research work excel

Excel Tutorial: How To Use Excel For Research

• Introduction
• Understanding The Excel Interface
• Basic Data Entry And Formatting
• Formulas And Functions
• Data Analysis Tools
• Collaboration And Security

Conclusion & Best Practices

Introduction to excel for research.

Excel is a powerful tool that is widely used in both academic and professional settings for managing and analyzing research data. Its user-friendly interface and robust features make it an essential tool for researchers looking to organize and analyze their data efficiently.

Overview of Excel’s capabilities for managing and analyzing research data

• Data Organization: Excel allows researchers to easily input, organize, and store large amounts of data in a structured manner.
• Data Analysis: Excel includes a wide range of functions and tools for performing complex data analysis, such as statistical calculations, charts, and graphs.
• Data Visualization: Researchers can use Excel to create visually appealing charts and graphs to present their findings in a clear and concise manner.
• Data Sharing: Excel files can be easily shared with collaborators, making it a convenient tool for collaborative research projects.

Importance of Excel in academic and professional research environments

Excel plays a crucial role in academic and professional research environments due to its versatility and effectiveness in managing research data. Researchers rely on Excel to organize, analyze, and present their data in a way that is easily understandable and accessible. Whether conducting a small-scale experiment or a large-scale research project, Excel provides the tools needed to streamline the research process and draw meaningful conclusions from the data.

Initial steps to setting up a research project in Excel

• Define Research Objectives: Before setting up a research project in Excel, it is important to clearly define the research objectives and determine the type of data that will need to be collected and analyzed.
• Create a Data Entry Sheet: Start by creating a data entry sheet in Excel, with each column representing a different variable or data point that will be collected.
• Import Existing Data: If you have existing data that needs to be imported into Excel, use the import function to bring the data into your Excel sheet.
• Apply Formatting: Apply formatting to the data entry sheet to make it easier to read and navigate, such as using bold headers for each column and shading alternate rows for better visibility.
• Set up Formulas and Functions: Use Excel’s built-in formulas and functions to perform basic calculations and data analysis tasks, such as summing a column of numbers or calculating averages.
• Importance of Excel in research
• Basic functions for data analysis
• Advanced features for research projects
• Creating visualizations for presenting data
• Tips for efficient data management

Organizing Data Effectively

When conducting research using Excel, organizing your data effectively is crucial for easy access and analysis. By structuring your data tables, utilizing Excel's sorting and filtering features, and implementing named ranges, you can streamline your research process and make data referencing and formula calculations more efficient.

Structuring data tables for ease of use and access

One of the first steps in using Excel for research is to structure your data tables in a clear and organized manner. This involves labeling your columns and rows appropriately, using headers to identify different categories of data, and ensuring that each piece of information is entered into the correct cell.

Tip: Use freeze panes to keep headers visible as you scroll through large datasets, making it easier to reference and analyze your data.

Utilizing Excel’s sorting and filtering features to manage large datasets

Excel offers powerful sorting and filtering features that allow you to quickly organize and analyze large datasets. By sorting your data based on specific criteria, you can identify trends, outliers, and patterns more easily. Filtering allows you to display only the data that meets certain criteria, making it easier to focus on specific subsets of your data.

Tip: Use the 'Sort' and 'Filter' buttons on the Excel toolbar to quickly access these features and customize your data display.

Implementing named ranges for better data referencing and formulas

Named ranges in Excel allow you to assign a specific name to a range of cells, making it easier to reference that data in formulas and calculations. By using named ranges, you can make your formulas more readable and reduce the risk of errors when referencing data across multiple worksheets or workbooks.

Tip: To create a named range, select the range of cells you want to name, then go to the 'Formulas' tab and click on 'Define Name.' Enter a descriptive name for your range and click 'OK' to save it.

Data Analysis Tools in Excel

Excel is a powerful tool that can be used for a wide range of research tasks, including data analysis. In this chapter, we will explore some key Excel functions and formulas that are particularly relevant to research, as well as how to use PivotTables for summarizing and analyzing data, and how to leverage the Analysis ToolPak for advanced statistical analysis.

Overview of key Excel functions and formulas relevant to research

When conducting research, it is essential to be able to manipulate and analyze data effectively. Excel offers a variety of functions and formulas that can help with this process. Some key functions and formulas that are commonly used in research include:

• VLOOKUP: This function allows you to search for a value in a table and return a corresponding value from another column.
• IF: This function allows you to perform a logical test and return one value if the test is true and another value if the test is false.
• AVERAGE: This function calculates the average of a range of values.
• STDEV: This function calculates the standard deviation of a range of values, which is useful for measuring the variability of data.

Using PivotTables for summarizing and analyzing data

PivotTables are a powerful feature in Excel that allow you to summarize and analyze large amounts of data quickly and easily. With PivotTables, you can create custom reports, analyze trends, and identify patterns in your data.

To create a PivotTable, simply select the data you want to analyze, go to the Insert tab, and click on PivotTable. From there, you can drag and drop fields into the rows, columns, and values areas to customize your report. You can also apply filters, sort data, and format the PivotTable to suit your needs.

Leveraging the Analysis ToolPak for advanced statistical analysis

The Analysis ToolPak is an Excel add-in that provides a collection of advanced statistical analysis tools. These tools can help you perform complex data analysis tasks, such as regression analysis, correlation analysis, and t-tests.

To enable the Analysis ToolPak, go to the File tab, click on Options, select Add-Ins, and then click on the Analysis ToolPak option. Once the Analysis ToolPak is enabled, you can access a wide range of statistical functions and tools by clicking on the Data tab and selecting Data Analysis.

Data Visualization Techniques

When it comes to conducting research, one of the most important aspects is being able to effectively visualize and present your data. Excel offers a variety of tools and features that can help you create visually appealing charts and graphs to illustrate your research findings. Let's explore some key data visualization techniques in Excel:

Creating charts and graphs to illustrate research findings

Charts and graphs are powerful tools for summarizing and presenting data in a visual format. Excel provides a wide range of chart types, including bar graphs, pie charts, line graphs, and more. To create a chart in Excel, simply select the data you want to visualize, then click on the 'Insert' tab and choose the type of chart you want to create.

Pro tip: Use different chart types to highlight different aspects of your data. For example, a pie chart can be useful for showing proportions, while a line graph can be used to show trends over time.

Customizing visuals for clarity and impact in presentations

Customizing your visuals is key to ensuring that your data is presented clearly and effectively. Excel allows you to customize every aspect of your charts and graphs, from colors and fonts to axis labels and data labels. To customize a chart in Excel, simply click on the chart and then use the 'Chart Tools' options to make changes.

Pro tip: Use consistent colors and fonts throughout your visuals to create a cohesive and professional look. Adding data labels can also help clarify the information presented in your charts.

Using Sparklines for mini-graphs within cells to show trends

Sparklines are small, simple charts that can be inserted directly into individual cells in Excel. These mini-graphs are a great way to show trends and patterns within your data without taking up a lot of space. To insert a sparkline in Excel, select the cell where you want the sparkline to appear, then click on the 'Insert' tab and choose the type of sparkline you want to create.

Pro tip: Sparklines are especially useful for comparing data across multiple rows or columns. You can easily spot trends and patterns at a glance, making them a valuable tool for data analysis.

Managing Literature and Citations

When conducting research, it is essential to keep track of the various sources and references you come across. Excel can be a powerful tool to help you manage your literature and citations efficiently. Here are some tips on how to utilize Excel for this purpose:

Tracking research sources and references within Excel

One of the most basic yet effective ways to manage your literature and citations in Excel is to create a spreadsheet where you can list all the sources you have used. You can include columns for the author's name, publication year, title of the source, and any other relevant information. This will help you keep track of where you found your information and easily reference it later.

Utilizing hyperlinks for quick access to online resources

Excel allows you to insert hyperlinks into your spreadsheet, which can be incredibly useful for quick access to online resources. You can link directly to the websites where you found your sources, making it easy to revisit them when needed. Simply highlight the text you want to turn into a hyperlink, right-click, and select 'Hyperlink' to insert the URL.

Creating a dynamic literature review table with automated update features

For a more advanced approach, you can create a dynamic literature review table in Excel that automatically updates when you add new sources. By using Excel's functions such as VLOOKUP or INDEX-MATCH, you can pull information from your source list into your literature review table. This will save you time and ensure that your literature review is always up to date.

Collaborating with Others

When conducting research, collaboration with team members is essential for efficiency and accuracy. Excel offers several features that can facilitate teamwork and streamline the research process.

Sharing research data securely with team members

Excel allows you to securely share research data with team members by utilizing password protection and encryption. By password protecting your Excel files, you can control who has access to the data and ensure that sensitive information remains confidential. Additionally, you can encrypt your files to add an extra layer of security, especially when sharing data through email or cloud services.

Utilizing Excel's Track Changes and Comments features for team collaboration

Excel's Track Changes feature allows team members to make edits to the research data while keeping a record of all changes made. This feature is particularly useful when multiple team members are working on the same dataset, as it helps track who made which changes and when. Additionally, the Comments feature in Excel enables team members to leave notes and feedback on specific cells or sections of the data, promoting effective communication and collaboration.

Leveraging cloud services for real-time data access and editing

Cloud services such as Microsoft OneDrive or Google Drive can be integrated with Excel to enable real-time data access and editing. By storing your Excel files on the cloud, team members can access the data from anywhere with an internet connection and collaborate simultaneously. This eliminates the need for emailing files back and forth and ensures that everyone is working on the most up-to-date version of the research data.

