Excel Data for Analysis (Free Download 11 Suitable Datasets)

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Excel is one of the most extensively used tools in data analysis. With proper data for analysis, various features, functions, and user-friendly interface of Excel make managing, organizing, and visualizing data efficiently.

In this Excel tutorial, you will find 11 ideal datasets for data analysis in Excel. These datasets cover a wide variety of fields such as sales, finance, management, marketing, research, etc.

What Are Excel Datasets?

Excel datasets are a collection of information stored in worksheets of Excel in a structured way. Each row in a dataset represents a single observation and each column represents a variable. Thus each cell represents a piece of information about the observation. This information can be numerical, text, dates, or any other type of data.

What Are the Sources for Sample Excel Data for Analysis?

Users can find Excel datasets from the following sources:

  • Online platforms: Various online database platforms such as government websites or public databases are the most common data sources for analysis. This type of data is available in xlsx or CSV file format. You can also use various Excel functions and features to import data from these platforms.
  • Built-in Excel Samples: If you go to the Home option of the File tab, you will find various built-in templates. These templates come with some built-in data that we can use for analysis.
    Built in Templates in Excel
  • Data Generation Tools: Some online platforms help you generate fictional data when you provide your necessary variable or field names. After generating the required data, you can copy the data and paste it into your worksheet.

11 Ideal Excel Datasets for Analysis

You have to apply various features and formulas to extract, organize, and visualize data while performing data analysis in Excel. To get a meaningful result after applying such formulas and features, you need proper Excel data samples.

Here are 11 suitable data samples for analysis in Excel:

1. Supermarket Sales Data

The Supermarket sales data provides details on customers, products, and various specifications for each customer order.

Here, is the list of variables our dataset contains for each order:

  • Order No. – A distinct identifier for every order.
  • Order Date – The date on which an order is placed.
  • Customer Name – The name of the customer who placed the order.
  • Shipment Date – The date on which an order is shipped to the customer.
  • Retail Price – The price of the product sold.
  • Order Quantity – The number of products ordered by a customer in a single order.
  • Tax – A percentage of the purchase price paid to the government.
  • Total – Total price including tax paid by the customer.

You can  preview the supermarket sales data in the image below:

Supermarket Sales Data for Analysis in Excel

Here is a list of data analysis tasks you can practice with this dataset in Excel:

  • Task 1: Calculate average spending, average order quantity, and average shipment days for an order.
  • Task 2: Find the top 5 customer names and total values (based on total spending).
  • Task 3: Filter the date based on months and calculate the order quantity and average shipment days for each month. Then, show these in a column or bar chart.
  • Task 4: Apply conditional formatting to highlight customers who made repeated purchases in a specified period.

Download Sample Dataset


2. Employee Management Data

An employee management database contains various information about employees. The diversity of such datasets helps calculate various data analysis parameters.

In the employee management dataset given in this section, you will find the following variables:

  • Employee ID
  • Full Name
  • Department
  • Designation
  • Hire Date
  • Annual Salary

Here is a preview of the employee management data:

Employee Management Data for Analysis in Excel

Here are some data analysis tasks that you can practice for this dataset:

  • Task 1: Determine the average annual salary across all departments and for specific departments. Then, highlight the departments with the highest and lowest average salary.
  • Task 2: Find the top 5 employees based on the highest salary values.
  • Task 3: Show the salary distribution by designation for each department in a dynamic chart.
  • Task 4: Create a search box to find the employees who were hired in a specific month, after a specific date, or in a specified period.

Download Sample Dataset


3. Project Management Data

Project management datasets contain various information related to the tasks of every project. The sample project management dataset provided in this section contains the following variables:

  • Project Name
  • Task Name
  • Assigned to
  • Start Date
  • Days Required
  • End Date
  • Progress

The following image shows a preview of our given project management dataset:

Excel Data for Project Management Analysis

Here are some data analysis tasks to practice from the given project management dataset:

  • Task 1: Determine the overall progress of each project and show it in a dynamic gauge chart.
  • Task 2: Highlight or filter the tasks that are behind schedule based on the current date and task end dates.
  • Task 3: Create a dynamic Gantt chart to show the timeline of each task of a project.

