Data science has received a major shakeup when it comes to the tools that are commonplace in the industry. Microsoft Excel has long been one of the premier tools for many data science experts. Today, however, there has been a massive shakeup with the use of Python instead.
Users with advanced Excel skills are still very much in demand with the analysis of data, but Python is quickly becoming one of the more popular tools. In this article, we are going to compare the evolved version of Excel to Python so that we can see the value that Python can bring the data science.
Excel the evergreen version:
As still a popular choice for many consultants, some risk factors and challenges are associated with the use of spreadsheet technology here. Creating a spreadsheet can be a fragile process, and many challenges need to be overcome from the perspective of anyone working with Excel.
Excel also has real difficulty with handling the growing amount of data that is required for updating a wide range of spreadsheets. Making sure that this data can be properly fixed up and added to remains important. The program itself may not be able to handle the enterprise-level solutions that can be often required for proper analysis. Most spreadsheets today are becoming even larger and more complicated especially with ongoing inputs from AI.
The program also has many security risks, and it can be easily skirted around from the perspective of malicious hackers. The various security risks associated with the program can often lead to some problems that a typical IT department may need to get around. Using a solution like python can help to make sure that data can remain more secure.
Python:
Python requires some coding skills to get started, but it has some huge advantages over Excel.
There is less in the way of data security, and it can ultimately be easier to read for large data requirements. This means that if you are planning on doing some data analysis for AI and machine learning, the system can offer a substantial advantage over Excel. Data scientists are also using Python so that they can better visualize data even when they are presented with extremely large sets. If you are looking to perform massive calculations or even visualize data in a very effective manner, it is much easier to use a system like python.
One of the largest advantages that Python can deliver is the chance to use data throughout the entire workflow. This can often lead to a faster programming solution, a better presentation of data and a much faster use for building prototypes with the data. By learning Python, you could essentially become much more in demand professional within data science. The process of learning this programming language can also be quite easy if you consider yourself to be a bit of an expert with managing data in Excel.
Source: The information used in this article is from , https://www.analyticsindiamag.com/is-python-the-new-ms-excel-in-data-science/ , https://www.datasciencecentral.com/profiles/blogs/why-excel-users-should-learn-python ,
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