When we hear or see the words “data science”, in addition to the standard definition of the term, there are also a variety of assumptions and beliefs that are also caught up in that understanding. At the level of denotation, data science can be defined as the use of analytical tools, software, and programs to better understand information and data. Generally, data science uses statistical analysis, algorithms, and machine learning in order to uncover patterns and trends within a dataset. 

With that being said, there are several connotations or contextual meanings, beliefs, and assumptions that are commonly associated with “data science.” Metaphorically speaking, a word association cloud or network analysis of the term data science could also connect it with other terms, such as big data, artificial intelligence, and even specific programming languages and analyses. Thinking comparatively, while there are many things that we associate with data science, there are also several things that we do not commonly associate with it. 

Especially when thinking about the recent move towards distinguishing the field of data analytics from data science, the connotations or beliefs around data science also means that while data science has come to be associated with advanced statistical analyses, programming languages, and complex code, many of the ways that data was analyzed in the past have become less popular. Over time, spreadsheet programs such as Microsoft Excel and other early data analysis tools, are becoming less commonly associated with doing data science.

However, Microsoft Excel has many capabilities which make it a useful tool for data scientists. Even with all of the new data analysis tools that have been produced since the creation of Excel, this widely used spreadsheet software has also updated and changed over time. Instead of promoting an either/or understanding of data science and the tools that can be used to complete data science projects, this article offers five reasons why data scientists should use Excel. And, by reading this list, perhaps you will begin to think differently about what programs and tools should be associated with this constantly evolving field and industry.

Background and Uses of Microsoft Excel

Created in 1985, Microsoft Excel is a spreadsheet program that can be used to organize information and data into rows and columns. Similar to database management systems, Excel can be used for inputting data and presenting it in simple ways. Excel can be used for mathematical and statistical analyses, as well as the creation of charts and graphs that visualize the data being stored within a spreadsheet. Offering multiple functions and formulas, learning how to use Excel can make the process of managing information and data easier and more efficient for whoever uses it. 

Although Microsoft Excel has grown out of favor with some data science students and professionals, this spreadsheet software is still quite commonly used for performing statistical analyses, data storage, and organization within the realms of business and finance. Below are the primary reasons why Microsoft Excel remains a popular data science tool within many industries, as well as the reasons why it should continue to be used by data scientists.

1. Ease of Use and Accessibility 

More than anything, Microsoft Excel is incredibly easy to use. Whether you gained familiarity with Excel in an educational setting or an office setting, the popularity of Microsoft Office products means that most people have some knowledge of how to use this tool. In addition, one of the greatest barriers that many students and professionals have to the field and industry of data science is a lack of knowledge around using certain data science tools and programming languages. Especially for beginner data scientists, the ease of use and the wide-scale availability of Microsoft Excel makes it an excellent introductory program for both students and professionals.

2. Communicating Findings to Diverse Audiences

Building on the ease of use and accessibility, one of the challenges for many data science students and professionals is communicating their insights. While individuals who are familiar with data science can find it easy to understand complex statistical models and charts, for audiences outside of the field of data science, it is not always easy to understand certain types of data models and visualizations. The familiarity of Microsoft Excel across fields and industries makes it useful for explaining and communicating the process of collecting and analyzing data with those that are not as familiar with data science.

3. Visual Approach to Data Organization and Management

Programming languages and data science tools can be text-heavy in their representation of information and data. Especially when working on a big data project, it can be difficult to see or visually interpret certain datasets. However, the format of Microsoft Excel is excellent for those who prefer to take more of a visual approach to organizing datasets. By representing data through rows and columns which can be easily updated, annotated, moved around, and manipulated, more visually oriented individuals can easily see and engage with data within the format of the spreadsheet. The rows and columns format also makes it simple to not only create metadata but also to change metadata over time as a dataset grows. 

4. Inference and Exploratory Analysis

Before a data science project begins it is important to perform an exploratory analysis in order to learn more about any potential findings within the dataset. Microsoft Excel has multiple functions programmed into the software which makes it simple to explore a dataset through sorting, filtering, and pivot tables. Tools like Scenario Manager also make it easy to create and compare hypothetical scenarios across datasets. In addition, you can use charts to create data visualizations that can also be used to make inferences about a hypothesis and to see potential patterns or trends emerge from the initial mapping or graphic representation of the data.

5. Programming Language Compatibility

With all of that being said, working with Microsoft Excel does not preclude you from using programming languages. Data science professionals that are committed to writing code and queries can use SQL, Python, R, and many other programming languages to manipulate data within Microsoft Excel. Incorporating programming languages into Excel is especially useful for performing more complex analyses or working on larger data science projects.

Need more reasons to use Microsoft Excel?

Offering just a few of the reasons why data scientists should use Excel, taking one of Noble Desktop’s Microsoft Excel courses may give you even more reasons! In addition, Noble Desktop’s data science courses also offer even more data science tools. For students and professionals on the go, you can take live online data science classes through Noble Desktop or one of its affiliate schools. You can also find in-person data science classes in a city near you.