Pouyan has done his Ph.D. research work on predictive modeling of consumer decision making and remains interested in dev

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e even mean? Cobol is still running a lot of our financial transactions. Yet I think we can say that Cobol has been replaced in the sense that nobody today will deliberately choose Cobol for any new project. And wherever possible people will attempt to migrate away from it.

Now that we have solved the problem of explaining a complex process to others, we also now know how to solve explaining results to others in an easily interpretable way. The answer, of course, is data visualization. For this example, we assume that we have built a model already and need to explain the results to stakeholders. One of the easiest and best ways to explain results is through simple charts. For example, we will look at results from a model in terms of amount per group. A quick way to show how The United States is performing by the state is through the use of heatmaps. You can color code depending on your respective scale for example. This type of visualization is easier to understand rather than a file with an overwhelming amount of columns with long values.

To summarize this process, you could create a visualization that better describes the proposed process, as well as the timeline involved. There are several ways to approach this visualization. You could create a proof-of-concept by utilizing products like PowerPoint, Google Slides, or some more involved products including Jira, Lucid Charts, Draw.io, and ProductPlan.

But if you treat your painful emotions like enemies by running away from them or trying to eliminate them, you train your brain to see them that way in the future — and this only makes you more reactive to them and keeps your mind constantly stressed out and worried.

Data Scientists focus on complex Machine Learning algorithms created from complicated programming languages. Even more aspects of a Data Scientist’s processes are complicated as well. When you build your models you are relying on the education of development, statistics, software engineering, and much more. Consequently, there needs to be a way to digest this complicated, yet useful and significant data. The main method a Data Scientist can not only communicate to themselves, but also to non-technical stakeholders, is the use of visualizations. I will be describing some of the main reasons Data Scientists need to know data visualization techniques below, rather that be from the exploring of data to the explanation of model results. Please continue reading if you would like to know the top four reasons why a Data Scientist should know how to visualize.

Thank you for reading my article! I hope you found it both interesting and useful. Please feel free to comment down below on the reasons why you use data visualizations as a Data Scientist.

After all, it’s hard enough to cope with the stress of workplace politics or family drama when your mind is clear and still. But when you’re trying to do it with a mind that’s buzzing with worries and insecurities, regrets and ruminations, frustrations and irritations, well… it can be overwhelming, if not completely debilitating.

Data Scientists and Stakeholders may have the most fun and use out of this reason for using visualization. After all the hard work is put in, you can see how your model is performing. You can set up alerts for when accuracy or an error metric like root-mean-squared-error (RMSE) is above or below a certain threshold. Perhaps, you will notice that your model is doing worse as time goes on, suggesting that you either need more data or you need to change something else about your model to make sure that it is similar or better to where it was before.

In the same way that displaying Machine Learning algorthim results to stakeholders is beneficial, so is visualizing data analysis to yourself and your team. The same tools from the above section can also be utilized for this use case. It is important to get a sense of your data before implementing the steps in your Data Science process, like Machine Learning algorthim comparison. You can compare data — its columns and rows, visually as well.

Another way to easily visualize your data is by using Pandas Profiling [5]. With one line of code, df.profile_report(), you can display common and powerful charts, like descriptive statistics through histograms, heatmaps, and matrixes. Below is an example of visualizing your exploratory data analysis with variable examination and correlation plots. These visuals are great for diving into the data to gain a better sense of abnormalities, trends, and relationships between features. This visual analysis can save you time on incorporating possible features into your Machine Learning algorithm as well (before performing feature engineering).

For example, you want to start a project that will help the company classify clothing items quickly on an e-commerce website. In order to get ‘buy-in’, or proof that this process will be beneficial, you will have to outline the process, the expected resources need, and likely results.

Thank you for reading my article! I hope you found it both interesting and useful. Please feel free to comment down below on the reasons why you use data visualizations as a Data Scientist.

When you touch a hot pan on the stove, is the pain bad? Nope, not at all. Even though pain feels bad, it’s actually good! Pain is a messenger telling you to move your hand so you avoid the real danger — tissue damage resulting from third-degree burns.

Data Science can be complicated, but there are ways to improve its understanding by means of data visualization. We have covered several reasons why a Data Scientist should not just know programming and statistics but also know visualization techniques.

As a Data Scientist, you cannot simply start a project without getting the green light from stakeholders first. Some of the stakeholders will not understand Data Science concepts and processes at all — until you explain them well. It is up to you to prove to them that a process could occur including automation and prediction.