After learning how to use Excel for research, it is important to keep in mind some key points to ensure the integrity and replicability of your results. By following best practices and avoiding common pitfalls, you can make the most out of Excel for your research projects.

A Summary of key points on using Excel for research

• Organize your data: Properly structure your data in Excel by using separate sheets for different types of information.
• Use formulas and functions: Take advantage of Excel's powerful functions to perform calculations and analysis on your data.
• Visualize your data: Create charts and graphs to present your findings in a clear and concise manner.
• Document your process: Keep track of your steps and decisions by adding comments and notes in your Excel sheets.

Common pitfalls to avoid

• Ignoring data backup: Always make sure to regularly back up your Excel files to prevent data loss.
• Not using version control: Use version control tools or features in Excel to track changes and revisions in your research.

Best practices for maintaining data integrity and ensuring replicability of results

• Validate your data: Double-check your data entries and formulas to ensure accuracy and consistency.
• Protect your data: Use password protection and encryption to secure sensitive information in your Excel files.
• Share your work: Collaborate with colleagues by sharing your Excel files and documenting your methodology for transparency.

Related aticles

The Benefits of Excel Dashboards for Data Analysts

Unlock the Power of Real-Time Data Visualization with Excel Dashboards

Unlocking the Potential of Excel's Data Dashboard

Unleashing the Benefits of a Dashboard with Maximum Impact in Excel

Exploring Data Easily and Securely: Essential Features for Excel Dashboards

Unlock the Benefits of Real-Time Dashboard Updates in Excel

Unleashing the Power of Excel Dashboards

Understanding the Benefits and Challenges of Excel Dashboard Design and Development

Leverage Your Data with Excel Dashboards

Crafting the Perfect Dashboard for Excel

An Introduction to Excel Dashboards

How to Create an Effective Excel Dashboard

• Choosing a selection results in a full page refresh.

• Machine Learning
• Google Analytics
• Artificial Intelligence
• Financial Accounting
• Illustrator
• Digital Art
• After Effects
• Operating System
• Cloud Computing
• Influencer Marketing
• Email Marketing
• Digital Marketing
• Content Marketing
• Affiliate Marketing

Excel is a powerful tool that can be used for scientific research and analysis. With its vast array of functions, tools, and features, it has become a go-to software for many researchers and analysts. In this blog, we will explore how to use Excel for scientific research and analysis.

Data Management

The first step in any scientific research and analysis is to manage data effectively. Excel allows users to store and organize data in spreadsheets, making it easy to work with. To manage data effectively, users can create tables and charts, add formulas, and use conditional formatting to highlight important information.

Data Analysis

Once data is organized, it’s time to analyze it. Excel provides numerous functions for data analysis, including regression analysis, hypothesis testing, and correlation analysis. These functions enable researchers to identify trends, patterns, and relationships in their data, making it easier to draw conclusions and make recommendations.

Data Visualization

Data visualization is a critical part of scientific research and analysis. Excel provides several options for visualizing data, including charts, graphs, and histograms. These tools can help researchers understand the data better and communicate their findings to others effectively.

Statistical Analysis

Statistical analysis is an essential part of scientific research and analysis. Excel has a wide range of statistical functions that enable researchers to analyze data and draw meaningful conclusions. These functions include t-tests, ANOVA, and chi-square tests.

Graphical Analysis

Graphical analysis is another critical component of scientific research and analysis. Excel provides a range of tools for creating graphs and charts, including scatter plots, line charts, and bar charts. These graphical tools enable researchers to visualize data and identify trends quickly.

Data Manipulation

In scientific research and analysis, it’s often necessary to manipulate data. Excel provides a range of tools for manipulating data, including sorting, filtering, and conditional formatting. These tools can help researchers identify outliers and anomalies, making it easier to draw accurate conclusions.

Collaboration and Sharing

Excel allows researchers to collaborate and share data with others easily. It enables users to share spreadsheets via email, OneDrive, or SharePoint, making it easy to work with others remotely. Excel also enables users to protect data and restrict access, ensuring data security and integrity.

Quality Control

Excel can also be used for quality control purposes in scientific research and analysis. By using functions like conditional formatting and data validation, researchers can identify errors and inconsistencies in their data. This can help ensure that the data used for analysis is accurate and reliable.

Macros and Custom Functions

Excel allows users to create custom macros and functions, which can help automate repetitive tasks and streamline workflows. This can be particularly useful in scientific research and analysis, where data sets can be large and complex. By automating certain tasks, researchers can save time and reduce the risk of errors.

Importing and Exporting Data

Excel also provides options for importing and exporting data from other software and applications. For example, researchers can import data from a laboratory instrument or export data to a statistical software package. This flexibility enables researchers to work with data from a wide range of sources and ensures compatibility with other tools and software.

Pivot Tables

Pivot tables are a powerful tool in Excel that can help researchers analyze and summarize large data sets quickly. Pivot tables enable users to rearrange and summarize data, making it easier to identify trends and patterns. This can be particularly useful when working with large and complex data sets.

Data Mining

Excel also provides options for data mining, which involves using statistical algorithms to identify patterns and relationships in data. For example, researchers can use Excel’s data mining tools to identify trends in sales data or identify commonalities between different samples. This can help researchers identify new research questions or opportunities.

Conclusion: Excel is an incredibly powerful tool for scientific research and analysis. With its vast array of functions, tools, and features, it provides researchers with the ability to manage, analyze, and visualize data effectively. By utilizing Excel, researchers can draw meaningful conclusions and make informed recommendations, advancing their scientific research and analysis.

If you’re looking to enhance your expertise in Excel , LearnTube has got you covered with an array of online courses tailored to your needs. With the help of our specialized learning app and WhatsApp bot, you can enjoy a seamless learning experience. Our platform offers an extensive range of courses that cater to both novices and seasoned learners. For valuable insights, explore our diverse selection of courses on our website .

More from author

How much will i earn as a flutter developer the ultimate salary guide for 2024, top companies hiring flutter developers in 2024, the ultimate guide to flutter certification: how to become a certified expert, leave a reply cancel reply.

Save my name, email, and website in this browser for the next time I comment.

Related posts

How to use excel for sales forecasting and pipeline management.

Latest posts

Want to stay up to date with the latest news.

We would love to hear from you! Please fill in your details and we will stay in touch. It's that simple!

LearnTube by CareerNinja is a platform with 200+ Free courses, 900+ hiring partners that will help you with placement and internship opportunities. So study along with 2Lakh+ users today!

Latest Posts

Most popular, 15 hidden photoshop features you probably didn’t know about, how to become a data analyst from scratch, debugging django applications: tips and tricks, fast access.

• Business & Finance
• Information Technology
• Data Science
• Design & Creative
• Programming

© CareerNinja 2022 | All rights reserved | Made with Love

Public Health

Preparing Data in Excel

•   Center for Global Health and Development
•   Center for Preparedness and Emergency Response Solutions
•   Center for Collaboration on Research Design and Analysis
•   CCORDA Scope of Services
•   When to Contact CCORDA
•   Request for CCORDA Services
•   CCORDA Service Fee
•   Educational Opportunities
•   Grant Preparation
•   Preparing Data in Excel
•   Authorship
•   Administrative Core and Membership
•   Contact CCORDA
•   Center for Health Policy
•   Center for Health Policy Analysis and Rural Health Research
•   Central States Center for Agricultural Safety and Health
•   Center for Reducing Health Disparities

The members of CCORDA have expertise in design and planning of studies, including preparation of data collection forms and database creation. We encourage researchers to include CCORDA in all phases of the study from design to analysis and dissemination of results. Some researchers collect and enter their own data for analysis. Accurate data entry is critical for the success of the study. We have prepared some helpful hints for entering data into an Excel Workbook for ease in statistical analysis.  

Microsoft Excel can be a useful platform to enter and maintain research study data. Excel is fairly easy to learn and use. Researchers can use Excel's simple statistical and plotting functions to help gain insight into their data. However, most research projects require more extensive statistical techniques that can be most easily performed using additional statistical software packages such as SAS or SPSS software.

In order to have your data easily imported into a statistical software package we have developed these guidelines for data entry into Excel.

Here is a good example of data entry into an Excel file followed by guidelines for data entry.

Good Excel Data Sheet:

Guidelines:.

• Include a unique identifying number for each case.
• Be sure that each variable name is unique (no duplicate variable names).
• Variable names must start with a letter.
• Do not include special characters (#, !, ?, %, etc.) or spaces in your variable names.
• Choose readily recognizable names for variables - but not too long (<= 16 characters best).
• Don’t enter data such as "120/80" for blood pressure. Enter systolic blood pressure as one variable and diastolic blood pressure as another variable. Don't enter data as "A,C,D" or "BDF" if there are three possible answers to a question. Include a separate column for each answer.
• Two digit years can cause problems for statistical software when reading data from Excel files. The best format for dates is mm/dd/yyyy, where mm is a 2 digit month, dd is a 2 digit day and yyyy is a 4 digit year.
• Missing data can cause a multitude of problems. To enter a missing data value either enter a blank or an "impossible" numeric code (for numbers) or an easily recognizable single digit character code for character (trying to avoid mixing numeric and character data). Be sure, if you use a missing value code, that it cannot be confused with a "real" data value.
• When entering data keep the same format throughout.

