Why You Should Get
a PhD in Data Science

A Doctor of Philosophy (PhD) in Data Science will not pay off instantly. You will have to dedicate a few years to sharpening your skills and gaining more expertise in the field before being eligible for the highest-paying jobs.

If you believe in delayed gratification, then getting a PhD might be on the list of things you want to do. To aid in your consideration, you should be aware of the essential information provided in this guide about PhDs in Data Science.

As a graduate degree, a PhD in Data Science will involve research. You’ll need to be committed to working in the field, testing new methodologies, and experimenting with data science tools and technologies.

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Data science has a wide range of applications in which you can choose to specialize. Aside from the field of information technology, you can apply your expertise in the health and medical sciences, or you can work with mergers and acquisitions in business and finance. Below are some basic facts about data science according to research conducted by Stitch.

• 38% of data science professionals have a PhD.
• More than half of known data scientists are based in the US.
• In the last four years, data scientists have doubled in number.

A doctoral degree is the highest level of data science education you can pursue. Everything there is to know about data science, from the theoretical to the practical aspects, should be taught to you by the end of your program.

After earning your doctoral degree, you are expected to have a wide span of knowledge and a deep understanding of the field. Your expertise entitles you to academic freedom and an impressive salary.

Although a PhD takes years of time and research, you’ll have more freedom with your projects or research, whether in academia or industry. Since your PhD proves your expertise, you can be a leader in the field. What is more, a PhD program in data science leads to a higher salary than even the best data science master’s programs.

While job prospects for professionals with a PhD in Data Science are few, you can look at it as quality over quantity. According to the Bureau of Labor Statistics, the field of data science is projected to grow by 31 percent from 2019 to 2029.

You can take on full-time or part-time jobs relevant to your specialization in academia, the government, or the tech industry. In these kinds of jobs, you’ll be directly analyzing data for your research department, your bosses, or your clients. If you prefer to be the boss, you can become a consultant instead.

Earning a data science certification will give you a sense of accomplishment and fulfillment in both your personal and professional life. Certification validates your knowledge and expertise to potential employers and clients.

There is a long list of professional credentials available. All you need are the resources and the commitment. The certifications listed below range in cost from $99 to $850.

Fine-tuning your skills with a PhD means that you will be veering away from the topics and tasks less relevant to your degree. Because you will have specialized knowledge, your pool of potential jobs becomes smaller. Below are the roles that you can take on after getting your PhD. Job growth and employment figures are estimates based on available BLS data.

Graduating from an accredited school catches the attention of future employers. Aside from that, you can also other benefits such as financial aid opportunities. PhD in Data Science salaries are also higher when the degree comes from an accredited school.

The US Department of Education and the Council for Higher Education Accreditation award graduate school accreditations.

If the school you want to enroll in has national accreditation, it has met the quality criteria and other key national education standards.

In a nutshell, regional accreditation is like national accreditation, but for a smaller geographical area.

Institutional accreditation is a type of quality assurance for a higher learning institution.

The opposite of institutional accreditation, a specialized one is only applicable to a specific course or department, not the entire school.

Applying to a PhD program can be confusing. Some schools might require a higher level of education and more research experience. Others may only ask for one of these, but you might have to fulfill course prerequisites before you start the program. Whatever the case for your chosen school, below are the requirements for a typical PhD program.

Transcripts from previous schools are required for most PhD applications. Most programs only accept PhD candidates who already have a Bachelor’s Degree in Data Science, and some require a master’s degree as well. A few institutions waive degree requirements for students with significant experience in the field.

A letter of recommendation is a valuable testament to your competencies. If you are still in your internship or working at a company, do your best so that your supervisors can vouch for your abilities. You can also ask for letters of recommendation from professors in your previous degree program.

If there is a requirement that can make you rethink your choice to apply for a PhD, it would be the letter of intent. This is the document where you lay out why you want a PhD and why you will be a good fit for the program. The admissions committee will use this letter to determine how motivated you are and how likely you are to finish the program.

Most PhDs cost around $30,000 per year. If you are to study for four years, that would be $120,000. Although you are not going to pay the entire sum upfront, this is still a large amount of money.

Fortunately, there are plenty of financial aid opportunities for prospective PhD students, so it is unlikely that you will have to pay much out of pocket. Even without aid, you can save money on a PhD program by choosing a school in your own state.

A PhD fellowship is a type of award that students can use to fund their research. Usually, a fellowship includes a tuition waiver and a yearly stipend. A PhD fellowship will not require you to hold down a teaching or research assistantship. You’ll also have more freedom to pursue your own research interests.

A PhD candidate can also apply for a teaching or research assistantship to help finance their studies. These assistantships are a type of work-study program. The guidelines vary by university.

Student loans should be your last resort in financing your doctorate. While you won’t have to work while studying, you will owe a lot of money after you graduate.

Fully-funded programs are incredible options for PhD students. Not worrying about tuition and basic living expenses could definitely boost a person’s academic performance. Some fully-funded programs also offer a stipend and health insurance to keep students motivated to finish. Below are some fully-funded doctoral programs for you to check out.

• University of Southern California | PhD in Data Science and Operations
• University of Nevada, Reno | PhD in Statistics and Data Science
• Kennesaw State University | PhD in Analytics and Data Science

The workload for a PhD in Data Science is similar to other PhD programs. Since it can be overwhelming, we have divided the requirements into sections for ease of understanding.

Typical coursework for a PhD student consists of about 30 credits, which is about the same as a master’s program. The main difference between a master’s and a PhD is that the latter imposes additional requirements, which include qualifying exams and a dissertation.

To become a PhD candidate, you’ll need to take a certain number of qualifying exams. Each exam is a measure of your level of expertise in the field. If you pass your qualifying exams, you will be permitted to start your dissertation project.

