What is so great about analyzing data in Excel? Well, Excel has been around since the 1990s, way before sophisticated Business Intelligence tools like Power BI and Tableau. So, people trust Excel, are familiar with it, and can start with their analyses without spending time learning a new tool. Since, Many people are more comfortable working with Excel. So, In this post we will learn the basics of Excel, one of the most popular data analysis tools, to help visualize and gain insights from your data analysis using excel.
For instance, The fundamental goal of data analysis is to understand the data and derive meaningful, actionable insights. Excel can achieve this at affordable prices. So, The new enhancements by Microsoft have definitely contributed to the popularity of Excel. Excel is also widely used for its powerful analytical capabilities.
Data Analysis Using Excel with Techcanvass
Hence, We will be covering various features and functions which make Excel a powerful data analysis tool. Use the telecom churn dataset from Kaggle in order to put the concepts into practice. Since, Most of the concepts are using this dataset for examples. Churn indicates a customer leaving the service to join another service. All businesses want to prevent churn and retain their customers. So, this is an essential metric in all industries. The snippet of the data looks like this:
Powerful Features Excel offers to Analyze Data
Let us look at the key analytical features of Excel that aid in powerful data analysis.
Pivot tables are a powerful tool in order to interactively and quickly group, aggregate, filter, and visualize data with simple drag and drop and no formulae. They are easy in order to change as per the requirements. Since, The pivoting draws attention to valuable information, which is fundamental to data analysis. A simple refresh will update all values when new data is added to the table you are analyzing.
Select the data you want to work with and Insert -> PivotTable. You can check out the example pivot analysis here.
This was called “Ideas” earlier, and this feature empowers you to ask questions in English and understand the data using a natural query language. You don’t need to spend time writing complex formulae. Visualizations are also displayed for the query you ask using the best suitable chart.
You can also see ‘Analyze Data’ under the Home tab. So, Excel will provide suggested questions, or you can type in a specific question.
For instance, It is a free add-on used to perform complex statistical or complex engineering analyses. So, You only need to provide the correct range of data on which you want the function to be applied. hence, You don’t need to know the underlying formulae. Excel will also select appropriate functions and display the results in tabular and/or graphical forms.
You need to load and activate the Toolpak using the File -> Options -> Manage -> Excel Add-ins.
Power Query (Get & Transform)
For instance, It is an Excel add-in that can be used for data discovery, cleansing, transforming, and combining data from different sources. It prepares data for further analysis. In this image, you can see Returns2. Returned is a column from another table.
Other Useful Excel Features to Analyze Data
You can sort data on multiple columns for analysis. In this example, since Salary is sorted in descending order within State sorted alphabetically. So, we can now compare data state-wise and salary-wise.
A Filter not only allows you to select data but also shows you the unique values, blank values if any.
Excel has many functions that help in data cleaning, sorting, analysis, and much more. Some common examples are:
|Detect missing values and will give the count of blanks.
|Combine the values of several cells into one cell.
|CONCATENATE(text1, [text2], …)
|Display the length of the string.
|SUMIFS(), MAXIFS(), AVERAGEIFS()
|Perform sum, max, and average using a criterion.
|SUMIFS(sum_range, criteria_range1, criteria1, [criteria_range2, criteria2], …)
|Return the rank of a numerical value, which is vital during analysis to understand where the value falls in the range.
|RANK(number, ref, [order])
|Combine data from multiple tables based on some common field.
|XLOOKUP(lookup_value, lookup_array, return_array, [if_not_found], [match_model], [search_mode])
|Replace the #VALUE errors with any text/value. Useful when displaying data to the end-user.
Since, You can visualize data using data bars, color variations, and icon sets depending on the cell’s value. In this figure, you can see some options and data bars. When you have large amounts of data, this is helpful to emphasize unusual values and identify patterns.
For instance, You can use the built-in conditions or create your own. So, You can apply conditional formatting to a selected range of cells, an Excel table, or a pivot table.
You have the option in Data -> Remove Duplicates, and we can also choose the column names that we want to check for duplicate values. It is an essential task in data cleaning.
Creating data visualizations is very easy using Excel. Analyzing rows and rows of data is much easier with a chart! Excel provides a variety of charts for your analysis.
Different Kinds of Data Analysis Using Excel
Excel tables also help to manage and analyze datasets systematically with headers and alternate shading or banding of rows. since, They have structured references and dynamic ranges, which aid in data analysis. so, You can convert data in Excel to a table by clicking any cell in the data and Ctrl + T.
What-If Analysis in Excel allows you in order to compare the outcome results based on variable changes. So, The goal seek functionality helps by mathematically adjusting a single variable to reach the desired goal, and there are many other features. This option is in the Data tab.
Since, Many other tools in the market for much deeper analysis like Python and R, Tableau, and Power BI are available. But Excel won’t go away. So, For many people, Excel is the first tool they learn. hence, Its ease and flexibility give users a comfort level. So, Excel will be the go-to tool for many organizations and people starting their journey in analytics.