Perhaps the biggest similarity of Business Analyst to Data Scientist is the words itself to describe the role. A Data Sc

The most popular general purpose programming language on planet Earth right now is the Python programming language. This is not only because Python is incredibly easy, and relatively fast, but also that Python has a killer ecosystem with tools to fit every discipline from business to finance and science. One area that this is certainly proven to be true is in the wonderful world of data science.

Both roles vary from company to company. What stays the same are the goals and impact that each role employs. Perhaps the biggest difference is the method of how you get to the solution or surface a finding. Some Business Analysts may find themselves eventually becoming Data Scientists, and vice versa. It depends on your preference for the skills and tools needed to perform your job.

The skills here cover tools that are used to answer issues analytically. Also important in this role is the ability to problem-solve and highlight key areas of improvements for the business.

Thank you for reading! I hope you found this article both interesting and useful. Feel free to comment down below some of the similarities and differences you have found or experienced between Data Science and Business Analytics.

On top of all of that, Plot.ly also has some lesser-known charts and graphs that you would be hard-pressed to find in most other data visualization packages. Funnel charts, pie charts, violin charts, and tree maps are just a few examples of some unique and fun ways to explore data using the Plot.ly library.

On top of the beautiful styling for basic plots, Plot.ly comes with a fully-featured 3D visualization system that rivals some of the best available with modern technology. One great advantage to the way that Plot.ly handles 3D is performance. Although 3D visualizations are of course quite complex and intensive, Plot.ly seems to handle a lot of these applications with relative ease. 3D visualizations are also awesome because the new axis allows for a greater amount of understanding by exploring data’s positional values in 3D space. Furthermore, the Z axis can also represent a variety of features — making it possible to view several different correlations at once.

One thing that has become surprising in the recent development of technology is geo-data. Geo-data has evolutionized with FIPS to such an extent primarily because of smart-phones and global positioning systems (GPS,) and this has given way to a strong foundation for data science to build on top of. This is of course because data is now more readily available than it ever has been for free with geo-data included.

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tured the problems perfectly to make it easier for individuals to analyze the problem and its solution. But of course, that’s not the only thing you’ll find in his repo. You’ll also find some valuable resources like online courses(MOOCs), books, training websites, YouTube channels, and a bunch of other resources that cover Python and solutions to a host of problems developers encounter while working with Python.

These two roles share goals with one another. Each requires a deep dive into data with similar tools as well. The process of communication is similar, too — working with stakeholders from the company to go over the business problem, solution, results, and impact. Here is a summary of the key similarities between a Data Scientist and a Business Analyst.

It is hard to argue with Plot.ly’s beautiful approach to maps, as well. Plot.ly’s clorepleths in particular are an absolute joy to use and can make compelling visualizations in a matter of seconds.

To start us off, I am going with a library for data visualization that is pretty well-known, but some might have never heard of. Plot.ly is a graphing library that takes interactivity to a whole new level. I would genuinely advise using Plot.ly over something like Matplotlib or Seaborn. This is because Plot.ly comes with a multitude of different tools that most scientists can certainly come to appreciate. Just how much is in Plot.ly?

Of course, there are some key differences between these two roles. One of the biggest differences is the use of Machine Learning for Data Scientists only. Another difference is that a Business Analyst can expect to communicate more to stakeholders than a Data Scientist would (sometimes Data Scientist work can be more heads down and not involve as many meetings). Here is a summary of the differences you can expect to find between these positions.

Thank you for reading! I hope you found this article both interesting and useful. Feel free to comment down below some of the similarities and differences you have found or experienced between Data Science and Business Analytics.

Plot.ly comes preloaded with all of the fantastic tools that your average data scientist or even just computer programmer might expect. Scatter plots, bar charts, and line charts are all staples of the Plot.ly module. While Matplotlib can accomplish similar goals, Plot.ly has the same functionality while also having default styling and Java-script interactivity that makes it a lot more fun and a lot easier to explore data. On top of that, presentations are certainly a thing that could be served a benefit from using Plot.ly over many of its competitors.

Of course, Python is also the most popular programming language used today for data science. While most scientists working with the language might be familiar with a lot of well-known and widely used packages such as Scipy, Sklearn, and Matplotlib, there are some packages which most data scientists have never even heard of that are also quite awesome!