So, You Want a Career in Data Analytics?
A few weeks ago I was asked to speak to students at my graduate school about my career in data analytics – shout out to Professor Sera Linardi and the University of Pittsburgh’s Graduate School of Public and International Affairs for their invitation! – I quickly realized I had never intentionally entered the data analytics field. How was I going to talk to a room full of new graduate students? Because I believe in the importance of data – and I wish someone had told me why I should take as many “hard skills” courses as possible during my studies.
What is Data Analytics, and Why Does it Matter?
Data Analyst. Data Scientist. Descriptive Analytics. Prescriptive Analytics. Predictive Analytics. What do all these terms mean – and why are they essential in the field of data analytics? All of these are important terms with their own definitions. For this blog post – and for simplicity – I’m only going to define data analytics. Data analytics is the process (automated or manual) of analyzing raw information to draw conclusions and make decisions.
Data analytics is not the same as data reporting. Presenting facts and figures does not make great analysis. However, great data – and knowing how to tell the good data from the bad – are a necessary part of great analytics.
Step 1: Develop Strong Math Skills For me, this was the hard part. I had a math tutor throughout high school, and I barely passed my college economics courses. However, by the time I entered graduate school and my brain finished developing (experts say this happens around age 25), I found a renewed sense of focus. Where in high school, I struggled, now I found success in advanced coursework such as statistics (quant I and II), economics (micro, macro, behavioral, development, and more), and mathematical theory/logic (game theory).
Step 2: Analytical Skills Matter, Too Math skills aren’t enough. If you want to get into data analytics, you need to know how to apply these skills to problem solve. Think about this example: your company is losing money, your boss wants to know why. You could answer, “because sales are down.” Or, you could dig into the data, analyze it, determine why sales are down and recommend a solution. Which approach adds more value? Rhetorical question.
Step 3: Computer Science is the Future I never took a coding or programming course. I wish I did, it would have made me so much more marketable, and it would have made repetitive tasks so much easier. Take Python, R, SQL – anything. I name those three specific languages because they are used a lot in data analytics. But the language itself doesn’t matter. What matters is learning how to do it and how to think like a programmer. From writing a few simple lines of code to clean a dataset to building a custom algorithm, programming is an essential skill for anyone in the field of data analytics. If coding isn’t a skill you can learn, make it your mission to gain business intelligence software platforms like Tableau or PowerBI. Become a power-user on a CRM platform like Salesforce, or learn how to incorporate geographic information system (GIS) skills into your analysis. These skills will help differentiate you from every other data analyst applying for a job.
Step 4: Learn how to Visualize Information Data visualization is an essential skill in data analytics. Learning how to pick the right chart for the right audience is just as important as learning how to implement user-centered design principles. Taking a key performance indicator (KPI) from a metric tracked in a database to a visual on a dashboard makes it more useful and actionable. One of my favorite data visualization blogs is Ann K. Emery’s Depict Data Studio blog – they have a fantastic interactive chart chooser that can help any beginner (or seasoned data analytics professional looking for something new) select the right chart for their project.
# Tips for Future Data Analytics Professionals
Network. Go to the events hosted by your graduate school. Talk to the speakers and ask questions. Send follow-up emails. Ask to meet for coffee. You never know when a job opportunity will become available.
Tell a story. Don’t just report facts, explain what matters and why. Whether you’re writing a paper for a professor, preparing an analysis for your manager, or submitting a proposal to win a client contract, you’ll do better by learning how to tell an impactful story.
Closing
A data analytics career isn’t for everyone. Data analytics is about knowing how to ask the right questions and trusting your training and instincts to make the right decisions. Before any analysis, I ask myself one question: “how can I use the data I have to make my client succeed?”
According to Jeff Barrett in his 2018 Inc.com article, up to 73% of company data goes unused for analytics. Data collected without analysis doesn’t add any value. Anyone entering the data analytics sector should know that their number one job is to add value to their team, company, or client.
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