Tag Archives: data scientist

8 Best Data Visualization Tools that Every Data Scientist Should Know

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Data scientists have to analyze, interpret, and visualize large datasets on a daily basis. This is why it is important for them to have the right data visualization tools at their disposal.
It can be difficult for people who don’t work closely with data every day (such as managers or executives) to grasp what they are trying to say if all they give them is words on paper or an Excel spreadsheet full of numbers without any context.
Data visualization tools allow data scientists to communicate their findings more effectively, which is important because it allows them to share their insights with other people who may not be familiar with data science concepts.

Best Data Visualization Tools for Every Data Scientist

In this article, we shall take a look at some of the best data visualization tools for data scientists and how they can effectively increase the efficiency of data scientists. Here are eight important data visualization tools to help data scientists make better-informed decisions.

1. Tableau

Tableau is a data visualization tool that can be used to create interactive graphs, charts, and maps. It allows you to connect to different data sources and create visualizations in minutes.

2. QlikView

QlikView is not just another data visualization tool, It is a data discovery platform that empowers the users to make faster, more informed decisions by accelerating analytics, revealing new business insights, and increasing the accuracy of results.

3. Microsoft Power BI

The Microsoft Power BI is the data visualization tool that is used for business intelligence type of data. It is and can be used for reporting, self-service analytics, and predictive analytics.

4. Datawrapper

Datawrapper is an online data visualization tool that can be used in various contexts. It is very easy to use, and it has a clean and intuitive user interface.

5. Plotly

Plotly is a data visualization tool that is used to create interactive graphs, charts, and maps. You can also use Plotly to create a visualization of a dataset, then share the link of that visualization with your readers on social media or on your blog.

6. Sisense

Sisense is a data visualization tool that allows you to easily create interactive visualizations from your data. With Sisense, you can quickly and easily create extensive, informative dashboards that will help you understand your data better.

7. Excel

Microsoft Excel is a data visualization tool that has an easy interface, so it doesn’t have to be difficult to work with.

8. Zoho analytics

Zoho Analytics is a data visualization and reporting tool that can help you to easily create custom reports and dashboards.

Conclusion

In the modern world, data is everywhere and it’s important for brands to be able to decode and communicate their message in an effective manner.
And for data scientists, learning and keeping up with all the latest data visualization tools is paramount, and only after they master this art, they can keep up with the pace of big data, and the fast-moving realms of AI and ML.

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Data Science Is Dead

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This article gives a very good criticism about the popular Data Science / Data Scientist these days. Data Science is something business people invented as a creative way for a new profession as a result of Big Data. “Science” is about creating knowledge as a result of study / research. Consequently, “Data Science” should be about creating knowledge through the study of data, not just data analysis, a/b testing, or troubleshooting which almost the majority of business people are doing. Essentially, the article claims data analysis / data troubleshoot is NOT Data Science.

Today it is a big hype for companies to look for Data Scientists with unrealistic expectation in those job ads. They are looking for a miracle medicine but a quick fix to data which they are unable to handle today. As a result, a short cut is taken and a new profession is created. Major software companies even invent new products to automate the jobs of Data Scientists! It is just like the story of the King’s New Robe. When a king was naked with an imaginary rob and walking down the street, everyone was so ashamed to be called stupid that they never called out the imaginary robe as a lie, not until kids, with their pure and untainted mind, laughing at the king’s stupid belief. The story was repeated to supply chain management (SCM) and is repeated to Big Data and Data Science today. People are so ashamed to be called stupid so they just follow the trend and try to build empires out of the trend. Companies are spending billions of dollars to just make reports as eye candy but do not really know how to use them to improve their bottom lines.

… you’ll realize that the “Big Data” vendors have filled your executives’ heads with sky-high expectations (and filled their inboxes with invoices worth significant amounts of money) …

The author claims there is no data science until you are working on “structured” data, where is most statistics draws its inference for prediction and control. The author emphasis the importance of “cleaning off the rotten banana peels” before you look at data so you won’t draw biased conclusion, which is totally against the idea of Big Data today.

I understand the importance of having fresh idea to keep people engaged in our advancement. Putting emotion aside, if possible, this article does provide a very bitter but true advice to Data Scientists.

Don’t be the data scientist tasked with the crime-scene cleanup of most companies’ “Big Data”—be the developer, programmer, or entrepreneur who can think, code, and create the future.

Reference: Data Science Is Dead

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