Excel Example for Data Analyst – Count cells that contain errors

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Excel is a powerful tool for data analysis, and it has many built-in features to help you work with your data effectively. One of these features is the ability to identify cells that contain errors, such as #DIV/0!, #N/A, or #VALUE!.

These errors occur when a formula or function can’t be calculated correctly, and they can cause problems when you’re working with your data. For example, if you’re trying to calculate the average of a range of cells and one of them contains an error, the entire calculation will be incorrect.

But don’t worry! Excel makes it easy to identify cells that contain errors, so you can fix them quickly and get back to your data analysis.

Here’s how:

  1. First, select the cells that you want to check for errors.
  2. Next, go to the “Home” tab in the ribbon and click on the “Conditional Formatting” button.
  3. From the drop-down menu, select “Highlight Cells Rules” and then choose “Formula Is.”
  4. In the formula field, enter “=ISERROR(A1)” (without the quotes). This formula says to highlight cells in the range A1 that contain errors.
  5. Click “OK” and the cells that contain errors will be highlighted in red (or a color of your choice).

 

That’s it! Now you know how to identify cells that contain errors in Excel.

It’s important to note that you can use this technique to check for other types of errors, too. For example, you can use the “ISNA” function to identify cells that contain the #N/A error, or the “ISNUMBER” function to identify cells that contain the #VALUE! error.

In conclusion, if you’re a data analyst, Excel is an essential tool for your work. By using the “ISERROR” formula and conditional formatting, you can quickly and easily identify cells that contain errors, so you can fix them and get back to your data analysis. This will help you make sure your calculations are accurate and your data is reliable, so you can uncover valuable insights and make informed decisions.

Excel Example for Data Analyst – Count cells that contain errors

Loader Loading...
EAD Logo Taking too long?

Reload Reload document
| Open Open in new tab

Download PDF [392.68 KB]

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!