  Bad Example

Notice in the Good Example above that the date variable has the same format (mm/dd/yyyy) and the sex variable is consistent throughout in both case and type (character variable). In the Bad Example the date variable is in different formats without a 4 digit year for all the observations. The sex variable is still a character variable, but statistical software will read this variable as having six different levels instead of two.

• If you decide to use multiple sheets for you data, follow the variable naming conventions for the tabs that name the sheets (keep the names simple and unique).
• For example, treated versus non-treated patients can be handled by column variable that has a code for Treated (yes/no).
• These features can be used on other separate "subset" or "analysis" spreadsheets that are for the investigator, but not the statistician or programmer.

Data Dictionary:

• Be sure the effort you are putting forth is necessary. The CCORDA member should be able to tell you precisely what form the data needs to be in to suit its conversion and analysis.

10 Excel Project Ideas for Your Data Science Portfolio

Join over 2 million students who advanced their careers with 365 Data Science. Learn from instructors who have worked at Meta, Spotify, Google, IKEA, Netflix, and Coca-Cola and master Python, SQL, Excel, machine learning, data analysis, AI fundamentals, and more.

Microsoft Excel is a very powerful tool to leverage when working with data. It has a well-established reputation in the data science community and mastering its advanced functionalities , such as VLOOKUP, Pivot Tables, and Power Query is still a top priority for aspiring professionals in the field.

So, if you already have experience in the software but need inspiration on how to use it to enhance your portfolio, keep reading to discover 10 Excel spreadsheet project ideas you can start working on today!

• Creating a personal spending budget
• Import/Transform Data to Excel via Power Query
• Experiment with Pivot Tables
• Utilize VLOOKUP
• Create a Tree Map
• Create a Histogram
• Create a Scatterplot
• Make a Forecast model in Excel
• Manage a Data Model
• Develop an Interactive Dashboard

1. Create a Personal Spending Budget

The best data science projects are rooted in identifying an area where value can be added by your analysis. Developing a personal daily/weekly/monthly budget in Excel is a great first step that allows for real-world application. A way to expand on this is to have variable inputs that can be adjusted to change the target budget value.

2. Import/Transform Data to Excel via Power Query

A great opportunity to experiment with obtaining data from various sources is by creating a workbook data connection in Excel via Power Query. Power Query can be used to import data from sources such as text files, online services, and databases. Once data is imported you can perform additional steps beginning with renaming columns and changing data types , and eventually moving on to advanced techniques such as adding conditional formatting, creating Pivot Tables , as well as combining different functionalities like the often paired INDEX and MATCH to retrieve specific data values. Overall, an Excel project centered on importing and transforming information is an excellent way to practice data collection, and cleaning, widely applicable in data science .   

3. Experiment with Pivot Tables

Pivot Tables are an essential tool for Data Analytics in Excel. They allow you to summarize data while easily highlighting points of interest.

The above is an example of a Pivot Table created from a dataset containing information on the top 1000 highest-grossing Hollywood films. With it, we’ve obtained the top 15 movies in terms of Domestic Sales quickly and efficiently. This type of analysis can be done on any dataset that interests you .

4. Utilize VLOOKUP

VLOOKUP works well when trying to create new, smaller tables from a large dataset. It’s a great function that allows you to find more targeted, cleaner data to work with. Using VLOOKUP will allow you to make information easier to analyze and will help in many of the projects further down this list.

Check out our VLOOKUP COLUMN and ROW tutorial for more details on how to use VLOOKUP to handle large data tables with ease.

5. Create a Tree Map

A tree map is a data visualization chart that is often used in dashboards. Excel offers a great opportunity to become familiar with this tool and to apply your skills in practice. With Excel you can develop multiple Tree Maps that highlight different points of interest in your data, all the while adjusting formatting and customizing the chart to your liking. The example below is based on the aforementioned dataset containing information about the top 1000 highest-grossing Hollywood films.

6. Create a Histogram

Histograms are tools used in statistical data analysis whenever we want to visualize the normality of the distribution of data. A potential Excel project involving histograms could take as its starting point researching different types of real-world data (such as the outcome of rolling dice or flipping a coin) and utilizing a histogram to visualize their distributions and determining what they would be classified as. An example of this would be the below visualization which was created using the RANDBETWEEN function in Excel to simulate 1000 coin flips.

After analyzing the Histogram, we can establish that a coin flip follows a Binomial Distribution logic due to there only being two possible outcomes, heads or tails.

7. Create a Scatter Plot

Scatter plots fall in line with contingency tables, and histograms as being very useful in statistical data analysis.  They are an often-used tool for visualizing the relationship between two variables in data. Like other fun Excel projects on this list, this one would work best with a dataset that holds particular interest to you. Once you have picked a dataset, it’s time to start charting relationships between variables of interest and digging into what you find. An example of a scatter plot made from the previously mentioned top 1000 highest grossing Hollywood films dataset can be found below.

8. Make a Forecast Model in Excel

Excel has a built-in forecast functionality that can be used to create a prediction on time-related data such as the number of sales of specific products. In order to create a forecast in Excel, you’ll need to select data with a date tied to it and the corresponding values for that date. Forecasting is an area of particular interest for data analysts and experimenting with it in Excel is a chance to get business-applicable experience. Excel also allows you to customize your forecast and set parameters such as the confidence interval, seasonality pattern, and timeline range.

9. Manage a Data Model

Excel enables you to integrate data from multiple tables which can then be connected by defining relationships. An Excel data model is in some ways similar to a relational database and is perfect for visualizing and extracting insights from several different sets of related data. The diagram view in particular is a wonderful way to see the relationships in the model you create.

10. Develop an Interactive Dashboard

The final suggestion on this list is a way to combine everything else we covered so far into one extensive Excel project. Each of the previous examples can be practiced in isolation, but it would also be beneficial to use them together to develop a dashboard for displaying your data and highlighting your data science capabilities in Excel. In order to make a dashboard interactive, it needs to have elements of Pivot Tables, VLOOKUP, relational data, and the chart types mentioned earlier. Adding Slicers to the worksheet will enable users to select their own filters and can ensure that an Excel dashboard is able to rival one built in Tableau or Power BI . Moreover, it comes with the added bonus of being in a format that more people are familiar with, as Excel is more widely used than those tools.

What is an Excel project?

Excel projects come in different shapes and sizes, but what’s common about them is that they involve manipulating data in a spreadsheet format with the intention to extract value from it. In fact, the best Excel projects are those that strive to make a measurable difference in the way something such as a business metric, or a personal budget, is understood and managed. That being said, the best data science projects are always passion projects, so take care to pick data sets that interest you and that are of value to you before you start pondering the business applications of your project. Excel is uniquely positioned to be both a powerful business intelligence tool and an excellent starting point for data analysts looking to acquire practical skills. Most of the software’s advanced functionalities find regular usage in the corporate world, with some even finding their way into predictive analytics solutions.

What projects can I use Excel for?

Data scientists looking to expand their project portfolio can use Excel to collect, clean, transform and visualize data. The skills you will gain from executing these projects can be easily transferred to more high-responsibility tasks, such as removing duplicate data from a dataset, preprocessing data so it’s ready for analysis, as well as drawing connections between data points and illustrating your insights. Even though it has a low barrier to entry, Excel is a powerful piece of software and its advanced applications extend all the way to the most cutting edge predictive analytics techniques, such as Machine Learning. In that sense, getting a firm grasp on the most common functions in Excel such as Pivot Tables, VLOOKUP, and its visualization tools, certainly pays off in the long-run, once you start building up on those fundamentals and expanding your knowledge beyond Excel.

How do I build a project in Excel?

Building a project in Excel follows three easy steps:

• Identify the area of your project and the desired outcomes,
• Find the best functionalities to execute your project,
• Create and share your exciting new project.

The most important thing when starting off with any Excel project is to identify the range, scope, and potential value of your idea: do you want to create something fun and exciting for your personal use or something professional to add to your data science portfolio? Are you trying to solve a particular problem, or are you just playing around? How much time, effort, and money will it take to complete this project? What will its benefits be?

After getting those out of the way, you can look into finding appropriate data sets to work with and researching the functionalities you will be using. Some projects can be executed on a 10-by-10 spreadsheet with very basic Excel knowledge, but for others you will be working with huge amounts of data and leveraging advanced functions.

Finally, once your project is done, don’t forge to share it with the world. Add it to your Kaggle profile and plug it into all your data science job applications. After all, a project is only as important and valuable as you make it out to be!

10 Excel Project Ideas for Data Scientists: Next Steps

Now that you have several projects to choose from, the next step would be to start your journey in performing data analytics in Excel. Explore more of the advanced functionalities of the software, such as the INDIRECT function that you can combine with VLOOKUP to create dynamic lookup tables. Or if you’re confident in your Excel skills, dive into the ML capabilities of the software and take your portfolio of projects to the next level with our Machine Learning in Excel course . Curious to learn more? The 365 Data Science Program offers 40+ self-paced courses led by renowned industry experts. Starting from the very basics all the way to advanced specialization, you will learn by doing with a myriad of practical exercises and real-world business cases. If you want to see how the training works, start with a selection of free lessons by signing up below.

World-Class

Data Science

Learn with instructors from:

Resource Planning Advisor

Saadan is a Resource Planning Advisor who graduated as a Master of Business Analytics from Arizona State University in 2021. He enjoys working on projects related to Machine Learning in sports, as the latter is what sparked his interest in data analytics from a very young age.

