occupations in terms of highest projected employment growth from 2018-2028. These professionals also earn median annual wages exceeding most others in that group. For students considering degrees in data science, the following information examines the field in detail. Read on to uncover degree and career options, requirements, projections, and application considerations.
Should I Get a Bachelor's in Data Science?
Earning a bachelor's degree in data science can lead to a fulfilling, rewarding career. The following list outlines some of the major benefits available to most data science professionals.
- Career Growth: According to BLS data, occupations for mathematicians and statisticians should grow by 30% from 2018-2028 -- much faster than the national average growth rate for all occupations.
- Salary: Salary potential varies by career, but several data science careers feature annual median wages near or exceeding $100,000, according to the BLS. In fact, the lowest 10% of earners in the mathematical sciences field still earn nearly $20,000 above the national average salary.
- Job Diversity: After graduation, data science students can find work in a variety of industries, including healthcare, education, government, technical services, and research and development.
- Career Advancement: The data science field allows for considerable growth and professional development after graduation. Professionals may pursue master's degrees or certifications to advance or diversify their careers.
- Multidisciplinary Tasks: Though it may depend on their career or industry, many data science professionals enjoy diverse day-to-day tasks. They may work with computer data, design surveys, solve mathematical problems, develop applications to analyze data, or interpret the data in other ways.
Advice from a Data Science Graduate
Alejo Pijuan works as a data scientist at Nike. He has lived in Buenos Aires, Argentina; Mexico City, Mexico; Lima, Peru; and New York City. He has also worked in San Francisco, California, and Portland, Oregon. He graduated with degrees in computer science and mathematics from SUNY Plattsburgh in 2018.
After graduating, he moved to San Francisco to find a job in the tech hub of the world. This led to a data scientist position at Divercity, a growth-stage startup focused on helping candidates from diverse backgrounds land jobs at diversity-minded companies. He then transformed that role into a blend of data science and product management. He attributes his current position at Nike to continuous self-education and consistent personal development.
Why did you decide to pursue a bachelor's in data science?
I am fortunate to have always known what my passion is: numbers. I started out as a mathematics major. A year into it, I got introduced to the world of computer science through a required intro to CS class and knew where I wanted to apply my passion for problem-solving, logic, and numbers. I added it as my second major sophomore year. What really called to me was the possibility of building with code whatever I could imagine.
It wasn't until my senior year that data science became the clear choice of career I wanted to build for myself. What did it for me was knowing that I wasn't bound to jobs in one industry or a particular type of role. With the same skill set, I can explore new roles in new industries all throughout my career. In fact, my end goal is building businesses, and figured that data science would be the perfect career to adapt to the new age of business: the age of data.
Tip #1: If you don't yet know what your burning passion is, that's truly just fine. Don't force it. But do follow your curiosity. If your curiosity is nudging you towards data science, try that. Pay attention to when your brain gets curious or asks, "I wonder how..." or "what if it was done this way?" This is not only a valuable habit for finding things that make you happy but also a great way to find your niche in data science. I got curious about languages a few years ago; now, my favorite niche is natural language processing.
What was the deciding factor in selecting your specific program and specific school?
I created my own data science "program," informally, through personal projects during my senior year. I had the support and experience from my professors in both the CS and math departments. It wasn't a breeze by any means. It was hard, confusing, and disorienting the whole time, but I kept pushing forward and learned when to ask for help.
I was fortunate to have my older brother to introduce me to my alma mater. The State University of New York (SUNY) at Plattsburgh has an amazing international students community. Feeling welcome was a big part of the decision. I could never repay the support that I got throughout and after my bachelor's at Plattsburgh, especially through times of uncertainty, like we are living right now.
Tip #2: Look for and ask people who are doing or have finished the specific program/school you're interested in. Who better to ask than someone with a personal experience of how the program/school influenced their success?
How did your degree program prepare you for your current career?
It's important to understand that there are no one or two classes that prepare you to be a data scientist. You will not remember most of the information you consume or tests you take. Those are just part of the process, not the builders of your success. I would say skill development has been the principal influencer of my career. Skills go beyond just information. Information itself is worthless, so are you studying to pass the final exam, or connecting the information to a broader network of knowledge, which then results in you passing the exam?
