🚀Entering Tech #010: How to become a data analyst

Today’s trivia is inspired by the hot Twitter news that has been trending all week. 

It’s that time of the year when companies and governments are gearing up for yearly reports.

Very soon, we’ll see stats that tell us how many people did what, when, and why. 

Take this edition of TC Daily, for example, where Timi talks about startup funding in October 2022. In that edition, Timi shares that North African startups had the highest raise per region, while fintech had the highest total raise per sector.

How do we get these kinds of stats and data? It’s via data analysis, the work of data analysts!

Data analysts are techies who take raw data, clean it, and extract actionable relevant information from it. They make sense of numbers and data, and tell stories with it. You know all those excel sheets with rows and rows of information? Data analysts take that data and simplify it for everyday people.

💡 Data analysis is how Professor X uses Cerebro to scan all living things on earth, and differentiate between mutants and human beings. Cerebro, in the comics, can even be used to classify mutants based on their power levels. 

Data analysts are the ones who can find out if popularly spewed beliefs like “Apple should build offices in Africa because Africans buy the most iPhones” or “Coldstone Wuse in Abuja, Nigeria, is the most profitable Coldstone in the world” are true.

Data analysts are super important to any organisation because they help sift through the proverbial chaff. Every time a company wants to make a decision on what product to build, service to offer or market to expand to, it’s data analysts—and not the CEO’s guts—who help make the best decisions.

So how do data analysts do it? How can they help companies and organisations make data-backed decisions? 

Data analysts do this in four steps:

🧺 Data gathering/collection: This is where data analysts find the data they need in the first place. They can do this by sending out forms, questionnaires, or even going out into their target demographic and observing.

🧹Data cleaning: This is exactly what it sounds like. Like anything freshly collected, data has to be cleaned before it can be analysed. When data analysts are done collecting, they sift through the data to remove invalid data like duplicate records, errors or white spaces.

🔎 Data analysis: At this stage, data analysts try to understand the data by speaking to computers using tools. They’ll break down the data and interpret it so they can tell stories later on.

📊 Data interpretation and visualisation: This is the final step for data analysts. After analysing the data, they’ll interpret it in ways anyone can understand. That’s where they say stuff like “59% of people do this and that.” Data analysts will also visualise this data using pretty charts, maps and bullet points so they can carry more people along.

Handling data for a small business might be relatively easy, but how do data analysts read the big data of mega companies? How does Twitter know how many of its 450 million+ accounts are verified?

To process big data, analysts use a number of data processing software tools like SQL (pronounced sequel), R, Power BI, and Microsoft Excel. In these tools, data analysts use code to extract and process the data they want.

For hardware tools, data analysts need powerful laptops, especially if they’ll be processing huge amounts of data.