, especially those new to the field, often think data science is only about collecting data from databases, working with algorithms, and deploying models.
However, it’s more than that. Data analysis and visualization are essential aspects of data science, which help you understand complex data, make sense of it, and create actionable insights.
During my early days in data science, I never saw the need for data visualization, and that was because I wasn’t exposed to and acquainted with the knowledge and appropriate tools for addressing visualization tasks effectively.
I still remember the frustration of spending hours buried in Excel sheets, manually updating pivot tables, and endlessly adjusting the chart’s layout, just to build something that still didn’t tell the story I wanted.
Don’t get me wrong, Excel is great, but sometimes it just doesn’t cut it.
As a computer science major with a growing interest in data, I knew there had to be a better way — but I didn’t know what that was yet.
My first real struggle came during a university project where I had to analyze student performance data across multiple semesters.
I know what you’re thinking; that should be quite easy.
Well, yes, it is.
But to me back then, it wasn’t.
I had rows upon rows of scores, attendance rates, course codes, and so on, but turning all that data into meaningful insights felt like trying to teach Tony Stark to be humble.
I tried everything: Excel formulas, conditional formatting, and even dabbled a little with matplotlib to generate some plots. Nothing clicked; it was overwhelming.
That was when a senior colleague mentioned Microsoft Power BI.
For those who don’t know, Power BI is a data visualization and Business Analytics tool developed by Microsoft that allows you to connect, transform, analyze, and most importantly, visualize data.
At first, it sounded like just another tool on a long list of software I hadn’t quite mastered. So I had to do some personal reading.
I got my hands on “Power BI Cookbook: Creating Business Intelligence Solutions of Analytical Data Models, Reports, and Dashboards,” a book written by Brett Powell, and that was the beginning of finer things.
It’s not just a book, it’s more like a comprehensive guide for understanding the whole concept of creating interactive visualizations using Power BI.
After a couple of days of learning the workings of Power BI, I had imported my dataset, cleaned it using Power Query, and built my first interactive dashboard.
For me, I saw it as something more than a technical upgrade, it was a mindset shift I didn’t know I needed moving forward into data science. It changed how I thought about data itself.
Moving forward into this article, I will be sharing powerful ways Power BI helped me in my Data Analysis and visualization journey, as well as personal stories and actionable takeaways that can help you grow in professionalism as a data scientist.
The day I stopped Copy-Pasting and Started Living
Yes, it was that big of a breakthrough.
When I started analyzing data, my workflow looked like a chaotic relay race: I opened an Excel file, copied data, opened a new window, pasted it into another sheet, crossed my fingers, and prayed to the heavens that nothing broke.
And guess what, something always breaks.
After copying and pasting from one file to the other, I had folders filled with files named things like Sales_Q4_FINAL_final2.xlsx, and yet I still couldn’t keep track of everything.
Power BI’s ability to pull data from literally everywhere, databases, spreadsheets, and even cloud services, means I no longer have to play data Tetris. With just a few clicks, I connected my Excel sheets, SQL database, APIs, and even data files I stored locally.
Don’t worry if you had challenges importing your datasets, or something didn’t just work as you expected. It’s easy, trust me, you just need more practice.
Play around with the dashboard and understand what button does what and how to use them. There’s this satisfaction that comes with finding your way around.
The first time I saw all my data update live, I just sat back and smiled. No copy-pasting, no chaos, just clean and connected data.
Intuitive Visualizations with Customization Options
Like I said in the beginning, most people underestimate the power of good visuals, particularly when dealing with data. I find that absurd because let’s be honest, raw data doesn’t always tell a story.
According to a study published in the journal Information Visualization, people process visuals 60,000 times faster than text.
If that doesn’t do it for you, even MIT suggests that the human brain can identify images seen as little as 13 milliseconds.
In practical terms, these studies mean that your dashboard visuals are being absorbed and interpreted before someone finishes reading your chart title or even takes a look at the numbers you spent hours crunching.
My favorite feature of Power BI has to be the interactive and advanced Data Visualization capabilities. With its intuitive drag-and-drop interface, you can turn the dullest (As much as I love data, it looks dull at times) datasets into dynamic dashboards.
With a wide array of visualization options ranging from:
- Matrix & Table Visuals
- Gauge and KPI Visuals
- Slicers and Filters
- Decomposition Tree
- Waterfall Chart
- Map Visuals
There are a lot more others, but I consider these my personal favorites.
Data scientists and analysts need the ability to successfully interpret data, identify trends, and help businesses make better decisions.
As computer science pioneer, Ben Schneiderman, rightly put:
“Visualization gives you answers to questions you didn’t know you had”
Power Query: The Silent MVP Behind My Clean Data
You might ask, What is Power Query?
Power Query is a data transformation wizard built into Power Bi. It is a wonderful feature that allows you to clean, reshape, and prepare data before loading it into your model for analysis and visualization.
I see it as the engine that powers data preparation in Power BI.
Data is messy. That’s just part of the job. Plus, with companies and businesses expanding, more and more data is being collected. It’s quite challenging for most data scientists and analysts to get hold of large sets of raw data.
Remember the challenge I had with my university project?
It turned out that one of the reasons why I was finding it difficult to perform analysis was that my datasets were all chaotic.
I was asked to analyze students’ performance, which pulled data from three different CSVs, each with its quirks. One had admission codes instead of names, another used inconsistent date formats, and the third had course titles listed in ALL CAPS (screaming at me).
With Power Query, here is how I built a complete workflow:
- Replaced admission codes with readable names
- Converted date formats
- Standardized text formatting
- Merged everything into one organized table
Data preparation takes up to 80% of a data analyst’s time. Imagine how much time you would save and how productive you’d become when you focus all that time and brainpower on generating better insights. Time reclaimed for coffee and yes, real analysis.
Collaborative Sharing and Cloud Accessibility
I believe collaboration is a key player in the data science industry, and here is why: No single person usually has all the expertise required to take a project from raw data to real real-world project.
Stay with me.
Consider data science a process. It involves collecting data, storing it in a database, and creating algorithms and models that improve data quality, analysis, visualization, and other essentials.
To handle data effectively, these stages are often handled by various professionals specializing in diverse areas, all working together toward a shared goal. Hence, collaboration.
Power BI, being a cloud-based platform, allows you to publish and share your analysis reports with other data professionals.
Instead of emailing Excel files (which I’m sure we all did once or twice), with a few clicks, I was able to publish a dashboard and share a live link with my team. They can make changes, share their thoughts, or even update the data source in real time.
In a remote/hybrid work world, having that sort of seamless collaboration is a real game-changer for data scientists.
Applicable takeaways
If you have ever tried data analysis and visualization but found it difficult or complex to understand, maybe you haven’t been using the right tools.
Power BI didn’t just help me tackle the problems I encountered when I first started working with data, it transformed how I approached data altogether.
Most of us are already familiar with Power BI, while it’s a new adventure for others. Regardless of what category you fall into, I highly encourage constant learning of the tool and how to maximize its features.
I highly recommend checking out Guy in a Cube on YouTube, he teaches Power BI through his informative videos.
For verbal learners, you can get a huge chunk of information from Brett Powell’s book. I mentioned it at the introduction, and personally, for me, it’s hands down the best book on data visualization I’ve ever read.
Familiarize yourself with these features and start improving your data analysis and visualization workflow.
