A PhD in data analytics prepares professionals to work in data-driven fields, including research, business, healthcare, and government.
The most common reason people pursue a PhD in a data-related field is that they are passionate about data and would like to have a career that involves research and making discoveries, usually within a sub-field.
Data analytics PhD programs allow students to get an in-depth knowledge of research methods and topics they will use throughout their careers. Like other research-oriented doctoral degrees, a PhD in analytics is most often pursued by people interested in academic careers.
Although, increasingly, data analytics PhDs are also employed by technology companies to help improve products, services, or business opportunities.
If you are curious to know more about research in the field of data and analytics, these research interests can be divided majorly into two different areas — methods and applications.
Applications of a PhD in Analytics
Examples of research that improves methods and techniques of data utilization
Method-based data analytics PhD research focuses on gaining a deeper understanding of algorithms used in analytics. This method of research can involve any of the following:
- Research involving understanding algorithms has led to tremendous growth in analytical tools with improved deep-learning performances on large-scale data.
- Researchers have also been investing their time in understanding methods to collect data with a low signal-to-noise ratio, working with incomplete data, or generating synthetic data to understand natural phenomena where data is not readily available or rare. Few others involve researching methods of combining data from sources that aren’t of the same type, e.g., voice data with self-reported psychiatric questionnaires to understand mood and emotions.
- As people and organizations are highly aware of how crucial data can be, there have been increasing reports of data thefts and fraud, which leaves vulnerable people at a loss. One area of research crucial in the data world is ethics and data privacy.
- With the explosive growth of data, ongoing research has made tremendous growth in developing storage systems to improve data availability with consistency in real-time analysis.
Examples of research that utilize data-related techniques to improve or create applications in a given field
Another common data analytics PhD research area involves understanding how other scientists, researchers, and practitioners apply data analytics to other fields. These areas of applications range widely, not just limited to finance or medicine but also “social good” projects.
Examples of research in social good projects solve specific crisis-related challenges, such as responses to natural and human-made disasters in search and rescue missions and the outbreak of disease. Other examples include using analytics to solve environmental challenges, education, criminal justice, etc.
PhD in Data Analytics Curriculum
A PhD in data analytics has an intensive academic workload, generally completed between four and five years. Since the data industry has emerged only in the last decade, institutions that provide Ph.D. solely in data analytics are hard to find. Data analytics-related specialization is tied to either STEM or business-related research programs.
Components of PhD in Data Analytics
Here is a general overview of the requirements that are needed to complete this degree program:
Credit Requirements
Every PhD program has requirements to complete a certain amount of credits. These credits could be related to foundational or advanced-level qualitative and quantitative methods in statistics.
Based on your interest and flexibility in the program, the institution may offer you an option of cognate courses. The course curriculum is similar to the master’s-level program with few additions of research-related classes.
Pre-Candidacy Research Projects
The first one or two years in the program prepare you for admission to candidacy by working on research projects. These research projects also help you develop the skills necessary to frame questions and solve real-world data problems.
Preliminary or Qualifying Examination
Every PhD program requires its students to go through a qualifying exam. These exams test their skills to meet candidacy requirements. These pre-candidacy exams assist in fulfilling the requirement of having the theoretical and practical knowledge needed to work on your research project.
Teaching Requirement
Almost all PhD programs require the students to teach undergraduate-level courses or assist the professor in their teaching classes. These opportunities and experiences prepare you for an academic career.
Dissertation Proposal
The dissertation proposal contains the hypothesis of your research that should meet the standards of publications in data analytics. The proposal needs to be approved by the committee of faculty members before any proceedings to work on it.
Successful Dissertation Defense
Students are expected to present their original work on the dissertation proposal. They are expected to be experts in their data-related dissertation topic and defend their analysis. This is an important aspect of your PhD in analytics as it signifies that the student has successfully grasped all the necessary skills required to conduct their own independent research post-degree completion.
Optional Requirements
A Ph.D. is not just about taking credits and completing qualifying exams. During this program, there are many opportunities that a student is likely to benefit from. Attending data analytics conferences and getting internships during school breaks help students exchange research knowledge and form social connections necessary for job search.
Since the data field evolves at a much faster rate, it keeps students abreast of the latest trends in the data industry. Conferences are likely to provide students with discounted academic prices to attend them. Online platforms like Kaggle give opportunities to network, form teams, and participate in online challenges to showcase your skills.
Some institutions can provide you with a data analytics master’s degree if you can complete more than two years of your program but cannot continue further.