Download Sample Dataset


4. Inventory Records Data

An inventory record dataset is suitable for data analysis practice. It contains comprehensive information on stock levels available and adjustments. Thus, users extract a great number of parameters while practicing data analysis.

The Excel dataset of inventory records provided in this section contains the following fields:

  • Product ID – A distinct identifier for each product in the inventory.
  • Product Name – Description of the product
  • Opening Stock – Quantity of the product available at the beginning of a specific period.
  • Purchase/Stock-in – Quantity of the product added to the inventory during the specified period.
  • Number of Units Sold – Quantity of the product sold during the specified period.
  • Hand-in-Stock – Quantity of product remaining in the inventory after sales.
  • Cost Price Per Unit – Cost of each unit product.
  • Cost Price Total – Total cost of the hand-in-stock products, calculated by multiplying the cost price per unit value and hand-in-stock quantity.

Here is a preview of the inventory records dataset:

Inventory Records for Data Analysis in Excel

To practice data analysis with this inventory records dataset in Excel, you can complete the following tasks:

  • Task 1: Calculate the turnover rate (units sold/stock-in) for each product. Then apply conditional formatting to highlight products with the highest and lowest turnover rates.
  • Task 2: Filter or highlight products with low sales and high stock levels.
  • Task 3: Filter or highlight products that need immediate purchase/stock-in.

Download Sample Dataset


5. Netflix Movies Data

The movie dataset provided in this section contains the following variables:

  • Movie Name
  • Release Year
  • Age Rating
  • Duration
  • Category
  • IMDb Rating

Here is a preview of the movie dataset:

Movie Data for Analysis in Excel

You can complete the following tasks to practice data analysis for this dataset:

  • Task 1: First, find out the average IMDb rating of movies for each genre. Then, plot a column chart or bar chart to compare the average IMDb ratings. Finally, highlight the genre with the highest IMDb rating.
  • Task 2: Find the top 5 movies and IMDb ratings.
  • Task 3: Filter the data based on decades and find which movies have the highest IMDb rating in each decade.
  • Task 4: Calculate the correlation between movie duration and IMDb rating.

Download Sample Dataset


6. Tokyo Olympic Data

The Tokyo Olympic data provided in this section consists of the Ranking of each team, Team name, count of Gold, Silver, Bronze, and Total medals. You can preview the dataset in the following image:

Tokyo Olympic Data for Analysis in Excel

Here is a list of data analysis tasks that you can perform with this dataset:

  • Task 1: Show the distribution of gold, silver, and bronze medals for each country in a chart.
  • Task 2: Highlight the teams that have a different ranking based on gold medals and total medals. Show the difference between these in a chart.

Download Sample Dataset


7. Technological Product Data

The technological product data provided in this section contains the following variables:

  • Brand
  • Device
  • Model
  • Country of Origin
  • Date of Release
  • Price

Here is a preview of the technological product dataset:

Technological Products Data in Excel

Here are some data analysis tasks that you can perform with the given technological products Excel dataset:

  • Task 1: Find all the unique brand names. Then, find the most expensive device from each brand.
  • Task 2: Find the list of unique origin countries. Then, calculate the average price of products originating from these countries. Finally, highlight the country with the highest-priced products on average.
  • Task 3: Create a search box to find all products within a specified price range.
  • Task 4: Calculate the average price of products for each brand. Then, show the average prices in a column or bar chart.

Download Sample Dataset


8. 2022 FIFA World Cup Performance Data

In our sample dataset, we have listed information on each player from the 2022 FIFA World Cup-winning Argentina team. Each row this dataset contains the following variables:

  • Player Name
  • Position
  • Jersey Number
  • Player Date of Birth
  • Club
  • Appearances
  • Goals Scored
  • Assists Provided
  • Dibbles per 90 Min
  • Interceptions per 90 Min
  • Tackles per 90 Min
  • Total Duels Won per 90 Min

You can preview the dataset from the image below:

2022 FIFA World Cut Data for Analysis in Excel

Here are some data analysis tasks that you can practice from the dataset given in this section:

  • Task 1: Filter the unique playing positions and calculate the average number of goals scored for each position. Then, visualize the data using a column or bar chart.
  • Task 2: Filter the players into various age groups (e.g. older than 30, between 25 and 30, under 25) and calculate the performance difference in terms of goals scored and defensive contributions.
  • Task 3: Determine the correlation between the player’s age and the number of goals scored or assists provided.
  • Task 4: Determine the correlation between player’s jersey number and their performance parameters.