That's roughly how your brain learns best. Did you ever wonder why learning a second programming language was so much easier than learning the first one? Even if the second one was C++, you already had foundations and a network of knowledge to connect the new information to. This takes time and practice. And then more time and more practice. It is not easy, it is not quick.
One of the most rewarding moments of my bachelor's was observing my brain being able to connect linear algebra class to gradient descent, understanding that optimization didn't just "pop up." I'm grateful to my professors and mentors for helping me understand that making the process of learning more efficient is orders of magnitude more important than specifically what I am learning, and in turn, that leads to transforming that information into knowledge.
Tip #3: Don't just learn to build a model from scratch; learn to observe what mistakes you make and skills you lack, and improve upon those. The best model you've ever built is simply the result of that focus and the skills you develop.
Why did you choose to pursue additional certifications? How did your bachelor's degree set you up for those courses?
The raw truth is that college itself doesn't make you employable. And the lack of experience on your resume surely doesn't help. Additional projects/certifications are not optional any more. My degrees helped me immensely with setting up a structure of self-study, resourcefulness, and asking for help when I got stuck.
Certifications have two amazing advantages over university: You get to choose the project/certification, and there are literally thousands of cost-free options. I personally stand behind fast.ai and recommend it to anyone in any level of data science expertise (only requirement is some working knowledge of Python). Jeremy Howard has the gift of very smoothly connecting old concepts to new information. If you're intrigued, complete lesson one and decide whether you want to do the rest of the lessons after you finish.
It will truly help in developing the skills you need to become more employable. Give it a chance; I promise you won't be disappointed.
Tip #4: Medium, YouTube, and Stack Overflow are your best friends, so make sure you know them well, ask them questions, find their interests, let them connect you to new people...and they might just help you land a job.
the job search like after graduating with your bachelor's in data science?
For me, it felt like graduating was the start of the race, not the finish. I took a couple of weeks to set up and then moved to San Francisco to find a job. My job search looked a lot like: search -> apply -> message the recruiter -> do certification -> work on project. The break statement is commented out until you accept an offer (and not necessarily the first one).
But why would they hire you fresh out of school? As much effort as you put into that final project your last semester, you must be currently working on a project when you step into any interview. Find time in your schedule to work on a project. Organization is key. Have a schedule you consistently open and update (no, your memory is not good enough).
Start a certification. Your resume must have at least one "in progress" certification/course you are actively working on.
Tip #5: In interviews, show you understand what they do (you can't fake this; you have to do research). What problem is the company trying to solve? For example, if you are interviewing at Facebook and get asked "why do you want to work here?", don't answer the same thing they've heard a thousand times. Your answer should show that you've done your research and studied their competitors.
If you did, you'd know that the lifeblood of Facebook is advertising. Something along the lines of "given Facebook's stake in advertising, and seeing that X company is your biggest competitor, I find Facebook's biggest advantages to take over the ad market to be A, B, and C. I think I can bring Y and Z to the table to help make that happen." What impression do you think you'll leave on the other person? Do make sure your answer is aligned to the job you're applying to.
Tip #6: Follow this guide.
What are some of the most rewarding aspects of working in data science? Some of the most challenging aspects?
Personally, the most rewarding part of data science is successfully translating a project's process and results to nontechnical stakeholders or business directors. There's something very special in transforming all the hours of code, debugging, researching, hitting your head against the wall, and the few "aha" moments, into a puzzle in which you're helping the other person connect the dots. I'd say it's also the most challenging aspect. It can be frustrating, but don't get discouraged.
Tip #7: Remember to probe your audience for their understanding, be patient, ask for feedback, and work on making your presentation/translation skills a little better every time.
What advice would you give to students considering pursuing a bachelor's in data science?
It is a vast field that grows at a faster rate every single day, so try new algorithms, applications and projects. Be curious.
School work can be a lot, so make sure you maintain or find new hobbies. Always having your brain on the same frequency is a great way to create the opposite of happiness.
Be resourceful: professors, books, Reddit, Medium, YouTube, Stack Overflow, Quora, research papers, GitHub for open source projects, and your peers.
Tip #8: Be patient with yourself. We tend to overestimate what we can accomplish in a day but wildly underestimate what we can accomplish in a year.
Admission Requirements for a Bachelor's Degree in Data Science
Each school features its own unique admission requirements for undergraduate students. Some schools impose rather flexible requirements, admitting applicants with only high school diplomas or GEDs. In academically challenging programs like data science, however, schools often require more from candidates. For example, many set a minimum 3.0 GPA for recent high school graduates in addition to high SAT or ACT score requirements. Applicants usually need to submit professional recommendations, as well.
