You’re in your twenties or early thirties, and every time you open Facebook, you see either an engagement announcement, wedding photos, or a life event involving babies. You start to wonder when this all started. You continue to wonder how many of these posts actually happen every year, how they change over the years, and whether is it mostly your male friends or your female friends, you’re sure you could find a way to mine your news feed history to plot some points. Basically, you are practising everything one needs on a great job profile.
If this kind of thought process sounds familiar to you, you could be perfect for a “Data Analyst” job role.
You could consider being a “Predictive Analyst”, if you’re interested in predicting how many wedding posts you’ll see in the next three years. Further, if you’re trying to figure out how to monetize this trend, you’re probably a prime candidate as a “Business Analyst”.
On the flip-side, if you’ve been casually mining data on twitter out of curiosity, say looking for patterns across events, hashtags, and geotags, you are better suited for a Data Scientist role. You’re probably good at spotting trends, asking questions about those trends and finding ways to answer those questions. While data analysts use data to answer existing questions, Data Scientists are skilled at mining data to help pose new questions or glean hitherto unknown insights.
Is it for you?
For both Analyst and Scientist roles, there’s a way to figure out if the job is for you and how easy or hard the transition will be.
When solving a problem (or in this case, choosing a career transition) you should always start by answering the question “Why?” before moving on to “How?” and then “What?”. If you’ve been thinking about a career change into Data Analysis, start with “Why?”. If data-driven decisions only satiate your curiosity or if your curiosity about the world comes from trends you observe, you’re on the right path. You’re the type of person that values facts and figures over opinion and deliberation. There are even online analytical aptitude tests you could try to make sure your decision is data-driven!
As for “How to be a Data Scientist?”, Data Scientists, Data Analysts, Business Analysts, and Predictive Analysts all need different proficiencies of a few skills including good knowledge of statistics, databases, programming, and machine learning. With the right tools in hand data scientists and analysts can sift through the noise of Big Data to draw conclusions, extract insights and make predictions.
Even if you don’t have a B.C.A.or B.Ss in Mathematics, you can start with Data Science course or practicing with data available for free online.
Once the “Why?” and “How?” are answered, the “What?” is just a matter of taking those classes, getting certified, and practicing. Easy or difficult will depend on the overlap between your interests and skill-set. Once you’ve found the sweet spot, all you need to do is narrowing down the types of roles and industry verticals of your interest, and start applying!