Download Sample Dataset


9. Healthcare Insurance Data

The cost of healthcare insurance varies based on a customer’s current age, BMI, smoking habits, etc. In our sample healthcare insurance data sample, you listed the following items for each customer:

  • Name
  • Age
  • Gender
  • BMI
  • Children
  • Smoking Status
  • Location
  • Insurance Price

The following image provides a preview of the healthcare insurance data for analysis:

Healthcare Insurance Data in Excel

To enhance your data analysis skills with this dataset, you can solve the following tasks:

  • Task 1: Determine the insurance price difference for smokers vs non-smokers.
  • Task 2: Determine the correlation between (i) BMI and insurance price and (ii) number of children vs insurance price.
  • Task 3: Calculate the average insurance price based on locations. Use conditional formatting to highlight the location with the highest insurance price.

Download Sample Dataset


10. Call Center Sentiment Analysis Data

While providing various types of customer services, call centers receive a wide variety of feedback from customers. The collection of such feedback and customer details makes an ideal dataset for data analysis.

In our call center sentiment analysis dataset, we have included the following information for each customer service:

  • ID
  • Customer Name
  • Sentiment
  • CSAT Score
  • Call Timestamp
  • Reason
  • City
  • State
  • Channel
  • Response Time
  • Call Duration
  • Call Center

You can preview the call center sentiment analysis data in the following image:

Call Center Sentiment Analysis Data in Excel

You can try the following data analysis tasks using the provided call center sentiment analysis data:

  • Task 1: Calculate the percentage of customers for each type of sentiment and show it in a column or bar chart.
  • Task 2: Determine the average call duration for each type of sentiment and different channels. Visualize the data using two charts.
  • Task 3: Calculate the correlation between CSAT score and call duration.

Download Sample Dataset


11. Students Marksheet Data

A student’s marksheet dataset usually contains general information about students and their obtained marks in different examinations. In our student mark sheet data, we have included the following items:

  • ID
  • Name
  • Marks in Mathematics
  • Marks in Physics
  • Marks in Chemistry
  • Percentage

The following image shows a preview of the student’s marksheet data:

Student Marksheet Data in Excel

You can practice the data analysis tasks listed below with this dataset to enhance your data analysis skills in Excel:

  • Task 1: Filter or highlight the top 5 rows based on the highest percentages.
  • Task 2: Create a dynamic gauge chart to show the percentage of a student.
  • Task 3: Filter the students who are good in mathematics (scored 80 or more) but bad in Physics (scored 60 or lower).

Download Sample Dataset


Tips to Organize Data for Analysis in Excel

When you download, copy, or import data from another source, the data may not be organized properly. Practicing data analysis with such unorganized datasets may lead to inaccurate results or errors. Therefore, you should organize your data properly before analyzing the data.

To prepare your data for analysis, you can follow the suggestions listed below:

  • As you will modify the dataset while analyzing, you should make a copy of the original data. This will allow you to start from scratch if you make an unintentional mistake.
  • Use meaningful column headers for your dataset. This will help in analyzing the content of columns.
  • Unmerge all merged cells. Merged cells often create problems while applying Excel formulas and features.
  • Freeze the first row if you are using a large dataset.
  • Clean the data by removing leading or trailing spaces, and deleting unnecessary rows and columns.

Conclusion

To conclude the tutorial on Excel data for analysis, we hope that you liked the 11 data samples we provided. After downloading your preferred dataset, try to solve the corresponding tasks listed for that dataset. If you face any difficulties solving the tasks or have any other queries, feel free to share them with us in the comment section.


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Seemanto Saha
Seemanto Saha

Seemanto Saha graduated in Industrial and Production Engineering from Bangladesh University of Engineering and Technology. He has been with ExcelDemy for a year, where he wrote 40+ articles and reviewed 50+ articles. He has also worked on the ExcelDemy Forum and solved 50+ user problems. Currently, he is working as a team leader for ExcelDemy. His role is to guide his team to write reader-friendly content. His interests are Advanced Excel, Data Analysis, Charts & Dashboards, Power Query,... Read Full Bio

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