Choosing a Quantitative vs. Qualitative Data Analytics Degree
Start your GCU
journey today.
journey today.
Read time 5 minutes
Published on Mar 21, 2022
Read time 5 minutes
Published on Mar 21, 2022
We're here to help.
Speak with a University Counselor today.
The views and opinions expressed in this article are those of the author’s and do not necessarily reflect the official policy or position of Grand Canyon University. Any sources cited were accurate as of the publish date.
The field of data analytics can be categorized into two main types: quantitative vs. qualitative. Both have the same goal: to inform business decisions and drive growth.
However, there are significant differences between quantitative vs. qualitative data analytics. When working with quantitative data, analytics professionals are focused entirely on numbers. For example, they may be analyzing airline pricing data or perhaps looking at how the pandemic has affected the number of people traveling on vacation as compared to business travel. Quantitative data is any information involving numbers.
In contrast, qualitative data refers to descriptive, non-statistical data that lacks numerical value. Qualitative research helps to explain individual experiences and perceptions. For example, while quantitative research may report on the number of people traveling on vacation during the pandemic, quality research may explore the individual user experience.
Regardless of whether you choose to earn your degree in quantitative or qualitative data analysis, it’s very likely that you’ll find your investment in your education was well worth it. The impact on your career is likely to be significant. After all, doctoral degree holders are generally well qualified for their jobs. You also may be positioned to pursue high-level executive roles within your current company or your dream company.
Additionally, there are personal rewards to consider. When you earn a doctoral degree, you will be well recognized by your peers as a leader in your field, because writing a dissertation requires you to contribute to the body of knowledge in data analytics.
Beyond acquiring a deeper understanding of data analytics, your doctoral degree program will empower you to develop transferable skills. Graduates are recognized as having advanced critical thinking, analytical reasoning, decision-making and problem-solving abilities. This makes them extremely valuable as employees.
The College of Doctoral Studies at Grand Canyon University is proud to provide an academically rigorous and supportive environment to further the success of students. You can choose from a range of doctoral degree programs, including the Doctor of Business Administration: Data Analytics (Qualitative Research) and the Doctor of Business Administration: Data Analytics (Quantitative Research) programs. To begin planning your academic journey at GCU, click on Request Info at the top of your screen.
1Retrieved from IBM, The Quant Crunch: How the Demand for Data Science Skills Is Disrupting the Job Market in September 2021
Data analytics specialists who have been working in the field for a few years may begin thinking about returning to school to acquire new skills and enhance their opportunities for promotion.
According to a 2017 study, only 6% of entry-level data analysts were required to hold a master’s or doctoral degree. However, that statistic rises dramatically to 11% for managers in data analytics and to 39% for advanced analysts and other high-level data scientists. Clearly, an advanced data analytics degree is prized by employers looking for high-level data scientists.1
Once you’ve made the decision to return to school and earn a doctoral degree, you will then need to decide exactly what type of degree you’re going to earn. There are significant differences between quantitative vs. qualitative data analytics, as well as between quantitative vs. qualitative degrees. Explore this guide to learn about these differences and for help choosing the degree that best fits your goals.
Another way to reflect upon your choice of data analytics degree is to consider the typical data collection methods used for quantitative vs. qualitative data analytics. Before you can write your dissertation, you will need to perform original research and collect data.
In some cases, these types of data rely upon different approaches to data collection. Most research methods can be applicable to either quantitative or qualitative data collection.
Qualitative data collection methods include the following:
Quantitative data collection methods include the following:
Other methods of data collection, however, can be used for collecting either type of data. The difference often lies in the questions you will ask. Qualitative interviews tend to be somewhat unstructured and may lead to spontaneous follow-up questions. In contrast, quantitative interviews are highly structured and seek only to elicit numerical information.
The emerging field of data analytics is centered on the analysis of raw data. Data analytics blends three diverse fields: statistics, business and information technology (IT). This multidisciplinary field can be applicable to any industry, although you will most commonly find data scientists and analysts working in the tech and finance sectors.
The data analytics field has become increasingly important in the modern business world as well because all companies need to gain a competitive edge in their respective industries. A data analyst’s findings can be influential in making decisions that shape the direction of a company, such as by determining which new products to launch or how to set prices for them.
When you are deciding whether to pursue a quantitative vs. qualitative data analytics degree, one of the most important factors to consider is the goal of your research, or what issue you are trying to better understand. For example, if you are currently in the finance field, you may want to choose a quantitative data analytics degree if your goal is to report on the pace of decline in consumer spending during a certain period in time. However, if your goal is to better understand why consumer spending is down, or how investors are reacting to this change, a qualitative data analytics degree may be a better choice.
If you’re still not sure how to go about choosing a degree after considering your career plans, the next step is to consider the dissertation itself and your own preferences. Writing a dissertation is a significant investment of time; you’ll be spending years thinking about your topic, researching, performing data analyses and writing the final paper. Because of this significant time investment, it’s critically important to choose a topic that can hold your interest over a long period.
Some graduate students find that they would much rather devote a substantial period of time to working with numbers. Others decide that qualitative data analytics is their primary passion. Because you may very well end up working with both types of data in your career, your choice of degree and dissertation could simply boil down to your personal preference.
