The trade-off for time commitment and high costs provide networking opportunities, becoming a specialist in a certain segment of big data, and being a solid candidate for high-level executive opportunities. It is recommended to find some form of funding for a PhD through fellowships and assistantships, which can take at least four years to complete.
Consider the Doctor of Data Science in addition to the PhD in Data Science - although they are different, there are similarities as both are terminal degrees in the Data Science program area.
Online Doctor of Philosophy Programs
Northcentral University Doctor of Philosophy in Data Science
This fully online university provides a fully online PhD program within data science with a one-on-one education model, meaning that each student is taught individually by a professor. There are 20 total courses needed to complete the program, with an approximate time of completion at just over four years. Its curriculum offers advanced education within the topics of big data integration, business intelligence, data visualization, and strategy and theory of data. The degree meets the regional accreditation requirements of the WASC (Western Association of Schools and Colleges) Senior College and University Commission.
Example courses in the curriculum include Inferential Statistics and Predictive Analytics, Multivariate Analysis, and Experiential Methods. Enrollment into the program takes place each Monday throughout the year. There is two ways to enter into the 60-credit PhD program. One is a previously completed Masterâ€™s program that is in a relevant field. If the Masterâ€™s degree is in a different subject, the transcript must be evaluated by Northcentral and students will need to take any prerequisite courses before starting the core curriculum.
Featured Online Data Science Programs
Grand Canyon University Doctor of Business Administration - Data Analytics
The College of Doctoral Studies offers the DBA online through Grand Canyon University, which requires 60 credits to complete. Each course takes eight weeks to complete, either in a virtual setting or with weekly night classes. While most of the courses are available online, there are at least two five-day residencies that need to be completed online, including dissertations and an oral presentation that includes an interactive Q&A with the classroom. Up to nine doctoral credits, equivalent to three courses, can be used in transfer credits.
Some of the classes that students will take are Analytic Foundations for Business Leaders, Emerging Issues in Financial Management, The Sustainable Futures (focuses on business analytics with an emphasis on preserving the environment for moral and competitive purposes), and Enterprise Data Complexity. Not only will the curriculum provide a path into being a high-level data scientist, but graduates can become a business intelligence director, strategic innovation manager, and chief executive officer.
Colorado Technical University Doctor of Computer Science - Big Data Analytics
This PhD with an emphasis in big data and analytical studies will prepare students for using tools such as Hadoop and XML and analyzing massive amounts of unstructured and structured data for any level business. Course examples include Topics in Database Systems and Big Data Analytics, Qualitative and Quantitative Research Methods, and Futuring and Innovation. The curriculum includes eight dissertations and research projects, and along with the core curriculum, requires 100 credit hours to complete.
Various start times will be announced throughout the year for admission. Requirements to get into the program include completing an application online, submitting an official transcript, and meeting with a representative from the doctoral program to review information and discuss the academic plan. The fully online university is accredited by the Higher Learning Commission, and the business programs are accredited by the Accreditation Council for Business Schools and Programs (ACBSP).
Indiana University - Bloomington PhD Minor in Data Science
An alternative PhD that takes less time to complete is a minor in Data Science through the Libby School of Informatics, Computing, and Engineering. The curriculum will consist of any four courses that are offered in the Master of Science in Data Science program, and that is either online or on-campus options. In order to complete the PhD minor, all courses will need to be passed with at least a "B" grade. For those interested in similar, full PhD programs, Indiana University offers subjects in Computer Science, Statistical Science, Informatics, and more.
Most of the traditional courses found in the PhD curriculum will be similar to those offered in a Master’s degree program. However, the additional years required will consist of various research projects and writing reports.
- Business Analytics
- Business Intelligence
- Topics in Big Data
- Quantitative and Qualitative Research Methods
- Predictive Modeling for Business Decisions
- Inference and Representation
- Evaluation of Information Sources and Services
Strategical courses will provide the skills needed to work with other organization employees and looking over current operations and making adjustments to implement a more optimized approach or improved software and hardware in the future. This can also be expanded to new locations or completely overhauling a current established system. These courses will also take a look at different strategies that have been successful or have failed in the past.
Quantitative methods have students analyze and interpret statistical analysis through the use of mathematics, sampling, theories, and hypothesis. These methods focus on the collection of data and the creation of models that depict the data that is found. This is in contrast to qualitative methods, which attempts to find the reasoning behind why the data is represented or trending a certain way.