There are different types of qualifying exams. An oral exam tests your understanding of the field and your ability to demonstrate your understanding to a panel of experts. Theoretical and practical exams will test your theoretical and hands-on expertise.

Some universities require their PhD students to log a specific number of apprenticeship hours in teaching or research. If your chosen program features this requirement, you may have to complete your hours before you start your dissertation.

A dissertation is a document of around 10,000 words that shows your expertise in your chosen specialization. At this point in your education, you should be able to contribute original research to the field of data science.

Before you begin your dissertation, you’ll need to complete your coursework. The number of core classes that you’ll need to take varies by university. Below are the topics that will fortify your fundamental knowledge of data science.

As a PhD candidate, you should already have an excellent grasp of the foundations of data science. But to mentally prepare students, an introduction to data science is almost always part of a graduate program. Some of the topics included in this course are data formats, wrangling exploration, visualization, statistical methods, and data governance.

Probability and statistics are broad topics with wide applications. To set boundaries, the professor will limit the material to data science and its applications. This course might be split into two separate classes in some universities.

Students learn about multiple and random variables and expectation convergences in the probability portion of this course. In the statistical portion, students learn about models, estimation, hypothesis testing, Bayesian methods, and linear and logistic regression.

Machine learning falls under the artificial intelligence (AI) umbrella. So, some core concepts of AI might also be included in this course. Along with machine learning, you’ll also learn about deep learning, neural networks, and k-nearest neighbors algorithms.

A course on big data is designed to help you deal with large datasets with ease. The major subtopics you should be taking notes on are relational databases, distributed storage, distributed computation, applications, and algorithms.

Pursuing a PhD in Data Science online is not as common as traditional education. However, there are some good online data science programs out there. Below are some of the benefits of getting your PhD online.

If you opt to get a PhD online, you’ll accumulate less student debt. You won’t have to pay for the university facilities and amenities. You can save money on lodging and transportation and devote more resources to your dissertation.

There will be more options for your classes if you enroll in an online program. You can attend courses in the evenings or on the weekends, whichever works for you.

Just because you are studying online doesn’t mean you won’t have financial assistance available to you. Fortunately, online PhD students can still apply for grants and loans to fund their studies.

You can get a decent data science education at either a traditional university or a bootcamp. Let’s take a look at both options so you can decide which one will work best for you.

Many universities offer Master’s Degrees and PhDs in Data Science. This option requires you to take classes for several years and conduct original research on a specific topic.

Coding bootcamps are relatively new in data science education. Although data science bootcamps are comparable with traditional schools in that they provide bachelor’s-level education through intensive classes and training, few of them are as exacting as a PhD program.

But the field of data science is changing rapidly, and not all PhD programs can provide master’s students with the industry-led training they crave. If you have a Master’s in Data Science and you’re looking for a more advanced bootcamp, try NYC Data Science Academy.

Now that you have an idea of what a PhD in Data Science can offer you in your professional journey, what you’ll want to know next are your program options. Some program options are solely for data science, while others are in combination with big data.

Your chosen program will dictate how many years you should dedicate to your PhD. To help you out with your decision-making, we have compiled a list of the top program options for data science PhD students.

In this program, techniques such as predictive and big data analytics will be your daily fare. The goal is to be able to implement these methods and apply new strategies for powering different industries in society.

If you are a person who uses statistical data and evidence to support your opinion, then this program is for you. Here you will formulate, model, analyze, and solve research problems using computational and statistical methodologies. If you want to make your quantitative skills more impactful in the field, find a PhD in Computational Science and Statistics.

This type of program is for professionals who want to be involved in developing new systems and algorithms for dealing with high dimension, high volume, and heterogeneous data streams. Usually, graduates of this program are employed in the life sciences or in health care.

For business-inclined professionals with backgrounds in data science, this PhD program might be for you. Students in this program develop high-level decision-making and data science technical skills to design business-appropriate analytical models.

Professional organizations will broaden your career network and create opportunities for improvement. One of the perks of belonging to a professional organization is getting updates on the newest developments and innovations in data science. They also host meetings, seminars, and other professional events where you can interact with fellow data scientists.

Below are the top data science professional organizations that you might want to get involved with. If you have a specific area of interest, you might want to join the pool of experts in that specialization.

This professional organization in data science emphasizes continuous learning. If you share the same sentiment, then there is no doubt that ADaSci is for you.

Becoming a member entitles you to free access to ADaSci’s Lattice Journal publications, as well as priority access to its conferences and continuing education programs. Even getting a job as a fresh graduate of a PhD program will come easy since you will have the first word on new job openings and internships.

Also known as the Big Tent of Statistics, ASA aims to promote the practice and profession of statistics. An ASA membership can give you more career and learning opportunities in statistics. You also have free access to ASA journals, the Significance magazine, and Amstat News to keep you updated on what’s going on in the field.

If you are a K-12 educator who is genuinely interested in statistics and can commit to a professional organization, your best option is ASA.

Handling large volumes of data might be daunting to some. If the volume, velocity, and variety of big data are already part of your daily activities, then you might want to join a professional organization that could help. DASCA believes that this generation is the era of big data. If you share the same belief, then this professional society is for you.

DASCA offers three credentials for three of the top professions in data science: Big Data Analytics, Big Data Engineering, and Data Scientifics. ADaSci has similar offerings, but DASCA offers a wider perspective on the field by incorporating relevant big data concepts.

There is also an organization for professionals in operations research and management science. INFORMS is the largest professional organization in the field and you can expect that your network will expand fastest here.

With 16 journals, 11 events, and 165 communities, INFORMS has over 12,500 members. For 25 years, the organization has helped decision-makers from different industries achieve the full potential of their organization or project.