Microsoft Excel is one of the most popular and widely used tools for data analysis. It has many functions and features to analyze, visualize, and interpret the data. Excel can work on small or large datasets to gather all 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 discussed the process of data analysis using Python. Similarly today, we will explore effective methods of data analysis using Microsoft Excel. We will also spotlight on key features and techniques for the best analysis using MS Excel.
Organizing and Cleaning Data
The first step we need to do is to analyze the data and understand what we want from it. It is essential to ensure that our data is clean, trimmed, and well-organized so that the data quality is good. Clean data is easy to understand its features. But if the quality of data is poor then it can lead to inaccuracy where we are unable to predict the results.
Following are the key options we can use for organizing and cleaning 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 are reflected 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 the Find and Replace option we find errors like “#name?” Or replace it with null values or correct 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 many options, commands, and functions which help us to analyze data better. The following functions are mostly used for data analysis:
- SUM, AVERAGE, COUNT, MIN, MAX: These basic functions are used to perform mathematically in data like statistics. From this, we get to know the total values, averages, and counts, or find the minimum and maximum values in a range.
- IF and IFS: The 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 logic we apply on them or not and then give 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” is 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 conditions, 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 visualizations in Excel. It provides the dynamically summarize of large datasets so, that we can extract key features from the insights with just a few clicks.
Creating a Pivot Table:
- To make a pivot table, we need to select the data range and go to “Insert” and then “Pivot Table.”
- In the Pivot Table, there is a field list, we need to drag and drop fields into rows, columns, values, and filters to maintain the data accordingly.
- The pivot table automatically calculates 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 give a better understanding of the data in order to discover patterns, summarize large datasets in small datasets, and make comparisons between the different datasets.
Using Charts and Visualizations
Data visualization plays a crucial role in understanding and identifying the patterns and trends in the data. Excel provides a wide variety of charts and graphs that can help analyze data. You can get more insights and be well presentable in the reports.
- Columns 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 into the percentage of a whole.
- Scatter Plots – useful to identify patterns, trends, and correlations between variables. It displays this data as data points as dots.
- Conditional Formatting: used to visually highlight the data points based on certain logic with conditions. For example, you can highlight the data values which is more than 100 or less than 50 in the dataset.
- Excel also allows you to customize charts, and add titles, and labels.
Automating Analysis with Macros
Excel’s macros can save a lot of time and effort for tasks like working in repetitive mode. Macros are the scripts that automate tasks, allowing you to perform complex operations with a single click or we can make short keys.
- Recording Macros: Excel allows us to record a command that works on the sheet repeatedly as a macro. To record a macro, go to “View” then, “Macros”, and “Record Macro.” This record macro is done by writing the codes on (VBA) VISUAL BASIC APPLIANCE the actions we want to automate.
- Running Macros: After the macro is recorded, we can run it at 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 a summary of the data which consists of the calculations, formulas, and pivot tables which help to summarize 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 about the discussed practices in 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 analyze the data but also many other uses. It gives 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 makes 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.