Statistical inference is connecting the dots and forming a hypothesis on properties of a population. If the course is under the generic name, studies will consist of developing theories and generating randomized samples to come toward a conclusion. Bayesian inference, or Bayesian probability, is a subjective inference to data. This is the method of determining statistical evidence on a subjective belief, such as the potential reaction of customers when it comes to an organization merging or buying out a different company.
One of the most common requirements in a PhD program is completing dissertations, which are a written summary of a student’s research project. Some programs will require multiple dissertations on a variety of topics to fully showcase their knowledge and skills in certain subjects and situations. Some universities will label dissertations as a thesis, but the main difference between the two is a thesis is typically completed at the end of a Master’s in Data Science program while dissertations are developed throughout the doctoral program. These projects tend to happen frequently in any curriculum under a PhD program and is part of the reason it takes up to six years to complete.
How to Begin a Career in Data Science
At minimum, it is recommended that those pursuing a degree in data science hold a Bachelor’s degree in data science or the field of computer science, mathematics, or statistics. These will typically take four years to complete. Master’s degrees are geared toward working professionals to advance their career, and online programs can provide flexibility for those to complete coursework at their own pace while meeting assignment deadlines. These can be completed within a couple years for students that can commit to a full-time schedule, but part-time students will generally take at least three years.
Just as important as the education are the skills required to complete big data tasks. An education will help prepare students on what to expect, but they must acquire the ability to analyze data. This includes critical thinking, researching, manipulating data, mining, testing theories, and ensuring the accuracy of the data. Knowing various programming and software tools is very important. Python, R, SQL, Tableau, and Excel are some of the more popular options out there. Knowing one or more of these tools will at least demonstrate hard skills to the employer, even if they use other tool sets.
More specific to the modeling, analysts must be able to spot trends and variations, being able to connect the information together. This provides an opportunity to create data visualizations and be able to communicate this information to other non-technical parties. As one would expect, having another soft skill, such as communication, is very important.
Not only do scientists need to present information, but they must be able to work with other members on the team. They may have to work with engineers to help build and maintain the architecture, or to other analysts in order to work with unstructured data and to fill in any potential gaps with their findings. Communication is certainly key if the scientist’s role is more administrative or managerial when guiding a team in the information technology sector.
For those that do not have the time or resources to commit to a full-time higher education program, there is the ability to gain certification. These programs offer a truncated curriculum that frequently have a few core courses based on the subject and electives to choose from. These are typically four to five courses long and can be completed within a year. For example, Indiana University-Bloomington has a Graduate Certificate in Data Science that requires 12 credit hours to complete, and it can be done in as little as two semesters completely online.
Important Organizations and Associations
There are a number of great organizations to join within the data science community. One of the largest at around 13,000 members is INFORMS, a group that publishes academic journals on subjects within data science, member magazines that educates members with the latest innovations in analytics and software, and there are various meetups for professionals and students to learn and network with others. There are mentoring opportunities for members to advance in their education or careers, and they can also gain an edge through the Certified Analytics Professional and Career Center programs offered by the organization.
The Data Science Association has a mission to increase diversity and improve ethics within the professional data science landscape. Generally, there is an annual conference that focuses on a pure experience instead of being filled with vendors and recruiters, giving people an opportunity to learn about any of data science, from data mining and processing to deep analytics and complex machine learning. The home website offers many links to resources such as news, academic papers, coursework, and various upcoming events within big data.
Senior Data Scientist
Senior positions for data scientists will ultimately have the most responsibility among a team that is analyzing information. Not only are they processing information, but they are looking for context behind it and observing other sources that may have impacted or manipulated data. At times, they may have managerial or director duties in order to help get a project completed. Obtaining a PhD will create separation among others looking for senior positions as the curriculum forces graduates to have thorough knowledge on the topics they specialize in and showcases their ability to complete projects over a span of approximately five years.
Director of Research
Oversees the organization’s research and development practices and ensures that things are running effectively. There are many duties that this position has, including dictating roles for team members, reports to higher-level executives, creates strategies for the company to explore, keeps up with the latest trends and innovations and develops any plans to implement them into the company’s processes. At least five years of professional experience is recommended along with very good knowledge of the specific department they work for.
Chief Data Officer
Executive position that differs from the Chief Digital Officer and has emerged with the ongoing development of big data, managing the data sources and analysis techniques that the company will ultimately use. They are typically responsible for developing new strategies for the company within the field of data science and can share similar duties or be an alternative to the Chief Strategy Officer. CDOs have become more common in the financial industry after the 2008 recession as more companies needed to become more transparent and utilize their data more accurately. According to PayScale, the average salary for a CDO across the United States is $181,851 per year.