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Can a non-technical person learn data analytics?
The short answer is: Yes, absolutely.
In fact, many of us already use the basics of data analytics in our daily lives—often without even knowing it.
Think about it:
- When you compare prices before buying something online
- When you budget for a family trip
- Or when you check how your power bill has changed over time
That’s data analysis in action. You’re looking at numbers, spotting trends, and making informed decisions.
But does that mean you need a tech degree to become confident in data analytics? Not at all. With the right approach and a little curiosity, anyone—regardless of background—can learn and apply data analytics in a meaningful way.
At Excel in BI, we believe data skills should be accessible, practical, and empowering. So, if you’re new to the world of data or feel unsure about your technical skills, keep reading. You might be closer to becoming “data-savvy” than you think.

💡 Why Data Analytics Isn’t Just for Tech Experts
There’s a common myth that data analytics is only for software developers, engineers, or people who are “good with computers”. But the reality is that data analytics is more about thinking clearly and asking smart questions than writing complex code.
In fact, some of the best data analysts come from business, accounting, operations, or marketing roles—because they understand the problems that need solving. The technical side? That can be learned step by step.
👣 Your 5-Step Beginner’s Guide to Data Analytics
Here’s a simple roadmap we often recommend to non-technical learners who want to get started.


✅ 1. Start With the Tools You Already Know
If you have a laptop with Excel or access to Google Sheets, you already have the basic tools you need to begin analysing data. You don’t need to download any software or learn programming from day one.
These tools are widely used in businesses and have enough functionality for you to:
- Organise your data
- Run simple calculations
- Build basic visualisations
✅ 2. Learn to Use Simple Formulas
Once you open Excel or Google Sheets, try using a few formulas like:
=SUM()
— add values=AVERAGE()
— find the average=MIN()
and=MAX()
— identify the lowest and highest numbers
These basic formulas help you calculate and summarise data. As you get comfortable, explore others like =IF()
, =COUNTIF()
, and =VLOOKUP()
to build logic into your analysis.
🧠 Tip: Think of formulas like recipes—each one helps you solve a different type of question.


✅ 3. Work With Real Datasets
Now that you understand basic functions, it’s time to move beyond small examples and start using real-world datasets.
There are free websites where you can download structured datasets to practise with:
- Kaggle Datasets – A huge library of interesting data
- Data.govt.nz – Official datasets from New Zealand government agencies
Try importing a dataset into Excel or Google Sheets, and explore it. Look for patterns like:
- Price changes over time
- Differences between regions
- Correlations between columns
📈 Tip: Start with a simple goal in mind, such as finding which suburbs have the highest rent increases.
✅ 4. Try a Real Example: Where’s the Most Affordable City to Live in NZ?
Imagine you’re planning to live in New Zealand for a year. You’re looking for:
- A place with lower living costs
- Good internet connection (for remote work or study)
- Access to healthcare and public transport
Here’s how you could use data to help decide:
🔍 Step-by-step analysis:
- Get rental cost data
- Use MBIE’s Rental Bond Data
- Download data for different cities
- Get regional income or cost of living data
- Use Stats NZ
- Look for income, transport, and grocery cost data by region
- Compare key cities
Create a comparison table with:- Average weekly rent
- Average household income
- Cost of living index (you can estimate this by combining transport, food, and internet data)
- Score each city
Rank each city on affordability by dividing rent by income or by using a cost of living index. - Visualise your findings
Use a bar chart or map to show which city is the most affordable. You might find cities like Palmerston North, Invercargill, or New Plymouth offer a great balance of cost and quality of life.
📊 Bonus idea: Add filters to your sheet to explore based on internet speeds or proximity to nature—depending on your lifestyle goals.

✅ 5. Use Your Insights to Make a Decision
Take what you’ve found and present it:
- To your partner or family as part of planning your move
- To your employer if you’re proposing a relocation or remote work
- Or for your own confidence that you’ve made a smart, data-backed choice
You’ve just used data analytics to solve a real-life problem.
If you would like to learn how make visuals like this one, check this link

🎓 You Don’t Need to Be a Master to Get Value
Becoming an expert takes time and practice, but you don’t need to reach that level to benefit from data analytics. Even with just a few key skills, you’ll be better equipped to:
- Make smarter decisions
- Communicate with confidence
- Spot opportunities others might miss
No matter your background—whether you’re an accountant, engineer, cleaner, or student—you can learn data analytics. It just takes the right support, clear explanations, and a few hands-on projects to get you started.
🚀 Ready to Learn With Us?
At Excel in BI, we run free fortnightly online workshops to help people like you get started. We focus on simplifying data concepts and showing you practical tools you can use straight away.
We also offer structured online courses where we guide you step-by-step through:
- Data cleaning
- Excel and Google Sheets
- Power BI dashboards
- Storytelling with data
- Automation tools and more
💬 You’ll never feel left behind—and you’ll walk away with real skills you can use in your job or your personal life.
👉 Join our next free session
👉 Explore our beginner-friendly courses

Final Thought
If you’ve read this far, you’ve already taken your first step toward becoming data-literate.
Now let’s keep the momentum going—because data is not just for techies. It’s for everyone. Especially you.