What the Heck is Applied Data Science and Other Questions from an Applied Data Science Masters Student
There were several questions I Googled multiple times before applying to Claremont Graduate University for my Master’s Degree in Applied Data Science and International Science: “Applied Data Science”, “What is applied data analytics?”, “Analyzing data… that is applied?”, “If I have a sociology degree and I want to get a data analytics masters… is that okay?” I share these googled questions, not to sound like I have no clue what I got myself into, but to show that the emerging science of data analytics is confusing and complex. It’s okay to be confused with emerging fields in this world, but that shouldn’t stop you from entering them!
I first heard the term “applied data analytics” in a thesis meeting with my advisor, whose eyes lit up when I mentioned incorporating data into my sociology thesis. He asked, “Have you considered getting your masters in data analytics?” This question threw me completely off balance. Here I was in the beginning of my senior year, starting to field questions about my whereabouts after graduation and this advisor was asking me if I wanted to pursue a masters in the daunting and unfamiliar field of data science. My first thoughts were, “What does a Master’s in Science have to do with sociology? What is applied data science? Do I even qualify?” These were the questions I should have asked at the time… but did I?
You guessed it! Big fat nope. Hoping to impress my advisor, I decided to say I had heard of applied data science and knew a little, but I didn’t know if I qualified as a sociologist; requirements for my sociology degree included a couple research courses that incorporated data, but they were not the majority of the work I had done. My advisor explained sociology classes probably met the requirements of most programs, and while some require college calculus, others would be perfectly happy with my degree. This is when he told me “You go to grad school to learn. You should go into a master’s program with space to grow, so it’s okay you don’t have too much experience with data analytics.” For me, this was an extremely useful piece of advice that helped battle any doubt I had in my qualifications.
Once the meeting with my advisor finished, I immediately went to work googling, moving rapidly from simply typing in “applied data science” to complete sentences like “What exactly is applied data science, how does it differ from data science, and can I do all of this if I haven’t even taken any coding classes yet?”. In hindsight I should have just asked my advisor; he probably would have answered my questions better than Google.
One of the most basic questions I needed to answer was “What is the difference between data science and applied data analytics?” This question comes up because the three have a lot of overlap. When googling “data analytics”, “data science”, or “applied data analytics”, any of the three topics could pop up regardless, making it easy to conclude that there is no difference between the three. However there is a slight difference, and with enough combinations of “data”, “analytics”, and “applied”, I finally found a fair enough description of the three I’d like to share (maybe it will save you some googling). Keep in mind these are rough definitions, which will likely adapt or evolve over time.
Data Science- The overall term for the group of fields that mine large data sets to extract knowledge and insights
Data Analytics- Utilizing large data sets to identify trends and develop visual representations of data so it is more easily digestible
Applied Data Analytics (Science)- Using data from real world issues and applying data analytics to create suggestions, solutions, policy recommendations, etc.
My own definition of Applied Data Analytics- The statistical analysis and visual representation of data used to explain or predict social, political, and economic phenomenon.
These definitions do overlap a lot, but I don’t see an issue with using them interchangeably because often enough I’m asked to explain what my degree means anyway, so the label becomes less relevant. However, if you are in a situation where labels are important (think job interviews and school applications) the simplest way for me to understand these definitions is that data science is an overarching umbrella under which data analytics and applied data analytics occur.
Because applied data science is a newer degree, its definition is weak at best; it grows constantly with the advancement of the field and technological growth. I ultimately decided after weeks of research that I would apply to one program at the Claremont Graduate University and see what happens. I made this decision partly because I didn’t know what I wanted to do when I graduated and partly because the program I chose had a specific emphasis on international relations (a cool mix of the familiar and the unfamiliar).
During the months I waited to hear back from the program I started to see more and more of how much data we use in sociology (and in politics, my minor). I began to get excited and I realized that data allows us to learn and observe the intersectionality of different areas of study. This is what makes applied data science so cool: it can be adapted across multiple areas of study, bursting doors wide open for whatever you want to delve into. Data is so universal, the only requirement for choosing an applied data science research topic is whether we can collect data on it. If the answer is yes, then you’re probably good to go!
Now after a year and a half in my program, as far as I have come with the question of what is data analysis, there is still so much I don’t know. The field is evolving. Statistical programs and computational power is expanding. And although the number of new programs I have learned has exceeded the number of semesters it has taken me to learn them (4), I still learn of new statistical programs each time I enter the classroom. There is so much to learn, but being in a field that is developing as you are learning has been an unexpectedly exhilarating journey.
