Data Analysis in the Real World

By: Nicole Luisi, MPH, MS

In movies and television, when a data analyst is assigned a task they spring to action, typing hundreds of words per minute without ever looking down from their wall of monitors — cut to them presenting the results, glossing over the details on how they scraped all the data from some website, performed a complex analysis, and solved a crime, all in about 60 seconds. Admittedly, it would make for some pretty boring television if they showed the 7 hours that person had to spend reformatting and cleaning a dataset, the 4 hours it took to resolve an error they encountered, or the 2 hours they spent staring at the screen trying to find the missing parenthesis that broke everything and caused them to question all of their life choices. Although we all envision ourselves solving the world’s problems with our fancy analyses, the reality is that real-world data can be messy, and we will probably spend 25% of our time on those fancy analyses and the other 75% on data cleaning and preparation.

In the classroom, we often use examples that demonstrate techniques (working with missing values, cleaning character data, etc.), but there is really no substitute for time and experience with real data. Real-world data is predictably unpredictable! Even the best systems can’t anticipate every issue that will occur during data collection, but it is safe to assume that you will encounter something you did not expect. Some things are just out of our control – your online survey platform could glitch and cause the skip patterns to fail, the intern you are training could accidentally enter data in the wrong table, a teenager might create a bunch of fake identities to repeatedly join your online study and scam you out of incentives, someone may even show up to enroll in person for your study and turn out to be 2 kids in a trench coat.    

So, what can aspiring data analysts do to prepare to work with messy, unpredictable data and stand out to potential employers?

• Get a solid foundation in the platform(s) you plan to use most. At Rollins we focus on SAS and R which are both widely used in epidemiology, but if you have a dream job in mind, find out what that organization prefers. It’s great to have some working knowledge of a variety of tools, but you want to get really good with one or two. If you like the structure of a class, you can consider formal classes and workshops offered by companies like SAS or R Studio, and there are a number of great online platforms that offer training as well (LinkedIn Learning, Coursera, DataCamp). There are also a lot of useful books out there with companion websites that provide datasets and practice exercises.
• Get some experience with real data. Again, you can only simulate so much in the classroom – working with real data (and its issues) will expose you to all kind of things. This experience might come through formal opportunities with employment, internships or volunteer work, through a thesis or practicum, or even on your own through the use of publicly available data. There is a ton of public health data available online (Census, NIH, CDC), and if you just want to play around and improve your skills, people have created all kinds of interesting datasets and made them available online (Github, Kaggle, FiveThirtyEight) – go ahead and download a dataset full of Netflix movie reviews, sportsball stats, Twitter posts with the latest controversial hashtag, anything that is interesting to you!
• Practice, practice, practice. Programming is otherwise known as…learning a programming language. Classroom training is only part of this – it can get you the foundation but to really excel you must put in the time. If you were trying to learn a new language, even after taking a traditional class, you might spend an hour a day on Duolingo, listen to songs in that language, or read books and articles in that language, looking up any words you don’t know along the way…it’s the same thing here. The more practice you get, especially with real data, the more you will have to draw from when you encounter something new. You don’t have to know everything (I certainly don’t), but you will get better at doing things from memory that you have done dozens of times, and you will also remember unique examples that forced you to learn something new. Much like reading will expand your vocabulary, practice will add techniques to your toolbox that you can adapt when faced with similar tasks later on.
• Focus on other related skills such as problem solving, communication, and critical thinking – it’s not ALL about programming. Even if you are starting out in a position where you don’t have a lot of input, you can still exercise these skills as an analyst. The best data analysts I have worked with are detail oriented people that take the time to ask questions (even of themselves) and carefully evaluate their own work. In some ways as an analyst it’s helpful to be a bit of a pessimist (at least that’s my excuse) – I spend a lot of my time anticipating things that could go wrong to prevent and identify data quality issues. As a hiring manager, I think programming and analysis skills are necessary, but I also think it is important to give equal weight to other skills like subject matter expertise, creativity, communication skills, attention to detail, etc. I would be more likely to hire a well-rounded person that has experience working on a real study, over someone with numerous technical skills and certifications on their resume that has only ever done this type of work in a classroom.