To ensure that applicants can successfully complete their data science training, some programs only admit those with backgrounds in mathematics and sciences. Candidates can demonstrate this through high school prerequisites, meeting specific grade requirements, or completing undergraduate courses in the area before declaring a major. Applicants with relevant professional experience may receive preference, as well. For more application information and assistance, Common App provides a thorough guide for the application process.
What Can I Do With a Bachelor's Degree in Data Science?
After earning a bachelor's degree in data science, graduates can choose to enter the workforce or continue their education into more advanced degrees or certifications. The following information explores these pathways in more detail, outlining career and educational outlooks and options for graduates.
Career and Salary Outlook for Data Science Graduates
Data science graduates enjoy a variety of promising careers with strong projected growth, high salaries, and robust advancement opportunities. Depending on their chosen pathways and interests, students can enter a wide range of math-focused and computer science professions. Due to the discipline's highly skilled nature, data science careers typically offer salaries exceeding the average for all occupations. The median annual wage for computer scientists, for example, is double the average for all occupations.
In addition to high wages, these professions typically offer exceptional growth in terms of projected employment and individual career advancement. Some available positions offer similar growth to the average career, but mathematical sciences professionals can expect their occupations to grow at more than five times the national average.
To acquire more advanced, higher-paying jobs, learners can pursue further degrees and certifications. The following list examines some specific career pathways available to data science graduates.
- Mathematicians and Statisticians
- Mathematicians and statisticians work in a variety of industries, using mathematics and data to solve practical problems. They create and employ various methods for collecting and analyzing data. They help governments, organizations, and industries use this information to improve operations and outcomes.
- Computer and Information Research Scientist
- These professionals work with computers and data systems to improve their capabilities and operations. They assess and evaluate problems or shortcomings in technology or within organizations, and they develop methods for arriving at solutions. These scientists may work with organizational data or create the algorithms and software to help others work with such data.
- Actuaries use data to determine risk and the financial costs associated with them. They often work in the insurance industry, financial industry, or within organizations, utilizing the data available to them to help their employers more accurately assess risk and make informed decisions.
- Financial Analyst
- Financial analysts use data to help people and organizations make better financial decisions. These professionals observe trends and current information to evaluate investment opportunities and determine how various market factors might influence finances in the future. Analysts may also use their skills to evaluate and help improve operational efficiencies.
- Entry-level economists typically work within the government, researching and evaluating data relating to resources, goods, and services. They explore market trends and use their analytical skills to find solutions for economic problems. They may focus on state or federal governments, economy, employment, or productivity.
|Job Title||Entry Level (0-12 Months)||Early Career (1-4 Years)||Mid-career (5-9 Years)||Experienced (10-19 Years)|
|Computer and Information Scientists||$101,000||$103,000||$118,000||$149,000|
Continuing Education in Data Science
Data science professionals can continue bolstering their education long after graduation. They can pursue advanced degrees, like a master's, which may lead to higher-paying career opportunities. A Ph.D. program can take graduates in other directions, qualifying them for positions in academia, research, or upper management. Professionals can also explore certifications, which can help them advance their careers without obtaining a new degree.
- Master's: Master's degrees in data science prepare graduates for many of the same careers as bachelor's degrees, but often with access to higher positions, higher pay, and more responsibility. These degrees typically run for two years and enable learners to develop career specialties. For admission, most applicants need bachelor's degrees in related fields. Some professional experience may be necessary.
- Ph.D.: A Ph.D. in data science may prepare students for similar careers as a master's degree, but graduates usually lean toward research and academic professions. These programs last 5-7 years, and learners usually develop their own specializations. For admission into Ph.D. programs, applicants often need master's degrees in related disciplines. They may need professional experience as well.
- Graduate Certificate in Data Science: Data science graduate certificates can provide students with the skills and training to further develop their careers and expertise in a relatively short period of time. Professionals can seek out certifications focused on specific areas of the field. Employers may consider graduate certificates when hiring for more advanced positions.
