Is Excel necessary for data science?

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Microsoft Excel has been around for over three decades yet it is still very valuable. Excel’s original concept hasn’t changed so much from what we have today, just that Excel now looks better and has lots of new capabilities to catch up with today’s data requirements.

Is Excel necessary for data science
is Excel necessary for data science

Excel is such a powerful program, especially with the latest versions, which allow users to retrieve data from the web (using APIs), run simulations (using add-ins), and even create batch jobs (using macros). With these useful Excel features, the pertinent question remains, “Is Excel Necessary for Data Science?”

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The answer to this question is No. Excel is not necessary for data science. Data scientists and analysts can still handle and process data effectively without using Excel. However, while Excel may be dispensable and unnecessary for data science, it remains a useful and relevant tool for any data scientist or analyst.

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Yes, Excel is not the only or most fitting solution for all data projects, but it remains a reliable and affordable tool for analytics. To data, Excel remains an essential base structure for intelligent data because Excel deepens one’s understanding of the data analytics process. Excel is also a smart way to extract actionable insights from data.

Excel can be beneficial in low-level data analyst type roles, which generally do not have mature data analysis processes or structures. Excel can also be used to view a small CSV or run some offhand calculations quickly. 

Several data scientists use Excel to perform basic data analysis tasks. This is due to their preference or their workplace specifics. But the more experienced data scientists will use a more optimized tool such as Tableau, R, or Python.

This is because Excel misses useful features like Reproducibility, Version control, Testing, Numerical accuracy, etc. Nevertheless, Excel can handle a small amount of data in comparison to what other platforms can take.

In a nutshell, Excel is generally not something most employers would want a ‘data scientist’ to use. The general expectation is that all data analysis or pipelines should be performed in R, SQL, NoSQL, Python, or even Java. Knowing how to handle all data analytics in Excel isn’t going to hurt you, but it also won’t get you a good data science job.

What Is Data Science?

As stated above, while Excel is not necessary for data science, it can also be used for data analytics. Knowing what data science is will enable you to understand how Excel can be useful for data analytics. So, what does data science involve?

Data science is an interdisciplinary field. It uses algorithms, scientific methods, processes, and systems to extract knowledge and insights from structured and unstructured data. Data science relates to data mining, machine learning, and big data.

As seen above, data science requires the use of powerful analytical tools. Although Excel is beneficial for making various charts and tables initially, the programs with which data scientists later work are SQL, R programming, Python, etc. 

Hence, knowing Excel is essential for data scientists, but data scientists should not rely upon this knowledge alone. Instead, they should get classes on R programming, Python, and many more.

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Do Data Scientists Use Excel?

Yes, data scientists use Excel, even experienced scientists. Some professional data scientists use Excel either due to their preference or due to their workplace and IT environment specifics. For instance, many financial institutions still use Excel as their primary tool, at least, for modeling.

In its most basic form, an Excel spreadsheet holds data points in each of its cells. Things such as SKU, raw data exports, date of sales, and units sold are entered (or imported) into a spreadsheet to allow for more comfortable viewing and organization purposes. 

Excel organizes raw data into an easy-to-read format that makes it easier to get actionable insights from the data. Excel also enables users to customize fields and functions that make calculations of more complex data possible.

Excel makes it possible to carefully study and visualize bigger datasets without the need for other software. 

While Excel is a useful tool for simple data science, aspiring data scientists must learn a proper programming language for data science, such as R or Python. Understanding and using Excel alone is not sufficient for any serious person aspiring to become a data scientist.

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Is Excel Important for Data Science?

Yes, Excel is essential for data science. Excel is suitable for simple data science tasks concerning spreadsheets. It emphasizes more on presentation and ease of use while having minimal support for actually analyzing the data. 

Unless you are only interested in calculating simple statistical measures like mean, average, etc. or building straightforward models like linear regression, the use of Excel is not sufficient for any data analyst. However, since most companies have to deal with simple tasks concerning data, they can utilize Excel to manage their data.

However, data science involves many other classifications and complex models that Excel can’t handle. Therefore, while Excel is vital for data science, data scientists need tools that are more useful to them like R, Python, etc., which also have libraries with tons of in-built models.

Most non-technical and young data scientists often use Excel as a database replacement, which is not professional. No experienced data scientist would rely on Excel as his/her primary tool, except when looking at data for the first time. Hence, it is incredibly easy to make Excel mistakes, even when errors occur in other applications. Excel aggravates the situation even more.

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10 Places Where Excel Is Used

Excel is used very widely nowadays in almost all industries and by everyone because it is useful and saves time. It has been in use for quite a few years, and it gets upgraded every year with new features. 

An exciting thing about MS Excel is that you can use the software anywhere and for anything. For example, for mathematical calculations, billing, data management, data analysis, inventory, finance, business tasks, complex calculations, etc.

