Data Scientists and Stakeholders may have the most fun and use out of this reason for using visualization. After all the

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).

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.

Pouyan is also leading the Data Science Circle team to build a career hub between employers and data science talents. DSC’s mission is to nurture the next generation of data scientists through career training and helping the employers to find top talents in big data.

Pouyan has done his Ph.D. research work on predictive modeling of consumer decision making and remains interested in developing state-of-the-art solutions in machine learning and artificial intelligence.

8 Career paths for junior data scientists to pursue If you finally achieved your goal of becoming a data scientist in a company you liked. You got onboarded successfully…towardsdatascience.com

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.

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.

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.

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diet rich in fruit and vegetables (and low on meat and soft drinks) was characteristic of high openness. Also, they found that conscientious people were less likely to overindulge in sweet and savory foods. Another study linked neuroticism with slow eating and low enjoyment of food.

The times are changing, and new technologies are emerging every day. The process of work in software projects is continuously evolving. Therefore, the data scientists must adapt to these new technologies for better performance at enterprise software projects.

Pouyan has done his Ph.D. research work on predictive modeling of consumer decision making and remains interested in developing state-of-the-art solutions in machine learning and artificial intelligence.

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.

Pouyan R. Fard is the Founder