Ranking the Best (and Worst) Data Analytics Tools

Introduction:

Hey everyone! In this blog, I’ll be ranking some of the most popular data analytics tools and sharing my honest take on each. I’ll be splitting them into two categories — tools that just clicked and the tools that just didn't. I’ll be evaluating their functionality, ease of learning, and overall popularity in the industry.

Some tools have completely transformed the way I analyze data, while others have been frustrating or just not worth the effort. Of course, this is all based on my own experience, so feel free to agree (or disagree) in the comments. Let’s dive in!

Tools That Just Clicked:

Python -

This had to be at the top of my list (and an obvious one). I’ve been using Python for years, and I absolutely love its versatility. It’s loaded with tons of libraries for data cleaning, building complex ML models, and even creating visualizations. That being said, it’s important to keep in mind the steep learning curve. But trust me, if you’re just starting out as a data analyst, it’s definitely a tool you want under your belt.

And that’s not all, learning Python can really give you an edge in the job market. A ton of hiring managers actively look for data analysts with Python experience. I found this graph on Luke Barousse’s datatech.nerd (super cool website — definitely check it out!). Out of almost 650,000 job listings, Python appeared in 30.9% of them, ranking in the top 3 overall.

SQL -

If there’s one tool every data analyst needs to know, it’s SQL. It’s hands down the best for querying and managing databases, making it easy to extract exactly what you need from massive datasets.

Moreover, it’s incredibly easy to learn since the syntax feels like “caveman English” — straightforward and to the point iykyk.

What I love most about SQL is its efficiency. Unlike Excel, where large datasets can slow things down, SQL handles millions of rows effortlessly. The ability to filter, join, and aggregate data with just a few lines of code is a game-changer. Plus, it’s widely used across industries, so mastering it gives you a solid edge in the job market.

Tableau -

This is hands down the best tool for visualization. The ease of importing data, setting up relationships, and exporting polished dashboards is just awesome. The interface is user-friendly yet packed with advanced features. Plus, you can create parameters and custom calculations to really dive deep into your data.

I’ve only recently started using Tableau in some of my projects, but I’ve already learned so many complex features just through self-learning. Every time I open it, I discover a new feature that upgrades my dashboard. I’ve been able to make some really amazing dashboards, and I feel like there’s still so much more to explore in this tool that will help me create even better visualizations.

Excel -

Excel made it to this list mainly because of its friendly UI and solid data-cleaning features. Filtering columns lets me quickly get a snapshot of my dataset and spot any outliers. With key functions like VLOOKUP, IF statements, and more, it’s a breeze to fill in gaps or organize data. When you throw in pivot tables, I can easily summarize my data into different aggregate forms and group them by various attributes.

One thing that really sealed the deal for me is its visualization feature, it’s the cherry on top. While it may not be the flashiest tool out there, its robust data cleaning and manipulation features already make it a solid choice. Add in the ease of building intuitive dashboards, and you’ve got an all-around data analyst tool that covers all the bases. Very much suitable for an upcoming data analyst.

Tools That Didn’t Work for Me

Rstudio -

Honestly, I’ve only used this tool during my data analytics courses and barely touched it for personal projects. But from the little experience I had, I just didn’t enjoy it. The UI was difficult to navigate, the syntax felt unfamiliar compared to other programming languages, and overall, it just seemed too narrowly focused on statistical analysis.

Compared to Python, R might have the edge in stats, but Python lets you do so much more making it a much more versatile tool.

PowerBI -

Now, I’ve added Power BI to this list mainly in comparison to Tableau. Having used both tools, I honestly find Tableau to be the far superior option. I struggled with Power BI’s UI — it felt less intuitive, and its features didn’t stick with me as well. On top of that, the dashboards I created in Power BI just didn’t have the same polish as the ones I made in Tableau.

I used both tools for a few months, and during that time, my Tableau dashboards consistently turned out better. To illustrate this, here are two dashboards I built around the same period. The first one was made in Power BI, while the second on was made in Tableau.

While this is definitely up for debate, I personally found it much easier to add more visuals in Tableau and derive more meaning from my dataset. That said, I did enjoy creating this colorful dashboard on Power BI that matched Airbnb’s color palette and found the text box functionality powerful.

Matlab -

Last but not the least, now I’ve only used MATLAB a handful of times for data analysis, mainly during my university courses, but from that experience, I just don’t think it makes the cut. The syntax felt difficult to understand and follow, and the learning curve is steeper than it needs to be.

Plus, unless you have a license, getting access to MATLAB is impossible, which definitely hurts its popularity among data analysts (especially newcomers and students!). That said, it does offer a solid range of statistical tools and visualizations, but for me, the drawbacks outweigh the benefits.

Conclusion:

At the end of the day, every tool has its strengths and weaknesses, and what works best really depends on the person using it. Of course, this is just my take based on my experience so far. Every analyst has their own preferences, and at the end of the day, the best tool is the one that helps you get the job done.

What are your go-to tools for data analysis? Let me know in the comments — I’d love to hear your thoughts!