We Think you'll also like

Tableau Tutorials

Tableau vs Excel: Which One Should You Use?

Excel Tutorials

INDIRECT Excel Function: how to apply it and combine it with VLOOKUP

VLOOKUP and MATCH: Another Useful Excel Functions Combination

VLOOKUP COLUMN and ROW: Handle large data tables with ease

• Technical Support
• Find My Rep

You are here

Using Microsoft Excel for Social Research

• Charlotte Brookfield - Cardiff University, Cardiff, UK
• Description

Providing step-by-step instructions for how to use Microsoft Excel for doing statistics, Charlotte Brookfield discusses different stages of the research process, from first planning to writing and presenting your research. With a focus on conducting robust data analysis, the book is reassuring, clear and straightforward, helping you to:  

·       Learn important data skills, such as preparing, cleaning and managing data

·       Reduce anxiety about approaching statistics and quantitative data

·       Boost your employability, showing you how to develop transferable skills, such as effective time management.

Whether you’re learning data skills for the first time or translating your statistics knowledge from other software, this book will help you successfully carry out social research in any setting with confidence, via an engaging pedagogy that includes: colour-coded chapters by difficulty, activities, 'Remember' boxes, further reading and skills checklists.

See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

For assistance with your order: Please email us at [email protected] or connect with your SAGE representative.

SAGE 2455 Teller Road Thousand Oaks, CA 91320 www.sagepub.com

Supplements

This is an invaluable resource for people learning statistics. Brookfield provides a clear, accessible and engaging introduction to using Excel to explore, analyze and report quantitative data. 

It makes perfect sense to train our students in Microsoft Excel: not only does the programme have increased functionality for social research, but it is also a staple in many graduate workplaces. This text is distinct from other Excel help guides in that it is explicitly designed around the social research process and with undergraduate students in mind. Using real-life datasets and tools such as ‘reminder boxes’, it is a highly comprehensive, engaging and accessible resource for introductory quantitative research methods modules. 

This is the book I have been waiting for. We have learned that employers value Excel and that many small workplaces cannot afford SPSS licences. This covers everything we would do in SPSS (possibly excepting recoding variables). Sold.

Preview this book

For instructors, select a purchasing option, related products.

• Photography
• Video Lecture Tips

Scott Kuban

How to enter research data into excel.

As a professor that often leads the data and analysis of scholarly research projects, and collaborates across many universities, I have encountered good, bad, and downright terrible data being passed around in excel files. This is usually the result of Ph.D. students still moving up the learning curve but can be the result of other issues. So I created this document to help students not be terrible at hand-collecting, recording, transferring, and (if we absolutely must) manipulating data in excel. Further, research professors may find a tip that allows them to further optimize their already no-doubt finely-honed data skills. This -much like the vaunted U.S. Constitution- will be a living document that evolves and grows with time. Constitution constructionist trolling aside, this simply means that I will be updating this document as I think of new tips or students find new ways to anger me with the data sent to me for a project. Tips are numbered merely for easy reference not to confer some order or importance upon them. Importance and/or recency of data anger will be conferred with exclamation points and/or italics. That said, the number 1 rule is pretty important.

Why should I care? First, there is the classic computer science mantra: garbage in, garbage out. Heaven forbid, but badly input data could, if not corrected along the way by a professor overseeing the work, bias a project’s results that will be submitted to a conference or journal. Second (and more likely with skilled researchers guiding the project), poorly collected data negatively impacts a research project through additional time wasted getting the data to merge properly, cleaning the messy data, and analyzing models with poorly thought out variables. The latter usually results in additional data collecting (if still possible) to replace the poorly thought out variables with better ones. All this wasted time and effort also creates frustration. If the same person is doing all of these tasks, you are only hurting yourself by ignoring these tips. If a student is collecting data poorly for a professor this can lead to… unpleasantries. Avoid future pain by following these rules.

Note: While using excel is a reasonable choice for data that must be hand-collected, it should be an absolute last resort for otherwise interacting with the data. And no, already knowing how to do something in excel rather than learning how to do it in SAS, Stata, or R is not a valid reason. Don’t be lazy.

• A column in excel represents a single variable that someone will do statistics on later! Treat it as such!!! This means a) figuring out what the variable will be later in the model to determine how the data should be entered and b) only entering that type of data. For instance, faced with collecting if an executive is a CEO a) figuring out that a binary variable is a wise choice for this b) entering a 1 or 0 only!!! Rather than the terrible text strings that often become an incoherent combination of YES, y, no, YeS, 1, Y, N, NO, Co-CEO, Interm CEO, [blank], Interm CEO that was later approved to be full CEO, NA, N/A, I couldn’t find this one, Not applicable, President Only, etc.
• Use dummy variables whenever possible. Many, many things can be converted into one or more dummies if you consider it first. So instead of ‘executive position’ with a bunch of free form text (similar to the example text in #1) create a dummy for each position: CEO, COO, CFO, etc.
• Expand your dummy variables as needed rather than breaking the #1 rule! For instance, after setting up dummies in #2 if one encounters a Co-CEO instead of abandoning your principals and writing “Co-CEO” in the column for CEO, create a new Co-CEO column that can still be coded with a 1 or 0. Make a note (see #4) as to whether Co-CEOs are still coded as a CEO (i.e., is the Co-CEO variable additive to the CEO variable or replacing it).
• Put any notes in a separate column or columns. Depending on the amount and type of data being collected in excel a single ‘notes’ column may suffice or it may be better to create a notes field for each variable that needs it. Name it something obvious like executive_scandal_notes for notes on the executive_scandal variable.
• Enter dates numerically (no text months) and consistently!!! Generally, this should be either: 2020/3/25 or 3/25/2020. Yes, doing the placeholder zeros (e.g., 2020/03/25) is a little cleaner but most programs can handle importing correctly without them and people tend to struggle with including them. I often see a huge mix of date styles in a single column: 3/25/1999, March 25th, 1999, 3-25-1999, 3.25.1999, Mar 25, Mar 25 99, 25th Mar 1999, etc. Which neither imports well or is easy to clean with a script as it is inconsistent (e.g., a script splitting on the /, or grabbing the final 4 characters for the year).
• Never change an ID in excel. These are often used to merge the excel data with other data so if you change the ID the merge will fail. Instead, create a new column such as ID_updated with the change you want to make. This allows for merging on the newly updated ID without throwing away the old ID through your reckless action.
• Unless you explicitly know otherwise, assume every variable is an ID variable (or otherwise similarly important). Disk space is practically free while time is incredibly valuable , so instead of changing data in an existing variable (column) make a new variable (column) to make your changes. For instance, to get the stock market reaction to an event the date often will need to not the event_date but the first day the stock market was open following the event_date. The programs that retrieve stock market data also want the data formatted in a particular way. This, therefore, presents two temptations to change the event_date while retrieving this additional data – don’t do it. Make a new stock_reaction_date variable and leave the event_date unmolested so that one can easily merge the new data into the existing dataset.
• Really it is best to avoid changing any variables data if at all possible. In addition to breaking merges if the variable is an ID, the variable in question may undergo cleaning and or other manipulation in code after import that your changes might break thus also wasting others’ precious time. This is extremely likely when adding additional data (usually at the request of reviewers) later in a project. Again, disk space is practically free while time is incredibly valuable so make another column and use another 0.0000001% of your disk space rather than potentially waste hours of time.
• Be careful of text IDs with preceding zeros (e.g., 0023817). Excel likes to throw these zeros away. One must make the column a ‘text’ format (rather than general, number, etc) to keep it from doing this. Compustat’s unique ID named gvkey is such a variable.
• Don’t hand make a variable that can be created in a program. For instance, perhaps one’s eventual model will consider if CEOs are fired the same day a scandal is announced by the firm. Collect the two dates (see #5) in two variables scandal_announcement_date and CEO_fired_date, but do not create a human entered dummy (it should be a dummy see #2) CEO_fired_on_announce_day as one can easily and without mistakes create this dummy in Stata/SAS/R by comparing the two dates.
• Any time one wants to enter N/A in a variable consider if a) the way you are creating this variable is a good one or if b) one merely doesn’t understand what N/A actually means. A) Could you use a different coding and/or additional variables (see #3) to capture the data without a useless N/A that both breaks #1 and doesn’t capture whatever is driving you to put N/A in the first place? B) If you are entering N/A into a mutually exclusive dummy you are using it wrong. E.g., CEO_has_former_CEO_experience has to be a 1 or a 0 (or a missing if one doesn’t know) the newly hired CEO either was a CEO before or not, therefore N/A makes no sense. And senseless data makes everyone unhappy.
• Watch out for spaces before and after data being entered. Macs particularly like to “help you” by adding a space after any and every word ever because using the space bar is not elegant or something. Depending on the data an extra space can break things on import or later in analysis and are a pain to locate as visually the data looks right.
• If you are asked to fill in missing data in excel, don’t change a bunch of other things without checking with your professor first. You may be changing IDs needed to merge your collected missing data back into the dataset or otherwise messing up the dataset (see #6, #7, and #8 ).
• Do not use multiple tabs in excel. If you are using multiple tabs in excel this means that you are likely doing some combination of the following: creating unnecessary duplicates, reusing variable names that can only be told apart by referencing the tab names, using variable names that are generally unclear without the tab names, and/or collecting data in a format different than the rest of the data that will require additional work to merge.
• The variable name (top excel row) of the data should a short but meaningful  name. Preferably in lowercase with underscores for spaces unless the variable exists somewhere else already then it should match that other naming exactly. For instance, firm_age is obvious in the data it contains, but even more complicated variables such as firm_FEC_total_amt_cycle reasonably conveys that is the firm’s total amount of FEC (federal election commission tracked campaign contribution) dollars in an election cycle (rather than in a year). Stata’s limit of 32 characters only allows so much description in the name but it should be helpful without referencing the longer variable label.
• The top excel row is for the variable names of the data below it and nothing else. This is not a place for notes to self (see #4) or even explanations of the data below. If you need a note to explain the data collected below, the variable is either ill-conceived (see #1, #2, and #3) or poorly named (see #15), or both.

