Data Science Degree or Data Science Training: Which Should You Do?
Deciding how to develop professionally in the field of data science can be tricky. So many paths exist for so many levels of education and experience that the process of choosing how best to advance can feel overwhelming. As the CEO of Edlitera, an instructor-led data science training program that offers technical professional development courses, Claudia Virlanuta understands the challenge that many face when deciding how to enter the industry.
Centrally, many are unsure which is the right option between getting a data science degree, attending an instructor-led training program, or completing a bootcamp. Virlanuta told Discover Data Science, “This is often the first question someone asks themselves when they decide to pursue a career in data science: How do I go from here to there? Do I need to go back to school, or can I learn the skills I need in some other way?” Here, we break down the advantages and drawbacks of going back to school versus pursuing instructor-led training.
An Overview of Data Science Degree Options
There are data science degree programs at every academic level – from introductory associate degrees to terminal doctorates. Because of the different kinds of degrees available in the field, it’s important to parse out just how long, what benefits, what modes of learning, and how credible each of the degrees can be toward your personal and professional goals.
Associate Degrees in Data Science
- Typically 30~60 credits
- High school diploma or equivalent required for admission
- More than 40 schools offering the major in data science
Online data science and data analytics associate degrees are growing in popularity across the country. As more schools realize the importance of data analytics across sectors and purposes, more universities have decided to begin offering introductory data science associate degrees. These are designed to give students both some entry-level courses in data science mixed in with other general education requirements. These kinds of degrees are best suited for professionals who have an inkling of an interest in data science and have no prior technical knowledge.
Topics you can expect to explore include:
- Introduction to Database Programming
- Analyzing Data Sets
- Introduction to Data Visualization
- Introduction to Programming
- Applied Predictive Modeling
An important note about getting an associate degree in data science: most career opportunities will require that candidates have a bachelor’s or master’s degree or equivalent work experience. So, while an associate degree in data science may be a good introduction into the field and into the modes of thinking required for success in the industry, it’s important to understand that more training, more work experience, or more education will likely be necessary.
The cost of getting an associate degree in data science really depends on the college or university. For example, data science programs offered at community colleges will likely cost less than a degree option hosted by a private university. According to CollegeBoard.org, students can expect to spend approximately $3,800 on getting an associate degree.
Bachelor’s Degrees in Data Science
- Can often be completed in under 4 years
- Typically 120 total credits
- Full-time and part-time options available
An online bachelor’s degree in data science is an excellent preparatory method for those interested in pursuing an entry-level data science role. Because most positions on the market will require a bachelor’s or master’s degree to be considered for hire, an undergraduate degree is probably the best way to gain a generalized, high-level understanding of the field.
Subjects covered in a typical bachelor’s degree in data science include:
- Introduction to Advanced Data Structures
- Machine Learning
- Object-Oriented Programming
- Data Systems and Mining
- Probability and Statistics Modeling
Because the balance between theory and practice is explored in a bachelor’s program in data science, you will have the opportunity to gain both technical and soft skills over the course of the degree. Exposure to data science programming languages, structures, frameworks, and practices will prepare you to enter the field after graduation. Additionally, the introduction to independent research will give you a competitive edge as you seek to pursue career options or further your education with an advanced degree.
Still, there are limitations to getting only a bachelor’s degree in data science. While the general nature of the degree is great for gaining a strong entry into the field, most leadership positions will require greater specialized knowledge, an advanced degree, or a certification in some aspect of data science. To navigate this drawback, it’s recommended that those who set out to get a bachelor’s in data science set clear career goals answering these questions: What do you want to do in data science? In what part of the field would you like to concentrate? If you seek leadership positions, do you need to attend an instructor-led course in a specialization or get a master’s degree? After spending some time with these questions, which are best to explore at the start of your career, you’ll have a clearer picture of which path you’ll need to take.
As we consider the cost of your educational investment, the average four-year in-state yearly tuition at a public university is $10,740, according to CollegeBoard.org. This figure grows to $27,560 for out-of-state students. Private universities will typically charge more, sometimes exceeding $50,000 a year at the most competitive schools.
Master’s Degrees in Data Science
- Can be completed in 18 months to 3 years
- Immersive internship opportunities
- Concentration options
For those who want to pursue more competitive leadership opportunities in the industry, a master’s degree in data science is the perfect way to convince a prospective employer that your technical knowledge is advanced enough to make major organizational decisions and lead teams of data science professionals. Because they typically take approximately 18 months to three years to finish, a graduate-level data science degree will enable you to explore specific aspects of the field more closely.
