🤔 But first, what's "data"?
✨ "Data analyst" defined
❓Why do data analysts exist?
🔍 What do data analysts do?
🆚 Data analysts vs. data scientists
2. Where can I find internships?
In a gist, data is any information that you can use to answer a question.
Picture this: You're hungry and thinking, "What to eat for dinner?" That's your question.
You now look for answers by searching for nearby restaurants, checking out ratings, reviews, and prices, and maybe even scrolling through some photos. Without realizing it, you're swimming in data!
So every time you scan the weather to pick your outfit or choose the quickest route to a destination based on traffic updates – that's data in action!
A data analyst is anyone who uses large amounts of data to help businesses answer questions. For example:
Data analysts exist because businesses these days are flooded with data—customer details, sales stats, market trends, you name it.
But that data is worthless unless someone goes in and makes sense of it. It's like having treasure maps with no one to read them.
Data analysts are the ones who transform raw data into valuable insights that can give businesses a competitive edge.
Data teams are given a question like "Why are our sales dipping in the summer?" or "What feature do our app users love the most?"
They'll then dive into mountains of raw information, which can be anything from survey responses to sales numbers. Their job is to tidy up this information, sort out what's important, and think deeply about the hidden messages within.
They also turn the data into clear, visual stories—think graphs and charts that pop with color and simplicity—so anyone can make sense of the data and put it to use.
I've worked three different data roles at this point, and all of them involved dashboarding.
– Former data analyst @ Instakart and Summer
Here's an example of a dashboard that a data analyst might be tasked with creating.
The difference between data analysts and data scientists really depends on the industry and the size and scale of a company's data operations. Smaller companies often use the two terms interchangeably.
It's really at companies with large amounts of data where there are more noticeable differences.
Whereas data analysts dive into data to figure out "what happened" or "what's happening now?", data scientists take this a step further by using past data to predict the future. This could be guessing if people will like a new feature in an app or predicting if sales will go up or down.
To do this, they use advanced math, statistics, and machine learning to spot patterns and make educated guesses about the future. This helps businesses not just to wait and see what happens but to plan ahead. They can decide how to spend their money, change their plans, or even shift their focus to make the most of what’s coming or avoid potential problems.
While a data scientist might focus on the future and the big picture, using their deep technical expertise to guide strategic decisions, the data analyst might work more on the day-to-day, carrying out the plans set by the data science team and making insights accessible to other departments.
For example, a data scientist might design an algorithm to predict customer buying behavior, and the data analyst would use this algorithm to create reports for the marketing department.
You can find plenty of internships on Prosple. We have a vast selection of internships curated for students like you. Just filter 'til you find the right fit!