This website may not work correctly because your browser is out of date. Please update your browser .

Using Excel for qualitative data analysis

• Using Excel for qualitative data analysis File type DOCX File size 80.23 KB

Resource link

• Using Excel for qualitative data analysis (archived link)

This article, written by Susan Eliot for The Listening Resource, provides step-by-step guidance on using Excel as a tool to support the analysis of qualitative data for research or evaluation purposes.

• Assumptions
• Worksheet Template
• Coding and Categorizing
• Making Comparisons
• Step-by-Step Guide

Eliot, S. The Listening Resource, (2011). Using excel for qualitative data analysis. Retrieved from website: http://www.qualitative-researcher.com/qualitative-analysis/using-excel-for-qualitative-data-analysis/

Back to top

© 2022 BetterEvaluation. All right reserved.

Add or change research services

Through Research options , you can customize options to suit your research needs such as including or excluding reference books or research sites. You can activate services for searching, add new services, and remove others from your computer. You can also turn on Parental Control, which helps to protect children from finding potentially offensive or disturbing material.

Tip:  If you're using Word for Microsoft 365 you might want to take a look at the similarly-named, but much more powerful, Researcher feature. You can learn more at  Research your paper easily within Word

Step 1: Find the Research feature

Click Review > Research.

For Outlook the Review tab only appears in the message inspector, so you need to start a new message first.

Step 2: Find research options

Near the bottom of the Research task pane, click Research Options .

Another way to find the research options is to click File > Options > Trust Center . Next, click the Trust Center Settings button to open the Trust Center dialog. Now click the Privacy Options tab and then click the Research Options button.

Step 3: Set the research options you want

Do one or more of the following:

To activate or remove research services, check or uncheck the check boxes you want, and then click OK .

To add research services, click Add Services , select or type the Internet address for the service you want in the Address box, and then click Add . The service is automatically enabled for searching, and it will appear in the Search for list the next time you open the Research task pane.

To add a Microsoft SharePoint Portal Server site, type or copy and paste the following URL into the Address box:

http://your root directory/_vti_bin/search.asmx

To remove a service provider and all of its research services, click Update/Remove , select the provider you want to remove, click Remove > Close .

To turn on Parental Control, click Parental Control , select the options you want, and then click Close .

Need more help?

Want more options.

Explore subscription benefits, browse training courses, learn how to secure your device, and more.

Microsoft 365 subscription benefits

Microsoft 365 training

Microsoft security

Accessibility center

Communities help you ask and answer questions, give feedback, and hear from experts with rich knowledge.

Ask the Microsoft Community

Microsoft Tech Community

Windows Insiders

Microsoft 365 Insiders

Was this information helpful?

Thank you for your feedback.

Spreadsheet Intelligence for Ideas of Excel

Ideas in Excel aims at such one-click intelligence—when a user clicks the Ideas button on the Home tab of Excel, the intelligent service will empower the user to understand his or her data via automatic recommendation of visual summaries and interesting patterns. Then the user can insert the recommendations to the spreadsheet to help further analysis or as analysis result directly. To enable such one-click intelligence, there are underlying technical challenges to solve. At the Data, Knowledge and Intelligence area of Microsoft Research Asia, we have long-term research on spreadsheet intelligence and automated insights accordingly. And via close collaboration with Excel product teams, we transferred a suite of technologies and shipped Ideas in Excel together. In this demo, we will show this intelligent feature and corresponding technologies.

SR. Principal Research Manager

Related Links

Research area.

• Artificial intelligence
• Data platforms and analytics
• Search and information retrieval

Research Lab

• Microsoft Research Lab - Asia
• Spreadsheet Intelligence
• Faculty Summit 2019 | The future of work

Series: Microsoft Research Faculty Summit

A Conversation with Bill Gates Hosted by Eric Horvitz

• Eric Horvitz

Quantum Computing and Workforce, Curriculum, and Application Development: Case study

• Krysta M. Svore ,
• Martin Roetteler

Cars, Computing and the Future of Work: Specific topics of mutual interest

• Ece Kamar ,
• Shamsi Iqbal

Quantum Computing and Workforce, Curriculum, and Application Development: Overview and access

• Mariia Mykhailova

Quantum Computing and Workforce, Curriculum, and Application Development: Open Resources

• Bettina Heim,
• Krysta M. Svore

Crowd, Cloud and the Future of Work: Updates from human AI computation

• Besmira Nushi ,
• Vani Mandava

Quantum Computing and Workforce, Curriculum, and Application Development: Insight into real-world quantum applications

Cars, Computing and the Future of Work: A UW & MSR Workshop: Welcome and Overview of Projects

• Eric Horvitz ,
• Satish Sangameswaran

Quantum Computing and Workforce, Curriculum, and Application Development: An overview of quantum computing

• Jeongwan Haah ,

Crowd, Cloud and the Future of Work: Welcome and Updates

• Kori Inkpen

Empowering People to Achieve More: How Useful a Concept is Productivity?

• Brendan Murphy

Keynote - The Future of Work And the Power of Data

• Johannes Gehrke

Productivity in Software Development

• Neel Sundaresan,
• Margaret-Anne Storey,
• Prem Kumar Devanbu

Artificial Emotional Intelligence, Social Systems, and the Future of Collaboration

• Mary Czerwinski

Workers of the World, Connect! Tech Innovations and Organizational Change for the Future of Work(ers)

• Jacki O'Neill ,
• Mary L. Gray

Increasing AI Programmer Productivity

• Markus Weimer ,

Human-AI Collaboration for Decision-Making

• Rich Caruana

Future of Spreadsheeting

• Andy Gordon,

Program Synthesis meets Notebooks

• Sumit Gulwani

Accessible Virtual Reality

Calendar.help: A Virtual Meeting Scheduling Assistant

• Pamela Bhattacharya

Visual Studio IntelliCode

• Mark Wilson-Thomas

Microsoft Teams: Collaborate with Any Researcher Anywhere

• Jethro Seghers

Project Alava: Programming Webs of Microcontrollers

• James Devine,
• Teddy Seyed

AI in PowerPoint

• Kostas Seleskerov
• Follow on Twitter
• Like on Facebook
• Follow on LinkedIn
• Subscribe on Youtube
• Follow on Instagram
• Subscribe to our RSS feed

Share this page:

• Share on Twitter
• Share on Facebook
• Share on LinkedIn
• Share on Reddit

• Spartanburg Community College Library
• SCC Research Guides

Organizing Your Research

• Source Table

Another way of organizing your sources could be to use a source table in Word or Excel to keep all of your notes in one place. Using a source table in a program like Word or Excel can help you see all of your notes at once and could help you see the connections between your sources easily. You could list out the different quotes or main ideas from each source to make sure they align well with your thesis statement. 

There are a lot of ways that you can organize your table, and you should choose the method that makes the most sense to you. In your table, be sure to include the source name, author, page number (if available), and sections for the direct quotes or paraphrases you want to include. 

Below are templates you can use to organize your research using Word or Excel. Feel free to edit the template to organize sources in a way that works best for you. 

• Excel Source Table Template Create a Source Table in Excel to organize research.
• Word Source Table Template Create a Source Table in Microsoft Word to organize research.
• << Previous: Research Note Cards
• Next: Research Outlines >>
• Research Note Cards
• Research Outlines
• Recommended Resources

Questions? Ask a Librarian

• Last Updated: Jan 17, 2024 9:51 AM
• URL: https://libguides.sccsc.edu/organizeresearch

Giles Campus | 864.592.4764 | Toll Free 866.542.2779 | Contact Us

Copyright © 2024 Spartanburg Community College. All rights reserved.

Info for Library Staff | Guide Search

Return to SCC Website

How to structure Microsoft Excel documents for systematic reviews

Affiliation.

• 1 IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
• PMID: 36856031
• DOI: 10.7748/nr.2023.e1866

Background: Systematic reviews are complex and involve time-consuming, deep research in the academic literature to search, extract data, assess their quality and report the results. Digital tools and software have been developed to simplify different phases of this process but some of these are not free to use. Microsoft Excel is typically accessible to a great many researchers free of charge, so using it involves no further costs.

Aim: To explain how to use Microsoft Excel to create transparent and complete reports for systematic reviews.

Discussion: The author's method includes six steps: downloading the references, preparing worksheets, removing any duplicate references, screening the references by title and abstract, screening the full text of references, and listing the articles for inclusion in the review.

Conclusion: The Excel method is efficient and free and can produce transparent and complete reports of systematic reviews. It is a valid alternative to the systematic reviews produced by advanced tools and software.

