Tableau vs Power Bi vs python: Better for data?

It is an important decision to make between Tableau vs Power Bi vs Python in data science today, as there are numerous tools like Python, R, Scala, PySpark, Hadoop, SQL, NoSQL, Tableau, Power BI, QlikView, Alteryx, etc., which individuals use on their day to day to work with data. The presence of so many tools confuses individuals when they won’t use the right tool for the right task.

In this article, we’ll go over the different things that each of these three tools offers i.e., Tableau vs Power Bi vs Python, that can help you make the right choice when you are encountered with a problem statement and some data.

Tableau vs Power Bi vs Python: General comparison

Tableau vs Power Bi vs Python: General comparison

Generally, All the three Tableau vs Power Bi vs Python have a high number of users all around the world who take their skills in a particular tool to the next level and concentrate on getting the work done. Someone who works highly with a machine learning algorithm will find it easier to work with python-like someone who finds Tableau easier for Data Visualization and the same for someone with Power Bi for Business Intelligence tasks.

The comparison, thus, is very very susceptible to the use case we’re considering. If someone requires any of the three, the choice can be made considering a lot of other factors.

Things like speed, system capabilities, the task at hand, type of analytics, and other factors can impact a person’s choice when choosing between the three different data science tools at hand.

Read this article if you want to know more about Tableau and Power BI: Tableau vs Power BI vs Alteryx

P.S. Another important thing generally missed out on is the learning curve involved. While it is relatively easy to learn python in the short run, it can be difficult to keep up with in the long run. Similarly, it’s tough to hold up Power BI in the beginning but once you get the hang of it it’s easy. Tableau is moderately difficult throughout.

For a data exploration project?

If your data relates only to data exploration, then there are a few little things to consider when you make a choice.

  1. Tableau: Tableau can be of help in a data exploration task that involves geographical locations or too many numbers that can be put together in groups to cluster information with the use of graphical representations without the need to use any kind of statistical summaries beyond percentages etc. This is where Tableau wins in Tableau vs Power Bi vs Python.
  2. PowerBI: Power Bi is a business intelligence tool that can work with data and can also present you with the query editor to enhance the data cleaning and wrangling process before you prepare the visualizations. This can be used when you want to explore some unclean data.
  3. Python: Python is the crown jewel and the most powerful among the three Tableau vs Power Bi vs Python, because of its versatility as a programming tool and can help individuals prepare data visualizations to dive deeper into the data while also enabling them to perform high-level statistical analysis on the data to ensure that no detail is missed out. They can also make very beautiful visualizations if an individual is ready to spend some time to gain the required technical expertise (which is not that difficult). This wins in Tableau vs Power Bi vs Python when data needs more than graphs to understand and is going to be used for some predictive analytics ahead.

Read more about data exploration using Python here: EDA with Python

For a project which involves Machine Learning?

It is not a difficult decision to make when your data requires some sort of predictive analysis ahead.

Predictive analytics requires machine learning and forecasting using time series analysis techniques at times. Here are the capabilities of Tableau vs Power Bi vs Python to help you choose better for this task.

  1. Tableau: Tableau is not the best choice probably when it comes to data exploration with some predictive analytics involved. Though tableau can extend graphs to make some predictive analyses that involve regression with a least square method or OLS method.
  2. Power BI: Power BI’s capabilities when it comes to Data wrangling are midway to Tableau and Python and can also do a little bit of share here and there with integration with R to make Predictive analysis possible. However, it’s not the best choice of the three i.e., Tableau vs Power Bi vs Python.
  3. Python: Python is the best choice of the three (for obvious reasons) Tableau vs Power Bi vs Python; when it comes to predictive analytics. Python is one of the world’s most used programming languages for individuals and companies alike. Using multiple libraries like Scikit Learn etc. that help data scientists perform predictive analysis and derive metrics to check the performance of their statistical models on the data.

Conclusion

To conclude, all data science tools in use are useful in some way or the other. The only difference comes when the situations differ and then the choice of applications changes. One can use each tool in all cases but each one carries its own merits and demerits.

For example, in Tableau vs Power Bi vs Python, one could always choose Python owing to their high technical expertise and if they have ample time, they can derive the most out of their data. If someone who is not very technically experienced and only uses small datasets to explore and have an overview of what the data is saying in their case, then for someone like this probably Power Bi or Tableau become an easier app to use.

In some cases, choosing between Tableau vs Power Bi vs Python also comes down to the system capability wherein on a low-end PC, one can probably not run Power Bi or Python as easily as they can use Tableau. If you use large datasets and force the system to perform beyond its capacity, it can also fry the RAM. (which wouldn’t be so nice)

So, probably one can use a checklist to see which application fits their needs the best for the given hardware. Let us know which application you prefer to use out of the three and why, in the comments below.

For more such content, check out our website -> Buggy Programmer

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An eternal learner, I believe Data is the panacea to the world's problems. I enjoy Data Science and all things related to data. Let's unravel this mystery about what Data Science really is, together. With over 33 certifications, I enjoy writing about Data Science to make it simpler for everyone to understand. Happy reading and do connect with me on my LinkedIn to know more!

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