Excel has lots of tools and functions that make it easy to work with and very useful. Here are 10 Uses of Excel:

1. Data Entry and Storage

Excel is a perfect tool for data entry and storage. With a large space for data storage (1,048,576 rows and 16,384 columns), it’s evident that Excel can store lots of data. Its excellent spreadsheet makes it easy for data to be inputted and viewed. Users can even create customized data entry forms tailored to their specific needs. 

2. Data Analysis

Data analysis is an essential function of Excel. Data analysis involves analyzing collected data to inform decision making. It converts data into something useful for the owner who owns the data. At its core, Excel is an excellent tool for data analysis.

3. Return on Investment

Tracking total business sales and expenditures with Excel allows you to see your Return on Investment for each marketing strategy or campaign. Excel makes it easy for users to track their ROI. If you follow your sale in Excel, you’ll be able to see your profit, how long it takes you to reach break-even, or additional profit levels from an ad campaign.

4. Accounting and Budgeting

Excel contains many formulas and functions that are both useful and easy-to-use for accounting and budgeting. The in-built formulas are handy for organizing and synthesizing results.

5. Trends Identification

When representing data in pictorial forms like graphs and charts, adding an average line can be very helpful. This addition gives explicit details of the key trends coming from the data. Excel includes average lines and allows trend identifications. This trend line extends beyond the graph to provide useful predictions for future activities, such as business forecasting.

6. Business Data Collection, Verification, and Cleaning 

Businesses often use multiple systems (i.e., CRM, inventory), each with its database and logs. All these can be easily exported into Excel for easy access. Also, users can use Excel to clean up data to remove duplicate or incomplete entries.

7. Data Visualization

With Excel, users can use formulae across a grid of cells to unlock their data’s potential. Data can be input into individual cells, sorted and filtered, and displayed in visual forms. 

With graphs, pie charts, and clustered columns, more meaning can be added to the data apart from just presenting data in a series of rows. Data visualization adds additional emphases to marketing materials and business reports.

8. Administrative and Managerial Duties

Excel can be useful for administrative and managerial duties by creating and outlining business processes. Excel helps in process optimization and is very useful for organizing scenarios and procedures.

9. Bringing Data Together

Excel can also effectively assemble data from various documents and files to be in one place. More so, raw data and information, including images, can be imported into Excel from other spreadsheets.

10. Scheduling

Businesses usually create schedules for employees and resources with Excel. This schedule can be designed with color codes and also programmed to auto-update as the schedules change. You can make weekly worksheets with headings for each day, and include the work shifts or hourly slots. All you must do is fill out each slot with the resources or employee name for a given day.

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Places Where Other Systems Should Be Used

Despite the many uses of Excel, Excel cannot do everything related to data processing. Even when using Excel for data analysis, it’s only in rudimentary form. Hence, there is a need for more advanced tools to be used in some areas of data processing. 

Excel is a fantastic tool. Nevertheless, people often use Excel in other ways. Here are places where other tools, more advanced than Excel, should be used:

Excel Is Not a Database

Excel is not a good database since it cannot be properly de-normalized, and its relational capabilities are basic. Excel can handle just over a million rows of data. Still, unless you’ve got a powerful computer, you’ll start seeing performance issues at less than a 10th of that, especially if you’ve got formulae.

Use Excel to get your data structure right and then get a better tool to build your database.

Excel Should Be Used to Build Forms

There are so many reasons why Excel should not be used to build forms. For instance, data used in forms is disconnected from any database. Also, most Excel forms are simply spreadsheets with empty cells to input data. As a result, Excel forms are only slightly better than a paper form.

Also, the format and layout of Excel forms are cumbersome and time-consuming. Designing a user-friendly Excel form is a difficult task. Excel forms usually have limited means of controlling and validating input, which results in bad data capture.

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Excel Is Not a Project Management Tool

Most times, people use Excel for planning projects – small-size to mid-size projects. Although Excel has in-built templates for project planning, these templates are best suited for small and straightforward solo projects that are just a schedule of tasks and dates.

However, Excel cannot handle complex projects well and can become a densely-packed, color-coded nightmare for everyone else except the person who created it. Also, manually updating statuses and generating the required reports in Excel takes more time than the actual work. 

Hence, dedicated project management software allows users to visualize and update the entire planning process, reporting, and monitoring a project in real-time. 

Excel is Poor for Big Data Analysis

Users began to use Excel for big data analysis when Microsoft introduced power Pivot in MS Excel 2010. Power Pivot enables users to analyze data sets far beyond Excel’s historical maximum of 1,048,576 rows. 

However, the steep learning curve and inadequate understanding of the technology involved can cause misuse of Power Pivot’s OLAP CUBEs for data analytics. Business firms that understand the challenges inherent in using Excel for big data analysis would prefer to rely on more dedicated business analytics solutions like Domo, Adaptive Insights, or Tableau.

Excel Is Not a Programming Tool

Excel is useful for doing Office operations for those handling non-development roles. Excel can handle maintaining to-do lists, organizing, and sharing data. Excel is not a programming language or tool like Python; a general level interpreted programming language. 

Although one can boost Excel’s capabilities using VBA Macros, Power BI, etc. There are dedicated tools that can better handle programming than VBA macros.

