What in the world is a "data analyst"? A guide for students

Find out what data analytics is and whether you'd like it as a career!

1. What's a data analyst?

　🤔 But first, what's "data"?
　✨ "Data analyst" defined
　❓Why do data analysts exist?
　🔍 What do data analysts do?
　🆚 What's the difference between data analysts & data scientists?

2. Would I like the work?

　💼 What data analysts do day-to-day
　😊 How happy are data analysts with the job?
　💃 What type of people thrive?
　👍 Pros
　👎 Cons
　🌍 Impact

3. Would I like the life?

　⚖️ Work-life balance
　🤸‍♂️ Flexibility
　🤝 The people
　⬆️ Your managers
　🧭 Values alignment

4. What's in it for me?

　🌱 Learning & development
　🌟 Job outlook
　💵 Pay
　📈 Career progression
　🔀 Exit options

5. Where can I find internships?

Part 1. What's a data analyst?

🤔 But first, what's "data"?

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!

1. You're using quantitative data like star ratings and dollar signs ("$$$") to numerically size up a place and compare it to others in a straightforward manner. This is also known as "structured data."
2. You're also using qualitative data like customer reviews and photos to figure out the story behind the numbers you see. This is also known as "unstructured 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!

✨ "Data analyst" defined

A data analyst is anyone who uses large amounts of data to help businesses answer questions. For example:

• At a tech startup, a data analyst might examine user engagement data to understand which features keep people hooked on their app. They’ll crunch numbers on user behavior to guide the company on what to build next.
• In a retail chain, a data analyst pores over sales data to identify what products are flying off the shelves and which ones are lagging. They might also analyze customer traffic and buying patterns to optimize stock levels and store layouts.
• In a manufacturing setting, a data analyst could monitor production line data to pinpoint inefficiencies or predict when machinery is likely to fail, aiding in maintenance planning to avoid costly downtime.

❓Why do data analysts exist?

Data analysts exist because:

• Businesses want to make data-driven decisions and not just take shots in the dark.
• In today’s world, businesses are flooded with data. There's so much of it—customer details, sales stats, market trends, and more—that without experts to make sense of it all, it’s like having treasure maps with no one to read them.

🔍 What do data analysts do?

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. 

Here's an example of a dashboard that a data analyst might be tasked with creating. This dashboard includes various sections like monthly sales, best-selling products, customer satisfaction over time, and the ratio of new versus returning customers, providing an overview of a business' performance. 

[IMAGE]

🆚 What's the difference between data analysts & data scientists?

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 especially outside of tech often use the two terms interchangeably.

It's really at companies with large amounts of data where there are more noticeable differences.

#1 Data scientists answer "What will happen?" instead of "What happened?"

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.

#2 Data analysts support the work of data scientists

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. 

Part 2. Would I like the work?

Coming soon!

Part 3. Would I like the life?

Part 4. What's in it for me?

Part 5. Where can I find internships?

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!