In your day-to-day routine as a data scientist, it is not entirely necessary to learn to use Tableau. But it is also true that it can still prove to be a useful skillset in solving everyday data-related problems. There are many companies out there who never use Tableau. But at the same time, there are companies that expect to have their data scientists and machine learning engineers working with tableau. Their work isn’t on a daily basis but more like a few days of work per week. But in the end, it all depends on you, your team, and of course, your business whether or not you want to use Tableau.
So that is why we thought why not highlight to our readers what Tableau can help you improve and what it is great at in terms of data science and machine learning.
What is Tableau?
Tableau is a very useful business intelligence tool that helps you deal with loads of data meticulously. The target market of the tableau is business and data analysts who have to deal with data on a regular basis. Keeping in mind the wonder that Tableau can do, there are many companies that even have a designated Tableau Developer. This person has to focus on creating reports and dashboards for their respective audiences or stakeholders. Thus, it is a very helpful and efficient tool that we can use. So I hope you know understand What is Tableau.
How can it change the way we think about data?
There are some important ways that can help us change the way we think about data using Tableau which are the following.
- Provides fast analytics.
- Very easy to use.
- Can deal with big data and any other type of data.
- Offers smart dashboards.
- Provides automatic updates.
- Can be shared in seconds.
How it doesn’t suit well for data scientists?
Though Tableau is an effective tool and data analytics and other businesses can improve the way they deal with huge data, yet there are some people who cannot use this tool the way others can, that is, the data scientists. So let us take a look at how Tableau isn’t as helpful for data scientists as it is for others.
It cannot operate with Jupyter Notebook:
As data scientists, automation and integration are two important keys. You would want to unite all your processes so that when you perform any of your business problems, you can use case, exploratory data analysis, feature engineering, model building, and deployment. Since all these steps are in one place and connected together, it is easy for you to work. But tableau cannot be operated with Jupyter Notebook which means that you cannot enjoy all these features.
It can be slow sometimes:
Well, this is something that might not happen to you often. But sometimes there will come a point where you find yourself with several tabs or sheets and dashboards and then all of a sudden you find yourself in front of a giant Tableau workbook that freezes. So this is the point where you get frustrated to keep on making new dashboards without deleting the old ones.
How it works well for a data scientist?
Above were some of the points that might not be in favor of data scientists. But that doesn’t mean Tableau is of no use to data scientists at all because it is helpful in many ways for data scientists.
It visualizes data really well for exploratory data analysis (EDA):
The EDA is often ignored in data science processes and one thing that data scientists don’t know is it plays a vital part in making or breaking your model. So it is very important to have the ability to visualize your data quickly before building out the model. With that, Tableau is also useful to display charts, graphs, or other forms of visualizations for your data science or machine learning model metrics.
It is a step up from Matplotlib and Seaborn Python libraries:
For many data scientists, it might take a lot of Python code, and in the end, what they’re able to make is an unappealing chart. But that is not the case in Tableau as it helps you make fancy visualizations in a few seconds without coding.
It also integrates really well with SQL queries:
Whatever you can do in SQL can be easily done in Tableau. You can efficiently paste your queries and then reference them to make whatever you want in Tableau. With that, you can also use a static excel or CSV file. Checkout Excel to MySQL: Analytics techniques for Data Businesses.
What did we conclude?
After taking a look at the benefits that data scientists can gain from Tableau, we have got really impressed by how useful it can be for data scientists. Initially, it was not intended for data scientists but then if we take a look at the pros and cons that we have discussed, then anyone can be convinced that Tableau is very useful for data scientists. Getting certified in Tableau gives a boost to one’s career. Checkout our Tableau Certification Preparation Videos for details as well as Tableau Certification Dumps for Desktop (Specialist & Certified Association) and Tableau Server. So, we can conclude our topic by saying that Tableau is a useful tool for data scientists, and those companies or data scientists who are unaware of the benefits they can enjoy using Tableau must read this topic and get an idea about it. Therefore, we recommend you to read this topic carefully and understand how data scientists can make the most out of this tool and don’t forget to stay home, stay safe, and never stop learning.I hope after reading this article you have a clear picture of What is Tableau and its benefits for Data Scientist
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