What Excel skills are required for Data Science?

What Excel skills are required for Data Science

Excel sheets also known as spreadsheets are popular among data scientists. The use of Excel sheets dates back to 1979 and is still one of the most common ways to manage data.

The top reason for the popularity of Excel sheets is that they are packed with plenty of features and functions. You can use multiple features to arrange, aggregate, and graph data in rows and columns.

Excel sheets allow you to record, arrange and visually showcase a large amount of data in a simpler form like graphs, pie charts, etc. Data scientists make use of these graphs, pie charts, and other forms to arrange and analyze data for their organization.

Data scientists have the skills and expertise required to manage and manipulate data in Excel sheets. If you want to make a career as a data scientist, you can learn these skills by joining a course in Excel. You can search for free online excel courses with certificate that help you get a job in the industry.

Let us explore the top Excel skills used by data scientists for data management.

Smuggle Data from websites



Fetching data from websites enables data scientists to attain speed and efficiency in their workflow. If they come across a website with a large amount of data that is useful for their assignments, they should have the skills to fetch that data. Here are the steps to follow to fetch data from a website.

  • Click on the Data tab.
  • Go to the ‘Get and Transform Data’ pane and choose the ‘From Web’ option.
  • A new dialog box will appear
  • Enter the website URL in the dialog box
  • You are ready to import the data.

Use of Functions and Formulas

use of functions and formulae


Every data scientist needs to make use of functions and formulas in Excel that are highly useful in handling mass data. Formulas are the functions that users can create and employ to perform large calculations with high speed and accuracy.

On the other hand, functions come pre-fabricated in an Excel sheet. Data scientists use functions and formulas to extract and summon data required for further analysis.


The AutoSum feature comes in handy when data scientists need to deal with multiple calculations. There are two ways to use the AutoSum feature in Excel.

You can use it in a blank cell at the end of a column by pressing ‘ALT+=’. The blank cell gets highlighted for the sum of values in the row or column.


In the ‘editing pane’ by clicking on the summation symbol to employ the auto sum feature at the end of a row or column.

Auto Filter

The Auto Filter feature in Excel allows users to arrange large amounts of data in an order such as ascending, descending, alphabetical, etc. To employ the Auto Filter feature in Excel, users may

  • Select the data they want to filter
  • Visit the ‘Editing’ pane
  • Choose the ‘Sort and Filter’ option and align the data as required

Expose Formulas

Exposing formulas helps data scientists to uncover the functions and formulas being used in a spreadsheet. They help them to understand how a conclusion was met in an Excel sheet. Users can reveal the formulas in a sheet by pressing the ‘CTRL + ~’ keys, and the formula implied will be displayed in the cell.

Use of Shortcuts

Data analysts and professionals working on spreadsheets recommend using shortcuts to achieve high productivity and efficiency. MS Excel is a keyboard-friendly application that allows its users to access the key features using keyboard shortcuts.

Users can press ‘ALT’ to reveal the shortcuts to features they want to use while working with any form of data stored in the worksheet.

Summing Up

Data scientists require a variety of Excel skills for effective data management. With these skills, data scientists can efficiently manipulate and analyze large amounts of data, making them valuable assets to any organization. There are various free online Excel courses that students and professionals can join to learn and improve their skills in this area.

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Within the bustling realm of data science, our editorial team stands as a collective force of learning and exploration. Meet the dynamic minds behind the scenes—Sukesh, Abhishek, and other Authors. As passionate data science learners, they collectively weave a tapestry of insights, discoveries, and shared learning experiences.
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