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Data analysis and visualization using spreadsheets | designcareersclub

Data analysis and visualization using spreadsheets

Information is considered a precious commodity in the age of digitization. There is a bombardment of information every day; hence, the need to prepare data and evaluate a data source effectively is required.

Analyzing and visualizing data using spreadsheets is a highly sought-after skill in the workforce. Using spreadsheets for data analysis can bring data to life using custom models. Plain sheets offers thousands of sheet templates you can use for data analysis. Work will be more efficient, automation possible and create data models for effective data linking.

This article will discuss how to use Excel spreadsheets for data analysis, analysis methods, and the formulas or functions needed to maximize the use of spreadsheets for data analysis.

Analyze data using Microsoft Excel

Microsoft Excel is a popular data analysis program. Data analytics is a process where raw data is cleaned, transformed, and analyzed to obtain actionable and relevant information for making wise business decisions.

Data analysis is also about Excel power tools, data collection, organization, tools, techniques and storage. Data analytics can transform raw data into statistical data and meaningful information for businesses.

You must first be comfortable using the Excel user interface to code formulas and features. You can then easily navigate the spreadsheet to view the data.

Data analysis can be presented in various visual formats, such as Excel diagrams, graphs, tables and images. Excel’s built-in pivot tables are a popular data analysis tool.

Several Excel functions can be used to analyze data, such as Conditional Formatting, Data Model, Financial Function and Analysis, Power Pivot, and more. Some of these features will be further discussed in this article.

5 Excel data analysis features and formulas

Data analysis is a challenging process to perform. It often involves using advanced Excel functions to develop the correct analysis.

Here are some advanced Excel functions you can use to interpret data in Excel:

1. Concatenated formula

The concatenated formula can feel overwhelming if you hear it for the first time. On the contrary, this formula is powerful and simple to understand and use.

This formula combines data from multiple cells into a single cell. The formula is =CONCATENATE(data range).

2. IF functions

There are several IF functions that are heavily used in data analysis. As a data analyst, you will use IF functions on a regular basis. It’s best to familiarize yourself with the common IF functions below:

SUMIFS is a familiar and commonly used formula in data analysis. This feature is not just about adding values ​​in a range of cells. The SUMIF function is used when adding data based on multiple criteria. The formula is =SUM PLUS IF(sum_range, range1, criteria1, [range1],…].

This formula is used to get the average value based on one or more parameters. The formula is =AVERAGEIF(average_rng, range1, criteria1,…].

This powerful Excel function counts values ​​that can satisfy a variety of conditions. This function does not require a sum range. The formula for is =COUNTIFS(range, criteria).

3. VLOOKUP and HLOOKUP functions

The VLOOKUP or Vertical Lookup function is used when looking for a specific value in one column and to return the value from another column but in the same row. The formula is =VLOOKUP(search_value, table_array, column_index_number, [range_lookup]).

The HLOOKUP or Horizontal Lookup function looks for a particular value in the top row of a data table and returns the value from a row in the same column. The formula is =HLOOKUP(search_value, table_array, row_index, [range_lookup]).

4. Search and search functionality

You can use the find function when returning the position of one text string within another. If coded incorrectly, it can throw a #value error. The formula for the lookup function is =FIND (find_text, within_text, [start_num]).

The search function, on the other hand, is used to search for anomalies and unique identifiers. For example, if you’re looking for a particular word like « Small, » the results might be « SMALL » or « small. » The formula for the lookup function is =SEARCH (find_text, within_text, [start_num]).

5. Rank function

The rank function is one of the oldest Excel functions that you can use for data analysis. The sort function can quickly show you how values ​​in a dataset are ranked in ascending or descending order. In a business setting, this feature can be used to track which customer has placed the most orders in a month or year.

5 methods for analyzing data in Excel

An Excel spreadsheet is versatile; you can apply different methodologies for data analysis, whichever one you find convenient to use. Below are the five common methods used for data analysis using Excel spreadsheets.

1. Data visualization

Using data visualization helps make your spreadsheet more visually appealing. You can even create awesome interactive dashboards. Charts can be edited, adjusted, and provide legend location and data labels.

Excel spreadsheet allows you to create different types of charts such as line chart, column chart, scatter textureand more.

2. Conditional Formatting

Conditional formatting helps highlight cells that are considered important. With conditional formatting, you can apply different colors to a cell, change the font, or add symbols next to the cell based on predetermined criteria.

Conditional Formatting can help you with data bars, icon sets, finding duplicates, comparing lists, checklists, and creating heatmaps.

3. Pivot tables

Pivot tables are considered the most practical method of data analysis. It can be used to access essential data from a huge data set. Using pivot tables allows you to summarize data and create dynamic reports.

4. Data validation

Always encode valid data values ​​to avoid displaying error messages on the spreadsheet. Setting data validation values ​​for a particular cell is possible and useful for data analysis. You may see a message prompting users for acceptable data values ​​to encode, which can speed up data validation.

5. Data cleansing

Before you start analyzing your data, you need to cleanse and organize the data collected from multiple sources. You can clean up data in Excel using three approaches: using text functions, containing data values, and containing time values.

Final thoughts

Working with spreadsheets can be repetitive and time consuming. However, Microsoft Excel has undergone several updates to improve user experience. Some of the significant updates to Excel include Power Pivot and Power Query.

Analyzing and visualizing data using spreadsheets has proven to be beneficial in various industries. Data analysis isn’t easy, and taking classes or short courses in Data Science can help you advance your spreadsheet skills from basic to advanced.

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