PhD in Data Analytics Online
There are many online educational opportunities available, especially in higher education. Like a PhD in data analytics online, online degrees offer a wide range of flexibility in terms of timing, workflow, and geographic location.
Leading universities offer programs that can bring the best of their faculty research to the masses. Many great data analytics master’s programs are now entirely online.
But, there are fewer than 100 percent PhD in data analytics programs online (although more are being created and launched to meet the uptick in demand and because educational formats are changing rapidly).
One of the main reasons doctoral programs are still taught mainly in traditional settings is that they require much collaborative research. Most data analytics PhD programs also require some teaching component, which is not primarily handled in person.
But the world is changing fast, and colleges and universities are adapting quickly to both the needs of students and the needs of an evolving workforce. So stay tuned, and keep track of updates to your favorite data analytics programs. Be sure to ask about remote or online options and possibilities when contacting traditional in-person programs.
PhD in Big Data Analytics
Big data is a term that was popularized in the last decade and refers to the classification and organization of massive data sets.
The reason experts or PhDs can wrangle big data is that the world continues to produce new data at an exponential rate.
By way of illustration, consider this statistic about creating new data; according to the site Statista, “The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 64.2 zettabytes in 2020.
Over the next five years up to 2025, global data creation is projected to grow to more than 180 zettabytes. In 2020, the amount of data created and replicated reached a new high.” For reference, the amount of data collected worldwide in 2010 was estimated to be two zettabytes.
There is a lot of enthusiasm about the trends and patterns found within massive data sets. Researchers in healthcare and agriculture are working with big data to find answers to questions ranging from cancer outcomes to crop outputs.
Given the new research opportunities made possible by big data, it makes sense that a specialty Ph.D. in big data analytics is emerging in university analytics graduate programs.
Career Paths for Data Analytics PhDs
As we mentioned initially, there are many potential career pathways for data analytics PhDs. This kind of degree often has a home in academia, but businesses and organizations are increasingly looking for researchers and practitioners of data analytics.
Academic Positions
- Postdoctoral Researcher/Research Fellow
- Appointed to the research staff, whose primary goals are to extend their education and experience. Although they hold a doctoral degree, they are not considered independent researchers and cannot serve as principal investigators. Some teaching duties may also be required. Positions are often for a fixed term ranging from six months to three years.
- Average Salary: $85,959
- Assistant Professor/Professor
- Typically the first step to tenure and conducting independent research. Once they complete tenure, they may be given the title of a professor. The tenure track is often a long journey of evaluating an associate professor’s publications, research, and teaching. The tenure track lasts somewhere between five to seven years.
- Average Salary: $80,057
Industry Positions
- Data Scientist
- By wrangling with data to develop meaningful insights, data scientists help organizations find and solve problems related to products or services. Combining computer science, statistics, and business knowledge, data scientists assist organizations in making objective decisions using data-driven strategies.
- Average Salary: $122,738
- Research Scientist/Quantitative Researcher
- Unlike data scientists or data engineers, research scientists don’t work on product development. Instead, they design and conduct experiments by developing hypotheses and measuring the outcome of their experiments.
- Average Salary: $119,165
- Chief Analytics Officer
- A chief analytics officer leads an organization’s data analytics strategy, driving data-related business changes and working with data scientists in developing data-related products.
- Average Salary: $151,203
FAQs About Data Analytics PhD Programs
Many top-tier universities require professors, researchers, and principal investigators to have a doctoral degree. A PhD is relevant if you are looking for a career in academia. However, it is not necessary to have a PhD to gain entry into data analytics unless you are looking into specific research roles in the industry. There is a minimal difference in the salary outcome of an individual getting a PhD versus someone who has a master’s degree in analytics.
If the institution cannot fund your PhD program, checking out external funding sources and scholarships before admission is highly recommended.
Most institutions need you to have a bachelor’s degree in a quantitative field. Work experience may also be preferred by not necessary. Strong research interest is recommended to gain admission.
Since PhD degree programs are research-oriented, an applicant’s GPA does play an essential role in the admissions process. Some universities have a minimum GPA cutoff, while others request that applicants complete undergraduate-level mathematics and statistics courses with a minimum grade.
This question is tricky to answer. Some universities encourage getting in touch with the professor to see if they are open to admitting new PhD students for the upcoming academic year. Other university programs clearly state that contacting professors during the admissions process is unnecessary. You can still express your desire to work with a specific professor in your statement of purpose during the application process if contacting professors directly is not allowed.