Microsoft Excel is one of the most popular and widely used tools for data analysis. It has many functions and features to analyse, visualize, and interpret the data. Excel can work on the small datasets or large datasets to gather all of the information. It has lots of potential to convert raw data into well-structured data which provides better insights as compared to other appliances.
Earlier we have discussed the process of data analysis using Python. Similarly today, we will explore effective methods of data analysis using Microsoft Excel and also spotlight on key features, and techniques for the best analysis.
Organizing and Cleaning Data
The very first step we need to done is to analysis the data and understand what we want from it. It is essential to ensure that our data should be clean, trim and well-organized so that quality of data is good and we can easily understand its features. But if the quality of data is poor then it can lead to inaccuracy where we unable to predict the results.
Followings are the key options we can use for organizing and cleaning of Data in MS Excel:
- Removal of duplicate values from the data: First we need to use the “Remove Duplicates” option under the “Data” tab to remove the duplicate values from the data. By these, unique values reflect in the data.
- Data Validation: Using data validation, we change or edit the type of data entered in cells. For example, we can edit a column to make it into only numbers, dates, or text values.
- Find and Replace: With Find and Replace option we find errors like “#name?” Or replace it with null values or correcting incorrect data.
- Trim and Clean: This is useful in cutting the extra spaces in the beginning, between the text data.
Using Excel Functions for Data Analysis
Excel has a many of options, commands and functions which help us to analyze data better. The following functions are mostly used for data analysis are:
- SUM, AVERAGE, COUNT, MIN, MAX: These basic functions used to perform mathematically in data like statistics. From this we get know the total values, averages, counts, or find the minimum and maximum values in a range.
- IF and IFS: Use of the “IF” function is to make logical comparisons in the data. The “IFS” function on the other hand allows to make more than one logical comparison. For example, we can check whether a value meets the logics we apply on them or not and then gives one value if the condition is true and another if the condition is false.
- VLOOKUP and HLOOKUP: These functions are used to search for a value from a table to another table of another data. “VLOOKUP” is used for vertical searches, while “HLOOKUP” used for horizontal.
- INDEX and MATCH: The “MATCH” functions can be used with the VLOOKUP and HLOOKUP. We use “INDEX” when we need to search in both directions vertically and horizontally mostly used in large datasets.
- SUMIF, COUNTIF, AVERAGEIF: Analysts use conditional functions in data like some logical and mathematical. For example, “SUMIF” adds up numbers with condition, and “COUNTIF” counts repeated values in the data.
Pivot Table: The Powerhouse of Data Analysis
Pivot Table is the one of most powerful mostly used in reports and dashboards to provide good visualisations in the Excel. It provides the dynamically summarize of large datasets so, that we can extract key feature from the insights with just a few click.
Creating a Pivot Table:
- To make a pivot table, we need select the data range and go to “Insert” then “Pivot Table.”
- In the Pivot Table there is field list, we need to drag and drop fields into rows, columns, values, and filter to maintain the data accordingly.
- Pivot table automatically calculate sums, averages, counts, and other measurements of the data.
- Grouping Data by options such as months, years, or numeric ranges.
Filtering and Slicing Data: we can filter data by adding filters to rows or columns. We can also use slicers to filter data with the change of graphs.
Pivot tables can gives better understanding of the data in order for discover patterns, summarizing large datasets in small dataset, and making comparisons between the different dataset.
Using Charts and Visualizations
Data visualization plays a crucial role in understanding and identify the patterns and trends in the data. Excel provides a wide variety of charts and graphs that can help analyse data. You can get more insights and well presentable in the reports.
- Column and Bar Charts – good to display categorical data such as sales of companies or population by countries.
- Line Charts – very useful for date related visualization.
- Pie Charts – are used to show insights in percentage of a whole.
- Scatter Plots – useful to identify patterns, trends, and correlations between variables. It displays this data as data points by as dots.
- Conditional Formatting: used to visually highlights the data points based on certain logics with conditions. For example, you can use highlight the data values which is more than 100 or less than 50 in dataset.
- Excel also provides to customize charts, and add titles, and labels.
Automating Analysis with Macros
In Excel’s macros can save a lot of time and effort for the tasks like working on repetitive mode. Macros are the scripts that automate tasks, allowing you to perform complex operations with a single click or we can make short key.
- Recording Macros: Excel provide us to record a command which works on the sheet repeatedly as a macro. To record a macro, go to “View” then, “Macros”, “Record Macro.” This record macro done by writing the codes on (VBA) VISUAL BASIC APPLIANCE the actions we want to automate.
- Running Macros: After macro is recorded, we can run it any time to repeat the same commands.
Best Practices for Effective Data Analysis
- Always make sure that the data is accurate.
- Clean and trim in data is essential for better understanding and meaningful insights.
- To make summary of the data which consists of the calculations, formulas, and pivot tables which help to summarised the data and dashboard.
- To make reports the data contains charts, graphs, and tables for better visualization for better analysis of the data.
You can understand and learn more on the discussed practices under our courses on data science and analysis. There are multiple programs for students and learners like you.
Conclusion
Microsoft Excel is an exceptionally powerful tool that is not only used to analysis the data but also many other uses. It is giving us a large scale of functions, tools, and features to help manipulate, analyze, and visualize the data. Summarizing data with functions, pivot tables, and automating repeated actions with macros. It has a lot of potential which make it an effective and efficient analyst.
Tools in Excel can turn raw data into valuable data and give the best insights which leads to better and quicker decision-making. So, start learning Microsoft Excel if you haven’t started yet.