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Excel is Poor for Big Data

Anything at Big Data scale or even just millions of rows cannot be done in Excel without using some third-party tools. Whereas, Python and some other programming tools can handle these in multiple manners depending on your requirements. Excel would start running into problems even before you hit this scale -like by default, it will try to Autosave things every few minutes.

In a nutshell, Excel can be a handy tool for exploratory analysis and drawing Pivot tables. Still, you need Python for a production environment or doing any reproducible analysis or when the data is running in millions of rows.

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Excel Is Poor for Machine Learning or Deep Learning

Excel is not the tool you can use for any advanced predictive modeling, machine learning, or deep learning. Use Python instead.

How Important Is the MS Excel Skill in Data Science?

The fact that Excel isn’t necessary for data science doesn’t mean that having skills in Excel is not essential for a data scientist.

Even though Excel isn’t a top resume-building skill for data scientists, it would be a pity if data scientists did not learn Excel’s ins and outs. Aside from the apparent native features of Excel, which include handling statistical and mathematical functions and formulae very well, Excel is now also a good data management and programming tool, thanks to VBA and xlwings, which enables users to leverage the power of Python in Excel.

With VBA and xlwings, Excel is now an excellent tool in the hands of data scientists. Data scientists can now create from Excel-based neural network to Monte Carlo simulations, to anything else involving programming.

So, go ahead and master the basics of named ranges and filtering, then move on to more advanced level features such as pivot tables and conditional formatting. However, Excel has its limits, so don’t push it. For more dedicated work, rely on R or Python.

Is Programming Needed for Data Science?

Yes, programming knowledge is necessary for data science because, as far as data analytics is concerned, programming cannot be eliminated. Programming skills enable data scientists to write meaningful programs for querying and retrieving data from different databases and writing programs that can run datasets on machine learning algorithms.

The importance of a sound knowledge of programming for data scientists cannot be overemphasized. Over the years, specific technical programming skills, such as Python coding, JavaScript, R programming language, Perl, HTML or C/C++, Spreadsheet tools (such as Excel), and SQL Coding have come to stay in the field of data analysis.

Finally, with the evolution of more robust data platforms, some of them can be used outrightly without the need for programming. However, it still surely helps data scientists have programming skills since this skill complements data analytics since it increases the breadth and depth of data and separates data scientists from statisticians or the traditional business analyst.

Note: No doubt, programming is an essential skill for a data scientist job, but that does not mean you need to be a die-hard programmer to pursue a career in data science. Good programming skills are highly preferred for data scientists, although it’s not mandatory.

Here are a few programming skills that are necessary for data science:

Python programming language:

Python programming language is a highly referred programming language with many useful features and packages written for it. Such packages include scikit-learn, scipy, matplotlib, pandas, and NumPy. Other valuable and interactive options to Python include ipython, ipython notebooks, ggplot, and Seaborn.

R programming language:

This is an excellent programming language and software platform that comes with packages such as ggplot2, dplyr (or plyr), ggally, plot ggpairs, and a GGplot2 Matrix reshape2.

JavaScript and HTML:

These are web development languages that convert static visualizations into interactive ones, creating online dashboards and reports. Javascript packages include D3.js, AJAX implementation, and jQuery.

C/C++: These are low-level programming languages that assist in turning high-level development codes such as Python and R into efficient production codes ready for deployment.

Suppose you currently lack these programming skills and am still looking to start your data science career. In that case, you can register for a data analytics certification course at Boti, where you will learn these programming skills practically from industry best experts.

Is Big Data Necessary for Data Science?

No, Big Data is not necessary for Data Science. Though there may not be too much difference between both, both are different fields and do not interrelate.

Data science can be seen as an evolutionary extension of statistics. It is concerned with large datasets with the help of computer science technologies.

As such, big data deals with the vast collection of heterogeneous data (unstructured, semi-structured, and structured data) from different sources.

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Excel is not necessary for data science but essential for data scientists. Excel is a tool for data analytics but not always a complete solution. Hence, get started with Excel to see what you can do with data but use different functions to enable data exploration for better insights.

If you intend to follow a data science career path, start with Excel, and then learn advanced tools like R and Python as soon as possible. To kick-start your journey into data science, get in touch with us today.


Luis Gillman
Luis Gillman

Hi, I Am Luis Gillman CA (SA), ACMA
I am a Chartered Accountant (SA) and CIMA (SA) and author of Due Diligence: A strategic and Financial Approach.

The book was published by Lexis Nexis on 2001. In 2010, I wrote the second edition. Much of this website is derived from these two books.

In addition I have published an article entitled the Link Between Due Diligence and Valautions.

Disclaimer: Whilst every effort has been made to ensure that the information published on this website is accurate, the author and owners of this website take no responsibility  for any loss or damage suffered as a result of relience upon the information contained therein.  Furthermore the bulk of the information is derived from information in 2018 and use therefore is at your on risk. In addition you should consult professional advice if required.