USES OF EXCEL IN DATA SCIENCE

Excel has several usages in the data science field. Following are the top uses of Excel for any data scientist.

– THE VISUAL BASIC CONCEPT

Popularly known for its short-form VBA, the Visual Basic for Applications is one of the popular features of the Excel sheet in general. The VBA is a popular programming language integrated into Excel, which helps you to perform over 10+ important functions. This is inclusive of the functions such as the creation of Excel VBA Userform, controlling of command buttons, referring to a particular set of Arrays on the Excel sheet, manipulation of strings, looping of cells on the Excel sheet, and more of similar nature. VBA is popularly used to execute codes too on the Excel sheet. You can know more about this by enrolling in a Best Data Science Course in Hyderabad.

– TO SORT, AND TO FILTER

Organizations, in general, prefer to store their data on Excel sheets for two different reasons: to sort and filter their data.

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The two concepts of sorting and filtering data are quite simple to handle.

To sort your data on the excel sheet, all you have to do is to click the filter button on the excel sheet. The same button can be used to filter the data on the sheet, in addition to a relevant keyboard shortcut(s). The filter button is one of the easiest ways on the Excel sheet to explore the database quickly. Using the quick shortcuts on the Excel sheet, you can always highlight the important information. This will enable you to make your presentation more informative and easier to understand too. Another main benefit of marking the information is the ease of locating the same whenever necessary.

Following the understanding of the uses of Excel, it is now time to understand the popular and important uses of Python in data science. Check out the Data Science Full Course for in-depth understanding.

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USES OF PYTHON IN DATA SCIENCE

– LIBRARY

To begin with, the libraries in Python are very handy, especially if you are a beginner programmer. For, Python libraries are comparatively simpler and easier to use. The prime aim of Python libraries is to consistently enhance the productivity of the developers.

There are several libraries involved in the Python library. This is inclusive of TensorFlow, Numpy, Keras, and PyTorch.

TensorFlow library is preferred by several developers for its vast benefits. This is inclusive of aspects such as flexibility, open-source, responsive construct, and parallel neural network training. TensorFlow is used in complex functionalities such as voice and photo search on the internet/ on your device. These functionalities play a major role in data science.

Similar to the TensorFlow library, there are several other such libraries too which contribute to the growth of Python in general. This, directly and indirectly, benefits the data science field to a great extent. You can Learn Data Science and know more about this.

– DIRECT USAGE

Apart from contributing to data science through its vast libraries, Python is used directly in data science. Indeed, it is correct to state that Python is used in every stage of data science as well as data analysis.

To simply put, python is used in aspects such as parallel processing to derive insights, collection of data from every corner of the internet, and graphical representation of data. Python is a popular choice to simplify complex information through graphical presentation too.

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FUTURE OF EXCEL IN DATA SCIENCE

Excel has been utilized in the Data Science field for quite some time now. For, data science is all about data, just like Excel.

Starting from simple tasks such as the creation of tables, charts, VBA scripts, and more of similar nature to complex calculations such as vacation costs. Excel has always been proven useful in the data science field. It is quite a matter of fact that Excel played a major role in data science, with respect to scientific innovations. This is inclusive of predictive maintenance technology, and self-driving automobiles.

What does the future hold for Excel in data science?

– To put it briefly, Excel is the prime choice for editing 2D data. Editing in Excel makes it easy to color, format, and share the sheet in a matter of seconds/ minutes. The best part is that your entire team can work on the sheet simultaneously. This will save a lot of time for the team and your organization in general.

– The best part is that the Excel sheet is filled with shortcuts. Every simple to complex task can be done in a matter of a few seconds/ minutes, with few keys.

– As time is evolving, Excel is becoming more equipped with more advancing technologies. Several new technologies are being integrated into Excel to make advanced analytics simpler. To put it simply, Excel is becoming more advanced to understand machine learning properly. More so specifically, execute the machine learning tasks properly.

– Currently, Excel sheets are being made in such a way that coding could be replaced at least to a certain extent.

– As of now, Python is being integrated to live Excel. This is a futuristic plan too. Using Python, currently, you can run an active Excel sheet, fetch table contents/ headers into a data frame, and more of a similar sort. These functions are set to become more advanced in the near future.

– Speaking of the Excel future in general with regards to data science, there have been several discussions regarding features such as execution of SQL queries on tables, fetching data from Hadoop/ Azure cloud, and execution of SQL queries on live databases.

– It is a known fact by now that Excel sheets are one of the easiest ways to manipulate/ modify data. Now the concept of Data lineage is being planned to be implemented. With the implementation of Data lineage, you will be able to track the source of the data easily. This makes data verification and cross-verification much easier.

Now it is time to understand the future of Python in data science.

FUTURE OF PYTHON IN DATA SCIENCE

– First of all, why are there claims that Python has a big future in data science? To put it simply, through statistical shreds of evidence, it has time and again proven that the percentile of Python integration in data science is multiplying every year. As per several latest reports, it is said that by every year since 2018, the python integration in data science is increasing by 151%

– Developers are finding ways to utilize Python more efficiently by lowering the existing barriers in the programming language. This will be a huge benefit for companies as well as educational institutions across the globe in the near future.

CONCLUSION

To simply put, the growth of Python and Excel is indeed exponential. Through the forecast of the future, it is safe to say that both Python and Excel are here to stay. More so specifically, they are sure to bring about positive effects in data science

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