Earning Your Bachelor's Degree in Data Science
Depending on the school, every bachelor's degree in data science varies. In most cases, schools offer BS degrees in the field, though some BA degrees exist. A BS focuses more on math and science training, while a BA focuses on the humanities. Most undergraduate programs require full-time students to complete approximately 120 credits over four years, usually taking 15 credits in the fall and spring semesters. Learners can slow down with part-time study or accelerate with summer courses.
Enrollees can also pursue online degrees. These programs often allow learners to study asynchronously or in a self-paced format, which can considerably accelerate degree length. Some programs also encourage students to choose specializations, which can help them fine-tune their training and chart unique education and career paths. Read through the following information to learn more about your study options.
Comparing Bachelor's Degree Options
Aspiring data science learners can access a variety of degree types. While data science remains at the core of all programs, the complementary courses, general education, and electives may change by degree type. Degree type can influence future study opportunities, as well. Therefore, students with specific master's degrees in mind might consider choosing similar bachelor's degrees. The following list details three of the most common bachelor's degrees in data science.
- BS in Computer Science - Data Science
- Some of the best computer science programs include a data science focus or concentration, as the interdisciplinary approach provides students with the foundational technological skills necessary for a variety of data science approaches and applications. The computer science degree may also allow students to access computer science-related master's programs more easily.
- BS in Data Science
- A BS in data science provides students with a math- and science-focused general education and data science concentration. The general education courses complement the major by providing foundational knowledge and skills. In terms of further education, a BS offers an excellent pathway into master's programs, as the MS in data science represents the most common option available.
- BA in Data Science
- The most uncommon option, a BA in data science provides students with an interdisciplinary curriculum. With foundational training in the humanities, learners may obtain a diverse knowledge base and skill set. BA degrees tend to offer more electives and flexibility, which can lead to more customization and control.
Popular Bachelor's Degree in Data Science Courses
The individual courses in a bachelor's degree in data science vary considerably by program and school. Depending on program goals or faculty specialties, students may enjoy a variety of courses. Learners can further influence their studies through electives and capstone choices, often choosing between theses or final projects. However, some data science courses tend to appear in most programs to offer fundamental training.
The following list highlights some of those programs, though course titles and details may vary between programs.
In this core course, students learn to organize and analyze big data. They use predictive modelling and cutting-edge analytical tools to help organizations make better operational decisions. In this introductory course, training typically focuses on data distribution and common applications, whereas advanced or concentrated versions look at more specific applications, such as business or financial forecasting.
Data visualization courses teach students to take data from its source into modeling and processing, and then into mapped visualizations. The training equips students with the skills to present their findings in relatable ways, teaching them how to use some of the most common visualization tools.
This course trains students to understand and use relational database management systems. Enrollees often learn relational algebra and structured query languages for programming and database creation. In addition to working with the latest database applications, some courses may also delve into security and authorization procedures.
In this course, students learn to program with object-oriented languages, starting with the principles and syntax and moving into the creation of object-oriented applications. Learners explore how to use programming languages to develop basic algorithms and solve problems.
Data mining courses teach students to find patterns and regularities within various databases. They learn the techniques and applications required to interact with databases, along with methods for cluster analysis. Additionally, some courses delve into techniques for developing forecasts and predictions with the findings.
Pairing Internships With Your Education
While not always available, internships provide students with practical training and outlets for learning application. They can also help students identify their career interests by providing intimate insight into the field and day-to-day tasks. Some programs place great value in internships and make them mandatory for students, specifically in their senior year.
In most cases, internships offer college credit and can significantly improve students' career prospects, particularly when they provide relevant industry experience.
Selecting Your Bachelor's Degree in Data Science Program
The right school and program for each student depends on their values. Once learners determine which programs best suit them, they should identify whether they want to study on campus or online. Prospective students should always seek programs with regional accreditation, which assures that an institution meets certain educational standards. Candidates may also consider school size, student-to-teacher ratios, and department faculty and their specializations.
Candidates should carefully consider their program options, finding ones offering concentrations or courses in their specific areas of interest. They should think about each program's composition and length, as well. Admission requirements and standards may also factor into applicants' decisions. Furthermore, aspiring students should weigh program costs and the availability of financial aid before deciding.
In addition to the above components, each student should consider how their decision might impact their career, including each program's extracurricular offerings and career support services. Each school's reputation might influence graduates' employment opportunities, and training could impact earning potential. Learners might also examine each school's alumni network and resources to determine whether it provides the support they need.
