Making data analytics clearer for beginners 📊 | Data career insights & practical business examples.
Nigeria
Joined September 2019
- Tweets8.2K
- Following2.3K
- Followers1.2K
- Likes42K
Pinned Tweet
Learning data analytics in public has taught me something deeper than Excel or SQL. 📊
You can start from somewhere completely different and still build something meaningful.
I studied Animal Science.
I taught in classrooms.
I worked in customer service and sales.
Now, I'm learning data analytics.
And honestly, starting over isn't always easy.
There are days when you feel behind.
Days when you wonder if you're learning fast enough.
Days when the people around you seem miles ahead.
But I'm learning that progress doesn't require you to have everything figured out.
It starts with asking better questions.
1. Start with the question, not the tool.
Before opening Excel, ask:
What decision will this analysis help someone make?
2. Understand your data.
Where did it come from?
What does it leave out?
Can you trust it?
3. Learn the fundamentals.
Tools will change.
AI will get better.
But the ability to think clearly will always matter.
4. Learn to communicate.
Finding an insight is one thing.
Explaining it clearly enough for someone to act on it is another.
5. Build by solving real problems.
Don't just learn formulas.
Don't just watch tutorials.
Take a messy problem.
Work through it.
Make mistakes.
Figure it out.
Share what you learned.
AI can write a formula or SQL query in seconds.
But knowing what to ask, what to investigate, and what the answer actually means is the skill I'm building.
I'm Obinna.
I'm learning data analytics in public, one real problem at a time.
If you're learning a new skill, changing careers, or starting over too, you're not alone.
We're building from where we are.
What are you currently learning that could change your career?
Building, mostly. Two weeks ago I rebuilt a Superstore dashboard, and this week I found the profit tile had been hiding 156,131 of losses because it only counted rows that made money. That was more useful than any course I have done this year. How is your own week going?
I used to clean data by deleting what looked wrong.
Then I learned the blank cells were the information. A missing discount is not zero discount. A missing region is not 'unknown', it is a row someone did not finish.
Count your blanks before you touch them.
I split my Superstore data by discount and the number flipped.
Full price orders: +$320,988 profit.
Discounted orders: -$34,591.
The discounts were not driving sales. They were paying for sales. One pivot table showed me that.
Two months collating is the part I want to hear about. What was the hardest thing to get right before the launch?
The barber example is the one that stings, because the work didn't get harder, the arithmetic got worse. What I see in Ebonyi is the same thing one layer down: a family can't absorb a school fee that used to be routine,
The biggest challenge in Nigeria today is that you wake up every day poorer, even with more money in your bank account, without having committed any crime.
It is no longer just about hard work. The entire system must function for ordinary people to survive. Purchasing power has been severely eroded.
There was a time when a barber could work hard, save consistently, and afford a car within two years. Today, that same dream has become almost impossible.
When the political and economic systems fail, everyone suffers.
You can do everything right as an individual and still become poorer because the system around you is failing.
Your personal prosperity means very little if the economy surrounding you is collapsing.
Filtering for discounted orders told me nothing. Grouping profit by discount band told me everything: full-price orders made $320,988, discounted ones lost $34,591. The discount policy was losing the money, not the product mix. Split before you judge a total.
Before you trust a total, count your blanks.
A column summing to 286,000 means one thing with 9,995 rows filled and another with 400 of them empty. Same formula. Different answer.
The number that made me stop: 1,871 orders were losing money and the dashboard tile said we were up 442,528. The tile was summing only the positive rows. Nobody had told it to include the negatives. Same table, same formula, two different stories.
Six months ago I couldn't read a pivot table.
Today I found 1,871 loss-making orders in a dataset of 9,995 rows that everyone around me thought was fine.
The skill wasn't Excel. It was knowing which number to doubt.
A dashboard tile was hiding 156,000 in losses.
The Total Profit tile summed only the positive rows. Nobody had told it to include the negatives.
If a measure can't return a negative number, ask what it's been told to ignore.
Check: does your SUM include losses?
I've watched this from the school side. The teachers who got noticed were rarely the ones talking about their work, they were the ones whose students suddenly turned around.
First thing I do in any new dataset: scroll to the bottom of the column I care about, not the top.
The top shows your best numbers. The bottom shows whether someone typed a total into the data.