It’s important to note that students in these programs will learn the nuances of programming languages that include Python, R, SQL, and Tableau through primarily project-based coursework. Through these programming languages, you will be able to engage in course topics that cover:
- Database Systems and Algorithmic Modeling
- Applied Statistics
- Advanced Machine Learning
- Data Governance and Policy
- Data Visualization
While some rare bachelor’s degrees in data science offer concentration options, master’s programs much more frequently offer students the opportunity to specialize in a facet of the discipline. Some popular data science degree specializations include:
- Modeling: Data science professionals who aim to focus on how best to build predictive, forecasted models should choose a specialization in data visualization and modeling. Organizations across industries rely on data science professionals who can develop data-informed forecasts through applied statistics, data analysis, and database programming.
- Business Analytics: Companies have started to employ large numbers of data science professionals to help grow their business and cut costs. As a result, many data science master’s degrees offer a concentration in business analytics to prepare students to use data to build models that offer data-driven solutions.
- Data Engineering: The data engineering specialization that some schools offer is best for systems-based problem solvers. Centrally, the data engineering specialization prepares leaders to manage teams as they handle massive quantities and sets of data.
- Machine Learning: Sometimes referred to as artificial intelligence, the machine learning specialization focuses on how data science professionals can innovate on data-adaptive modeling. Courses typically cover an advanced exploration of language processing, the software of robotics, and algorithmic forecasting.
- Data Analysis Management: While master’s degrees in data science are excellent for students who want to develop advanced technical and programming skills for success in the field, there may be a lack of focus on developing leadership tools. With a data analysis management concentration, you will gain a deeper understanding of what it takes to lead teams toward achieving organizational goals.
U.S. News and World Report has found that a master’s degree in computer science, which is a discipline and field deeply connected to data science, costs anywhere in the range between $15,000 and $72,000. This range breaks down to $500 to $2,000 per credit. Importantly, many universities and colleges that offer a master’s in data science degree also provide students with scholarship opportunities. From merit- and need-based scholarships to teaching and research graduate assistantships, there are ways to offset the initial investment of a degree.
Solving for an alternate perspective, master’s degrees may not be for everyone, considering the coursework explored in these programs can be highly conceptual and abstract. These environments may not support the learning habits of students who wish to enter the industry more quickly and begin making career advances immediately. Many graduates with just a bachelor’s degree can find desirable positions in the industry equipped solely with their undergraduate degree and substantial work experience. An additional means of progress as a professional in one’s field can be achieved through specialized data science training.
Doctoral Programs in Data Science
- Independent research development
- Approximately 70 credits
- Qualifying examinations and dissertation research typically required
As explored in the Discover Data Science guide to getting a PhD in data science, this advanced degree track can be exceedingly difficult for potentially unexpected reasons. Data science is a constantly evolving field, where many employers seek candidates who can demonstrate an up-to-date, industry-informed knowledge base in order to predict future trends. Even though getting a doctorate in data science signifies the apex of academic development in the field, the credential doesn’t necessarily prove that a candidate is knowledgeable of cutting-edge technologies. Instead, many organizations in both private and public sectors will value concentrated data science training on specific subjects over a terminal degree.
Though the doctorate in data science may be a more challenging route for entering advanced positions in the field, it will prepare you for otherwise unobtainable research positions, particularly in academia. For example, most university and college employers will require their faculty to leverage both their experience and their academic training as they conduct independent research and teach future data science professionals. In this capacity, a PhD in data science is perfect (and most often required) for those who wish to enter new academic environments and fields to introduce appropriate data collection and analysis practices.
A doctorate in data science will typically cost between $1,300 and $2,000 per credit. Because most programs require students to complete approximately 70 credits to complete coursework, this figure can be a high-cost investment, but also increases ROI exponentially. To offset the steep costs associated with getting this degree, some schools have fully funded PhD programs that support graduate students during their study. These schools include:
- Worcester Polytechnic Institute
- University of Southern California
- University of Nevada, Reno
- University of Maryland
- Kennesaw State University
Each of these schools offers funding opportunities in teaching and research assistantships and fellowships.
Instructor-Led Data Science Training Programs vs. Bootcamps
An important distinction between instructor-led data science training courses and bootcamps can be found in how each facilitates learning. Just as they both primarily focus on training students to learn a new tool or skill in the field, they also diverge in how they promote student learning.
For the most part, data science bootcamps sell their services based on guided, but mostly independent learning exercises. In the data science field, this means students are tasked with learning new materials through pre-recorded lessons, coursework assignments, and group projects. While this setting may be great for collaboration between classmates and for people new to the field looking to make a career transition, it may not be the best learning exercise for everyone.
On the other hand, instructor-led data science training programs can be viewed as having clearer direction than their bootcamp counterpart. As a program that also welcomes new professionals aiming to make a major career transition, instructor-led programs are typically project-based as well but are paced to allow students to grasp industry-best practices in a more guided, hands-on way. Because an instructor is continuously present throughout the delivery of a course, students have greater access to an industry expert able to offer more nuanced, practical instruction.