Implications for practice: The documents produced by this method are a good source for the direct production of scientific texts.

Keywords: audit; data analysis; data collection; literature search; methodology; research; systematic review.

©2023 RCN Publishing Company Ltd. All rights reserved. Not to be copied, transmitted or recorded in any way, in whole or part, without prior permission of the publishers.

• Research Personnel*
• Systematic Reviews as Topic

• Kutools for Excel
• Kutools for Outlook
• Kutools for Word
• Setup Made Simple
• End User License Agreement
• Get 4 Software Bundle
• 60-Day Refund
• Tips & Tricks for Excel (3000+)
• Tips & Tricks for Outlook (1200+)
• Tips & Tricks for Word (300+)
• Excel Functions (498)
• Excel Formulas (350)
• Excel Charts
• Outlook Tutorials
• About Us Our Team

Feature Tutorials

• Search Search more
• Retrieve License Lost License?
• Report a Bug Bug Report
• Forum Post in Forum

How to disable the research task pane in Excel?

In Excel, when you press Alt key and then click one cell in worksheet, a Research pane will appear at right of the sheet at the same time as left screenshot shown. This will be annoying. If you want to disable this pane, and make it no longer displayed when you apply Alt + Click in Excel. From this article, I will introduce you an easy way to solve it.

Disable the research task pane in Excel with VBA code

Disable the research task pane in excel with an easy feature.

Do with the following operations to disable the research task pane in Excel:

1 . Launch your workbook, and then hold down the Alt + F11 keys in Excel, and it opens the Microsoft Visual Basic for Applications window .

2 . In the opened VB Editor, press Ctrl + G to open the Immediate Window , and then copy and paste this code Application.CommandBars("Research").Enabled = False into the Immediate box, and press Enter key on the keyboard, see screenshot:

3 . Then save and close this VB Editor, and now, when you press Alt key and click the cell in the worksheet, the research task pane will not display any more.

Note : If you need to make the research task pane appear again in your workbook, please copy and paste this code Application.CommandBars("Research").Enabled = True into the Immediate box to replace the original code, and remember to press Enter key.

If you have installed Kutools for Excel , with its Disable research pane option, you can disable this pane quickly and easily as below demo shown:

Tips: To apply this Disable research pane  feature, firstly, you should download the Kutools for Excel , and then apply the feature quickly and easily.

After installing Kutools for Excel , please do as this:

1 . Click Kutools > Show & Hide > View Options , see screemshot:

2 . In the View Options dialog box, please check Disable research pane option under the Other box, see screenshot:

3 . Then click Ok to close this dialog box, now, the Research Pane will be disabled, and it will not be displayed any more.

Note : If you want to enable this Research pane, just uncheck the Disable research pane option in the View Options dialog box.

Click to Download Kutools for Excel and free trial Now!

More articles:

• Disable Cut, Copy And Paste Functions In Excel
• Supposing you have a workbook with important data which you need to protect from being cut, copied and pasted. How to achieve it? This article provides a VBA method for you to disable the cut, copy and paste functions at the same time in an Excel workbook.
• Disable Double-Click On Cell Border In Excel
• Normally, we double click on a cell to edit the cell content, however, the cursor jumps to bottom of the worksheet data as you may click on the cell border accidentally as following screenshot shown. This may be annoying if there are multiple rows, you should scroll up to the clicked cell. This article, I will talk about how to disable double-click on cell border in Excel.
• Disable F1 Help Key In Excel
• When you edit an Excel file, you may always use F2 key to edit the contents of a cell. But, you usually press F1 key instead, it will pop out the Helper dialog box. This may be very boring when you need to use the F2 key time and time again. In this article, I will introduce some quick methods to solve this problem.
• Enable Or Disable Button Based On Cell Value In Excel
• This article, I will talk about some methods to enable or disable button based on cell value in Excel worksheet.
• Prevent/Disable Automatic Hyperlinks In Excel
• As we all known, Excel create clickable hyperlinks automatically when we input the web addresses into cells. Maybe, sometimes, this is somewhat annoying. Today, I will introduce some quick tricks for you to prevent automatic hyperlinks in Excel.

Best Office Productivity Tools

Supports Office/Excel 2007-2021 and 365  |  Available in 44 Languages  |  Easy to Uninstall Completely

Supercharge Your Excel Skills with Kutools for Excel, and Experience Efficiency Like Never Before. Kutools for Excel Offers Over 300 Advanced Features to Boost Productivity and Save Time.   Click Here to Get The Feature You Need The Most...

Office Tab Brings Tabbed interface to Office, and Make Your Work Much Easier

• Enable tabbed editing and reading in Word, Excel, PowerPoint , Publisher, Access, Visio and Project.
• Open and create multiple documents in new tabs of the same window, rather than in new windows.
• Increases your productivity by 50%, and reduces hundreds of mouse clicks for you every day!

Excel Sample Data (Free Download 13 Sample Datasets)

Sample data in Excel refers to datasets used for practice purposes. While learning various Excel features and functions, or performing data analysis in Excel, users often need a sample dataset.

In this Excel tutorial, you will find 13 ideal Excel sample data. These sample datasets will cover a wide variety of areas such as sales, finance, management, sports, movies, etc. so that you can get your preferred type of data.

A set of sample data in Excel consists of multiple rows and columns. Each row represents a single observation and each column depicts a single variable. The sample data can be in xlsx or csv file format and users can download the files to get the dataset. Users can also use various Excel functions and features to load data from online sources and create their own datasets.

With meaningful sample data in Excel, users can perform various testing, learning, development, and data analysis tasks.

Here are 13 ideal sets of sample data in Excel:

Project Management Sample Data

A project management sample data is suitable for various types of data filtering, analyzing, and visualizing. Here are the variables that we have included in the sample data:

• Project Name
• Assigned to
• Days Required

Here is a preview of the project management dataset:

Download Sample Workbook

Inventory Records Sample Data in Excel

Inventory management records consist of product and stock information. In our sample dataset, we have included the following fields:

• Product Name
• Opening Stock
• Purchase/Stock-in
• Number of Units Sold
• Hand-in-stock
• Cost Price Per Unit
• Cost Price Total

Here is a preview of the inventory records sample data:

Call Center Customer Satisfaction Data

Call centers deal with customer service and receive various types of feedback from customers. In our Call Center Customer Satisfaction data, we have included the following fields:

• Customer Name
• Call Timestamp
• Response Time
• Call Duration
• Call Center

Here is a preview of our sample data:

Supermarket Sales Sample Data in Excel

Supermarket sales sample data is a popular dataset for learning and practicing your Excel skills. Here is the list of variables we have included in our supermarket sales sample data:

• Retail Price
• Order Quantity

Here is a preview of the sample supermarket sales data in Excel:

Download Practice Workbook

Employee Management Data

Employee management data contains information on all employees in an organization. In our sample employee management data in Excel, we have listed the following variables:

• Employee ID
• Designation
• Annual Salary

Here is a preview of the employee management data:

Technological Product Sample Data

Any technological product information requires various specifications. In our sample dataset, we have listed the following specifications:

• Country of Origin
• Release Date

The following image shows a preview of our sample technological product dataset:

Engineering and Manufacturing Sample Data

Engineered or manufactured products also require various specifications. Here is the list of variables we have included in our sample engineering and manufacturing sample data:

• Manufacturer
• Stock Quantity

Here is a preview of the sample dataset:

Students Marksheet Sample Data in Excel

A student mark sheet contains the student identifiers and marks in various subjects. In our sample students marksheet dataset, we have listed the following variables:

• Marks in Mathematics
• Marks in Physics
• Marks in Chemistry

Here is a preview of the sample student marksheet dataset:

2022 FIFA World Cup Performance Data

In our sample dataset, we have listed the information of each player from the World Cup-winning Argentina team. Here is the list of variables we have included:

• Player Name
• Jersey Number
• Appearances
• Goals Scored
• Assists Provided
• Dribbles per 90 Min
• Interceptions per 90 Min
• Tackles per 90 Min
• Total Duels Won per 90 Min

You can preview the sample dataset in the following image:

Tokyo Olympic Data

This sample dataset contains the team names, number of Gold, Silver, Bronze, and total medals, and ranking of teams (based on gold medal and total medal count) in the Tokyo Olympics. Here is a preview of the sample dataset:

Healthcare Insurance Sample Data in Excel

The price of healthcare insurance depends on various factors such as current age, BMI, smoking habits,  etc. In our sample healthcare insurance dataset, we have listed the following variables:

• Smoking Status
• Insurance Price

Travel Destination Distance Data in Excel

While deciding on a travel destination, we need to take the distance, available travel modes, travel duration, etc. factors into consideration. In our sample travel destination dataset, we have listed the following variables:

• Source City
• Source Latitude
• Source Longitude
• Destination City
• Destination Latitude
• Destination Longitude
• Distance (in km)
• Distance (in mile)
• Travel Mode

Netflix Movies Sample Data

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

• IMDb Rating

You can preview the dataset in the image below:

This ends our article on Excel sample data. We provided 13 ideal sample data that you can download as an xlsx file. After downloading the workbook, you can use the dataset directly in that workbook. You can also copy or import the sample data to your target workbook using Excel functions and features. We hope that the provided datasets were helpful to you. If you have any feedback or queries on this article, feel free to share them in the comment section.