Should You Get Your Bachelor's Degree in Data Science Online?
Online programs have changed the university search process. Learners no longer need to limit their search to local schools, because they can complete degrees online from a distance, often with no campus visits. Furthermore, the focus on independent study in data science lends itself to asynchronous delivery, one of the major benefits of online study.
However, online program options create additional considerations for potential enrollees. Learners must identify whether independent online study suits their learning style. Success in online programs requires personal motivation and drive, which on-campus students may not need. Moreover, online degrees may not offer the same level of interactivity as traditional programs. While online degrees provide a flexible, convenient approach to education, students should consider how the format might impact their learning process.
Accreditation for Data Science Schools and Programs
Accreditation plays an important role in choosing a school or program. Accreditation offers a quality assurance marker to students, employers, and other schools, and it often dictates whether students can receive federal funding or transfer credits. Above all else, students should look for regional accreditation. The Council for Higher Education Accreditation acknowledges seven agencies across the country that set quality standards and accredit the schools in their regions that meet minimum standards.
Data science students should also consider programmatic accreditation. The data science field does not require programmatic accreditation, but the Accreditation Board for Engineering and Technology (ABET) offers programmatic accreditation in STEM fields. Specifically, the Computing Accrediting Commission within ABET accredits data science and related programs. Students can use the Database of Accredited Postsecondary Institutions and Programs to research each program's accreditation status.
Professional Organizations for Data Science
Professional organizations provide great resources for students and professionals in a variety of fields, including data science. Learners can use the following organizations to stay current with industry news; to access resources; or to network, train, and find professional development opportunities. The list below highlights some of the best industry organizations for data science students and professionals.
- Association of Data Scientists This association aims to promote a set of industry standards and best practices. It encourages and supports the education of aspiring professionals in the field. It also offers continuing education, leadership training, and career support.
- Data Science Association This association lays out the standards for ethical practice in the data science field and seeks to improve the profession. Members gain access to a large professional network, industry information, and conference benefits.
- Association for Information Science and Technology This association strives to promote the interdisciplinary fields of information science and technology, shaping and setting professional standards and practices. Additionally, the association offers professional support and various career opportunities.
Scholarships for Bachelor's Degree Programs in Data Science
When seeking a bachelor's degree, financial aid plays an essential role for students. Learners in most disciplines can access federal aid, and the data science field offers access to a variety of program-specific scholarships, as well. The following list outlines some of the best scholarships available.
Remote DBA Experts to Students Scholarship
Who Can Apply: In an effort to promote the pursuit of data-focused careers, this scholarship recognizes students with winning essays on data and technology topics. Eligible applicants must possess a minimum 3.0 high school GPA for graduating seniors or a 2.5 college GPA for students already enrolled in a program.
UNCF/Alliance Data Scholarship and Internship Program
Who Can Apply: This scholarship recognizes minority college students pursuing a variety of degrees, including data science. Eligible applicants attend an accredited institution at the sophomore level and boast a minimum 3.0 GPA. The foundation prefers students at historically black colleges and universities.
Amount: Up to $5,000
Who Can Apply: Created to promote STEM education and bolster the Department of Defense (DoD) applicant pool, this scholarship rewards students pursuing degrees in relevant fields. Additionally, winners receive guaranteed civilian employment within the DoD.
Women Techmakers Scholars Program
Who Can Apply: Designed to encourage gender equality in the field, this scholarship awards women in computer science-related fields who demonstrate community leadership and academic excellence.
Betty Stevens Frecknall Scholarship
Who Can Apply: In support of students pursuing higher education in computer sciences, this scholarship awards applicants with a minimum 3.0 GPA within computer science or related fields.
Frequently Asked Questions
- What should I major in to be a data scientist?
- Data science professionals come from a variety of majors, including data science, computer science, and information systems programs.
- What undergraduate degree is best for data scientists?
- Depending on their interests, students might find data science or computer science degrees best for them. The former offers more focused data science training, whereas the latter combines this training with technology applications.
- What is a BS in data science?
- A BS in data science prepares students for entry-level careers in the field, equipping them with the skills to identify patterns in data and use it for projections, forecasting, and predictions.
- Is data science a good major?
- Data science degrees provide graduates with access to fast-growing, varied, and financially rewarding careers. Students may find the programs challenging, but data science majors enjoy great demand in a variety of industries.