The Value of Targeted, Instructor-Led Data Science Training
From Edlitera, Virlanuta has seen first-hand the advantages of instructor-led data science training. Of this approach she states, “Instructor-led courses are a good way to design a flexible curriculum to help you target specific skill gaps and learn new skills under the guidance of an expert instructor, and with the benefit of direct feedback on your work and someone to go to when you’re stuck.”
“For instance,” she continued, “a data analyst who is quite comfortable with SQL might look into a short Data Processing with Python course, or a Machine Learning course in order to take their skills to the next level, understand if data science is for them, and even start seeing opportunities at work for using their new skills (which would help them develop a portfolio of data science projects, which in turn would help them stand out when applying for a Junior Data Scientist role).”
Concentrated, instructor-led data science programs promote a central focus on teaching a specific skill in an efficient way. In other words, instructor-led courses are immersive, interactive, and engaging. Instead of functioning primarily as a component of teaching yourself, where you may be tasked with simply watching a video or reading content, instructor-led courses give a clear vision of the work and its practical outcomes.
Additionally, instructor-led courses in data science are excellent for people who are trying to figure out how to enter a new professional setting. This can be an unnerving process, especially when considering how many career opportunities there are in the field of data science. Instructor-led courses can help inform students on what aspects of data science they may find most appealing and can speak to their professional interests. While this practice certainly requires self-discipline and independent learning, targeted instructor-led courses can help facilitate a smooth transition into a new professional chapter.
Instructor-led courses are also appropriate for candidates who aim to continue professionally developing. There are situations that arise where training may be used to fine tune skills from a previous, semi-related career, including military experience. In these instances, instructor-led data science training courses can prepare you to advance further in your career.
When a Data Science Degree Is Necessary
While instructor-led courses can be excellent professional development and career transition tools, there are some career opportunities that have more strenuous requirements. Specifically, some careers in data science require specialized, sometimes advanced degrees in the field. Some of these positions include:
- Data Mining Specialist
- Machine Learning Engineer
- Database Developer
- Data Architect
- Data Scientist
In practically all these positions, either a bachelor’s degree or a more specialized data science degree is a minimum requirement for employers. It makes sense, considering many of these positions need professionals who can balance technical skills with verbal and written communication skills. These tools are typically developed in the process of earning one’s degree.
Apart from these career options, there are also instances where transitioning into a data science career would require a degree. For example, a candidate who has a degree in the Humanities (like English, History, or Studio Art) will have a much harder time competing for a data science position than a candidate who has a degree in a science- or mathematics-related discipline. As a result, it’s a good idea for those aiming to make a major career transition like that to consider going back to school. Additionally, getting a data science degree may be the best option for someone who possesses an undergraduate degree in the field but aims to advance their career. In this capacity, a higher degree would likely be a stronger credential than an instructor-led course certification.
Popular Data Science Training Areas
While data science degrees at every level can help you learn essential and foundational programming skills, instructor-led data science training programs and seminars can provide further insight into different aspects of the field. The focus of these kinds of sessions are typically more technical and provide students the opportunity to adopt new programming skills. Specifically, training programs cover topics that can include:
- Python programming
- Python languages/packages used in data science
- How to use Jupyter Notebook
- SQL training and applications
- Data stream processing
Many of these courses have different tiers and levels to accommodate students who enter with different levels of experience. From beginner to advanced, these instructor-led courses ultimately will provide students the opportunity to learn new skills or develop more fully the ones they already have.
How Training Can Supplement a Degree
It’s no secret at this point how effective it is to bolster the knowledge gained from getting a degree with an instructor-led training seminar. Again, Virlanuta chalks up the massive room for growth and continual development as an overall benefit of data science. “Data science is a very vast field,” she told Discover Data Science. “One can spend many years studying a very small part of data science theory in depth or spend months learning in broad strokes the tools and skills you need to access an entry-level role in the industry.”
From this logic, it’s understandable that a degree can give you more credibility in the job market and a heightened theoretical understanding. At the same time, that degree may be limited and may not be able to give you the hands-on, real-world, practical skills that you’ll need more immediately.
“At Edlitera,” Virlanuta stated, “we teach hands-on data science and machine learning skills to analysts and engineers at companies all over the world. It is not uncommon for participants in our courses and workshops to have advanced degrees in STEM fields. They are experts in their field, and they can use their expertise as well as the new machine learning and data science skills they are learning to work on projects using real world data and having real world implications for their employer.”
As the field of data science continues to grow, new and emergent skills will be required for data science professionals to stay ahead of the curve. And while those skills may not originally have been part of your degree program, there are several ways to continue evolving and adapting as a data science innovator.
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