Excel Sample Data: Knowledge Hub

• GST State Code List in Excel
• Historical Data of NSE Stocks in Excel

<< Go Back to Learn Excel

What is ExcelDemy?

Tags: Learn Excel

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

Thanks for the DATA SETS !

Hello Margit ,

You are most welcome.

Regards ExcelDemy

I needed Oil parameters Offshore Foundation design 2015 excel sheet access

Hello Ameer Ahmed ,

We don’t have any dataset you asked for and your question is not clear enough. Our datasets are given in the article.

Thank you so much for the data sets.

Hello Gaurav,

Leave a reply Cancel reply

ExcelDemy is a place where you can learn Excel, and get solutions to your Excel & Excel VBA-related problems, Data Analysis with Excel, etc. We provide tips, how to guide, provide online training, and also provide Excel solutions to your business problems.

Contact  |  Privacy Policy  |  TOS

• User Reviews
• List of Services
• Service Pricing

• Create Basic Excel Pivot Tables
• Excel Formulas and Functions
• Excel Charts and SmartArt Graphics
• Advanced Excel Training
• Data Analysis Excel for Beginners

Advanced Excel Exercises with Solutions PDF

Can an Excel spreadsheet with GPT-2 replace Copilot Pro? No, but it shows us how AI works.

What you need to know.

• A software developer named Ishan Anand put the entire GPT-2 small model inside a 1.25GB Excel spreadsheet.
• The model was placed inside the spreadsheet to illustrate how AI processes information.
• Anand has a series of videos walking through how the spreadsheet works and explaining insights learned by placing GPT-2 inside Excel.

Microsoft wants to pack GPT technology into just about every service and app it owns these days, and other tech companies are eager to integrate GPT models into various services. But what happens when you put all of GPT-2 inside an Excel spreadsheet? Rather than a tool for performing tasks or coding, you end up with a teaching tool.

According to Ishan Anand, the software developer behind " spreadsheets are all you need ," "if you can understand a spreadsheet, then you can understand AI!" Those aren't just empty marketing words either. Anand placed the entirety of GPT-2 inside an Excel spreadsheet to illustrate how AI functions. Specifically, GPT-2 small has been packed inside of Excel to teach how AI works.

As you would expect from a sheet that holds an entire GPT model, you may run into issues when navigating the spreadsheet. "Unfortunately, it is not unusual for Excel to lock up (but only on a Mac) while using this spreadsheet," said Anand. "It is highly recommended to use the manual calculation mode in Excel and the Windows version of Excel (either on a Windows directory or via Parallels on a Mac)."

Anand has a series of lessons that use the spreadsheet to illustrate how AI works. The videos break things down in a way that makes it much easier to stand how a model receives information, categorizes it, and then acts. You can download the spreadsheet used by Anand through GitHub .

The first lesson is a 10-minute video called Demystifying GPT with Excel. The video illustrates how GPT-2 processes information, providing insight into how models function and process information. The second lesson goes deeper into detail, including explaining the tokenization phase and the Byte Pair Encoding algorithm that's used in models like ChatGPT.

Anand has an extra lesson that goes even deeper to explain how he used Excel throughout his lessons.

GPT-2 is a precursor to more powerful GPT tech, such as GPT-4 Turbo that now powers the free version of Microsoft Copilot . GPT-2 was around before "chat" was added to a GPT model.

Grounding AI

While there are genuine security concerns surrounding AI, a lot of fear about technology comes from a lack of information or misinformation. Talking with people about AI reminds me of when I spoke with a person afraid of using a car with an automatic transmission because "the car may just drive itself and get in a crash!" People hear tidbits about tech and then conflate things that sound similar with cursory knowledge but are actually quite different.

Videos like the ones Anand shared help illustrate how AI models work rather than focusing on what they can do. In addition to being interesting and educational, the videos show how far AI has to go before it starts looking like Skynet. Of course, if we ever do reach Skynet levels of AI, we should have an emergency brake in place , thanks to Microsoft President Brad Smith and regulations.

Innovation at work: Iowa State's Research Park paving the way to success with soybean additive

At the Iowa State University Research Park, even sidewalks are paved with innovation.

Nacu Hernandez strolls down a path paved with asphalt just across the road from Iowa State’s Lloyd Veterinary Medical Center. A harsh wind scours the landscape on a cold morning in late February, and the sunshine does little to warm the rugged gray surface of the path. Hernandez kneels and places his hand on the cold asphalt. To the untrained eye, it looks and feels just like the kind of pavement that covers countless square miles of roads and parking lots the world over.

In fact, that’s the point.

The asphalt is durable and long-lasting, two qualities that are the material’s main selling points. But the asphalt on this path in Ames contains a soybean-derived additive developed by  SoyLei Innovations , a company Hernandez helped found. The additive makes the asphalt even more durable under harsh environmental conditions. But what really sets apart the soybean-treated surface is that it’s made of 100% recycled material. Applying the soybean additive to old asphalt means it can be reused, cutting down on its carbon footprint and saving money through improved efficiency.

SoyLei recently moved into office and lab space in the  Iowa State University Research Park  just a short drive north of the asphalt path. At the Research Park, innovative ideas like SoyLei’s additive take shape and hit the market, creating jobs and generating economic waves that reach far beyond central Iowa.

In addition to the Research Park’s wealth of state-of-the-art office and lab space, companies set up shop there to access the university’s vast array of expertise and equipment. Firms that lease space at the Research Park can also capitalize on a ready employment pool of well-educated graduates steeped in the university’s culture of innovation.

The ISU Research Park stretches across more than 550 acres south of Iowa State’s main campus. The development serves around 135 tenant companies, with more than 1 million square feet of developed building space. The park has attracted some of the world’s most recognizable brands, such as John Deere and Merck Animal Health. Companies with a presence at the Research Park employ more than 9,700 workers across Iowa.

“The Iowa State University Research Park serves as a launchpad for innovation and provides a place for innovators to flourish, including groundbreaking workforce solutions and research and technological advancements that shape our future,” said Alison Doyle, associate director of the Research Park. “Through our strategic partnerships and dynamic community, we spur economic growth, entrepreneurship and job creation across our state and beyond. Our commitment to customer service and collaboration fuels a thriving environment where ideas take shape and innovation transforms industries.”

The Research Park is also home to startups like SoyLei that only recently have taken their first leaps off the drawing board and into the marketplace. The research park reached over 98% occupancy in fiscal year 2023 when 15 tenants leased space or added to their existing space.

“For us, it was a no brainer to locate at the Research Park, especially with the relationship that the Research Park has with Iowa State,” Hernandez said.

More: Search for Ames' next superintendent heats up, candidates remain confidential

Innovative science, economic opportunity

Hernandez, a research scientist in the ISU Department of Chemical and Biological Engineering, founded SoyLei with a few colleagues in 2020 to find innovative uses for soybean oil. The fledgling company began developing an additive to make asphalt more resilient to wear and extreme temperatures. Most of the company’s products are applied to roads and parking lots, but they’re developing applications in construction processes, such as roofing shingles, to make them stronger and longer-lasting.

Scientists modify soybean oil with an organic compound called epoxide. The resulting product has properties that rejuvenate old asphalt. Soybean oil is an ideal carrier for the epoxide because it’s relatively cheap and plentiful in Iowa, a leading U.S. soybean producer.

The additive can augment petroleum products to provide a range of environmental benefits. Paving roads with asphalt releases carbon into the atmosphere and requires heavy energy use. But, mixing the SoyLei additive with virgin asphalt lowers the temperature it must reach in order to be used, making the process less energy intensive. The additive also allows for the inclusion of a higher proportion of recycled materials, leading to less carbon emissions in its production.

When Iowa State announced plans to build CYTown, a business and entertainment development at the Iowa State Center, the plan called for temporary parking lots to accommodate construction. SoyLei’s additive allowed the asphalt used in the temporary parking lots to be reused. The technology creates the potential for significant cost and energy savings, and less waste is better for the environment.

The company, which currently employs six people, sold roughly 300,000 pounds of its additive last year, Hernandez said, and SoyLei has doubled its sales each year since its founding.

SoyLei moved into office space at the ISU Research Park in the summer of 2022. The location gives the company easy access to lab space and essential equipment like compressed air, fume hoods, and vacuums. The space at the office park comes “set up for research, which is exactly what we need,” Hernandez said.

More: From an ABBA tribute to ‘Mean Girls,’ see the shows coming to Stephens Auditorium in April

Research Park products strengthen existing Iowa companies

Janas Materials Inc. grew from the research of Shan Jiang, an associate professor of materials science and engineering at Iowa State. The company is researching innovative water-based coating materials that could be used as an eco-friendly alternative to organic solvent-based products. The technology has the potential to reach far beyond Ames and expand choices for consumers across the country.

Jiang explained that water-based coatings, while environmentally friendly, simply do not perform as well as solvent-based products. To find the best of both worlds, his lab developed a chemical additive and approached Diamond Vogel, an Iowa-based paint company, with samples.

Diamond Vogel officials were intrigued enough to ink a research and development contract with Janas Materials to scale up production of the particle additive. The partnership with Janas Materials has strengthened Diamond Vogel’s relationship with Iowa State University, said Doug Vogel, the company’s vice president of marketing and strategic relationships and an ISU alum.

“It’s been energizing for our staff to interface not only with Dr.  Jiang but also with the grad students and students doing the work on the project as well,” Vogel said. 