If you want to be a great analyst, in some ways you will always need to consider yourself a student – I learn something new every day doing this type of work. Spend some time early in your program figuring out what appeals to you most in terms of software and data and then get to it!  

Nicole Luisi (MPH, MS) is a Director of Data Analytics and Informatics Projects in the Rollins School of Public Health, and an instructor in the Department of Epidemiology. She also teaches several courses for the Executive MPH program and serves as the EMPH Applied Epidemiology thesis advisor. Featured here is her dog, Doug, doing some casual data analysis.

Featured Image by Sai Kiran Anagani on Unsplash

Is Research Epidemiology For You?

By: Kristina Lai, MPH

One of the things I love most about my career as an epidemiologist is just how flexible my skills are thanks to the broad applications of my training. An area that is less well known is the role that epidemiologists play in clinical research and academic medicine. Research epidemiologists can work in nearly any medical specialty. They may work only with specific physician researchers or on collaborative studies within a larger clinical research team. This type of position is distinct from hospital epidemiologists involved in infection prevention and control.

I graduated from RSPH in 2016 with an MPH in Global Epidemiology. I initially worked in infectious disease surveillance and control at a local health department in Atlanta until I decided to pivot to a position that would allow me to use more of my data analysis skills. Thanks to my network and connections from my former ADAP, I found a dynamic role as a Research Epidemiologist with the Aflac Cancer and Blood Disorders Center at Children’s Healthcare of Atlanta. While I am no longer in this role, the research experience I gained was instrumental in preparing me for my current PhD program and my future career.

Epidemiologists in a clinical research and health services environment use many skills including study design, data analysis, data manipulation, and informatics. In my specific role, I led the collection and management of the largest longitudinal database of pediatric sickle cell patients in the country and served as the primary analyst and statistician for research studies related to sickle cell disease to better understand utilization, clinical outcomes, and health disparities among our pediatric patients. I also consulted on clinical study design and analysis plans, and managed data requests for clinical trials, grants, research studies, and quality improvement assessments. I also helped with the submission of IRB protocols, data use agreements, and other regulatory approvals.

Here are a few of the unique benefits of working as an epidemiologist in medical research:

1. Because you work with physicians who may not have epidemiologic training, you are often the expert in study design and data analysis for your clinical team. For this reason, most epidemiologists will have at least a couple of years of work experience under their belts before taking on a role like this.
2. You get to learn about all kinds of new research and hospital data management. I never thought I would know so much about blood disorders, but now I feel honored to have contributed to this important work. I also had the opportunity to work with specialists in informatics, outcomes research, and partners at universities and health agencies across the country.
3. You get to work with primary data and electronic medical records. This may not seem like much of a benefit, but the amount of data available to us for retrospective studies can answer a wide range of research questions that are just not possible in most epidemiology roles. That means that your research can impact clinical operations and decision-making.

There are many ways to get involved in this type of work. The Emory Healthcare and Children’s Healthcare of Atlanta systems are great places to network and get research experience. You can start as a student through your practicum, REAL positions, or thesis by reaching out to faculty members or looking for posted positions. Many clinicians may have a research idea or even full datasets, but they often need help from someone with the bandwidth to analyze it. While all epidemiologists on my team had at least a couple of years of epidemiology experience, one great way to start is by working as a data manager or clinical research coordinator to familiarize yourself with the clinical research environment. So, if you’re naturally curious, interested in applying your epidemiology skills to a clinical environment, and looking to gain valuable research experience, a role as a research epidemiologist might be for you!

Kristina Lai, MPH is a PhD student in Epidemiology at the University of California, Davis and a data manager with the California Department of Public Health. Kristina is also the co-founder of Allen & Lai Consulting LLC. She has broad experience with public health surveillance, clinical research, and international nonprofits. Kristina’s current interests lie at the intersection of health equity, infectious disease, and climate change.

Featured Image by UX Indonesia on Unsplash