Diamond Vogel employs roughly 1,000 workers, including around 300 employees in Orange City, where the company was founded in 1926. Vogel said he’s excited by the prospect of harnessing the additive under development by Janas to bring a new product to the water-based coatings market. He noted it’s still early in the relationship between the two companies, but preliminary results look promising.

“In chemistry, they’re often working at such a high level that’s so theoretical that it can be hard to have practical application,” Vogel said. “In this project with Janas, it’s getting more and more practical all the time.”

Diamond Vogel asked for additional samples of the additive under development in the Janas Materials lab. Since that requires more space and equipment, Jiang decided starting a company would be the best way to accommodate the partnership.

Jiang and his colleagues turned to the Research Park to work out the logistics.

The Research Park fits alongside the  ISU Startup Factory  and the  Pappajohn Center for Entrepreneurship  as a comprehensive suite of resources for startup businesses at Iowa State. The combination of programs makes Iowa State a fertile ground for faculty, scientists and staff to turn innovative research into successful businesses that drive economic development.

“It’s truly an ecosystem. If you wanted to do it alone, you’d just have too many things you’d have to consider,” Jiang said. “I think it would be impossible to translate our technology into a viable product without these resources.”

Jiang’s background as a scientist did not prepare him for the practical realities of creating and running a business, but the Research Park offered the help and answers he needed.

Research Park personnel helped the budding business land the lab space it needed to scale up from a 10-gallon reaction tank to a 200-gallon reaction tank. The Research Park also helped the company comply with all the government safety regulations for the use of flammable chemistry components. Research Park personnel didn’t just show Jiang space in park buildings. They also showed the company space at the BioCentury Research Farm and other locations on campus. The company chose to move into lab space in the Food Science Building as the first step and will continue to work with the Research Park to identify space for future scale-up and R&D efforts as the business expands.

“You need lab space if you want to do a startup right. I had no clue how to get space and equipment. I had no clue how to establish a business and deal with finance or human resources,” Jiang said. “Iowa State provides a huge range of help, from the Startup Factory to the Research Park, which helped me immensely as I figured these things out.”

Fred Love covers design, student affairs, the Iowa State lectures program and Reiman Gardens for the Iowa State News Service.   This piece can be found on the  Iowa State News Service website .

Main Container

Prime Minister of Canada Justin Trudeau

Search form Mobile

• Securing Canada’s AI advantage

Subscribe to email updates

Search form

Main content.

This website is not compatible with Internet Explorer or older version of Microsoft Edge(version 78 and older).

For full functionality please use a supported browser .

• news releases

Artificial intelligence (AI) has incredible potential to transform the economy, improve the way we work, and enhance our way of life. The global race to scale up and adopt AI is on, and Canada is at the forefront of this technology. To make sure we can seize every opportunity in the economy of the future, and set every generation up for success, we need to scale up our innovation ambitions. And do it in a way that brings everyone along. For Millennials and Gen Z, who feel their hard work isn’t paying off like it did for previous generations, we must invest in good-paying opportunities that help them get ahead. That’s why we’re focused on creating more good jobs, including in innovation and technology, which are among the highest paying of all industries.

AI is already unlocking massive growth in industries across the economy. Many Canadians are already feeling the benefits of using AI to work smarter and faster. The rapid advance of generative AI today will unlock immense economic potential for Canada, significantly improving productivity and reducing the time workers have to spend on repetitive tasks. Researchers and companies in Canada are also using AI to create incredible new innovations and job opportunities across all facets of the Canadian economy, from drug discovery to energy efficiency to housing innovation. In the past year, job growth in AI increased by nearly one third in Canada – among the highest growth of any sector. And most AI jobs pay well above the average income.

Canada has a world-leading AI ecosystem – from development, to commercialization, to safety. We have an advantage that can make sure Canadian values and Canadian ideas help shape this globally in-demand technology. Canada was the first country in the world to introduce a national AI strategy and has invested over $2 billion since 2017 to support AI and digital research and innovation. Since then, countries around the world have begun investing significant funding and efforts into AI to advance their economies, particularly in computing infrastructure. In order to maintain Canada’s competitive edge, and secure good paying jobs and job security for generations of young Canadians, we must raise the bar.

The Prime Minister, Justin Trudeau, today announced a $2.4 billion package of measures from the upcoming Budget 2024 to secure Canada’s AI advantage. These investments will accelerate job growth in Canada’s AI sector and beyond, boost productivity by helping researchers and businesses develop and adopt AI, and ensure this is done responsibly.

These measures include:

• Investing $2 billion to build and provide access to computing capabilities and technological infrastructure for Canada’s world-leading AI researchers, start-ups, and scale-ups. As part of this investment, we will soon be consulting with AI stakeholders to inform the launch of a new AI Compute Access Fund to provide near-term support to researchers and industry. We will also develop a new Canadian AI Sovereign Compute Strategy to catalyze the development of Canadian-owned and located AI infrastructure. Ensuring access to cutting-edge computing infrastructure will attract more global AI investment to Canada, develop and recruit the best talent, and help Canadian businesses compete and succeed on the world stage.
• Boosting AI start-ups to bring new technologies to market, and accelerating AI adoption in critical sectors , such as agriculture, clean technology, health care, and manufacturing, with $200 million in support through Canada’s Regional Development Agencies.
• Investing $100 million in the NRC IRAP AI Assist Program to help small and medium-sized businesses scale up and increase productivity by building and deploying new AI solutions. This will help companies incorporate AI into their businesses and take on research, product development, testing, and validation work for new AI-based solutions.
• Supporting workers who may be impacted by AI, such as creative industries, with $50 million for the Sectoral Workforce Solutions Program, which will provide new skills training for workers in potentially disrupted sectors and communities.
• Creating a new Canadian AI Safety Institute, with $50 million to further the safe development and deployment of AI. The Institute, which will leverage input from stakeholders and work in coordination with international partners, will help Canada better understand and protect against the risks of advanced or nefarious AI systems, including to specific communities.
• Strengthening enforcement of the Artificial Intelligence and Data Act, with $5.1 million for the Office of the AI and Data Commissioner. The proposed Act aims to guide AI innovation in a positive direction to help ensure Canadians are protected from potential risks by ensuring the responsible adoption of AI by Canadian businesses.

Today’s announcement is about investing in innovation and economic growth to secure Canada’s world-leading AI advantage today and for generations to come. This will create good-paying opportunities for every generation, boost innovation across the economy, raise productivity, and accelerate economic growth – and it’s just one of the things that we are going to be doing in Budget 2024. Alongside these measures, we’re building more homes faster, ensuring every kid has the food they need, investing in health care, making life more affordable, and creating good jobs to make sure every generation can get ahead.

“AI has the potential to transform the economy. And our potential lies in capitalizing on the undeniable Canadian advantage. These investments in Budget 2024 will help harness the full potential of AI so Canadians, and especially young Canadians, can get good-paying jobs while raising our productivity, and growing our economy. This announcement is a major investment in our future, in the future of workers, in making sure that every industry, and every generation, has the tools to succeed and prosper in the economy of tomorrow.” The Rt. Hon. Justin Trudeau, Prime Minister of Canada

“Today, we are making a significant investment to boost our economic growth. This will keep Canada a global leader in AI and ensure we are at the very cutting-edge of new technologies. And most importantly, this will mean more high-paying careers for Canadians who are leading the charge in AI.” The Hon. Chrystia Freeland, Deputy Prime Minister and Minister of Finance

Quick Facts

• The Government of Canada’s Budget 2024 will be tabled in the House of Commons by the Deputy Prime Minister and Minister of Finance on Tuesday, April 16, 2024.
• In 2017, Canada was the first country to establish a national AI strategy. The Pan-Canadian Artificial Intelligence Strategy is helping Canada maintain its position as a world leader in AI, businesses be more competitive, and Canadians benefit from growth in the digital economy. Phase 2 of the strategy was announced in 2022 with funding of more than $443 million.
• The federal research granting agencies – the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council (NSERC), and the Social Sciences and Humanities Research Council (SSHRC) – together have awarded $936.8 million in funding for AI-related research since 2017-18.
• Since 2017, the National Research Council of Canada Industrial Research Assistance Program (NRC IRAP) provided $705.8 million in contributions to AI-related firms. This funding supported 1,111 firms and 3,837 projects in the AI and Big Data Technology space.
• In addition, the NRC Digital Technologies Research Centre has invested over $27 million both directly to firms and on collaborative AI projects related to natural language processing, Indigenous languages, and high-performance computing for AI.
• In 2023, Canada announced renewed funding for the Global Innovation Clusters , including Scale AI , bringing total funding for the company to up to $284 million. Scale AI is dedicated to promoting collaboration in AI and supply chain management research and innovation by strengthening linkages between researchers in industry, academia, and research institutes in Canada and abroad, and providing financial support for AI and supply chain management projects.
• Canada has also made significant investments in fast-scaling AI-related companies through the Strategic Innovation Fund , including Sanctuary AI and semiconductor firm Ranovus .
• Canada was recently ranked number 1 among 80 countries, tied with South Korea and Japan, in the Center for AI and Digital Policy’s 2024 global report on Artificial Intelligence and Democratic Values .
• The Artificial Intelligence and Data Act (AIDA) was introduced in Parliament as part of Bill C-27 in June 2022. It is designed to promote the responsible design, development, and use of AI systems in Canada’s private sector, with a focus on systems with the greatest impact on health, safety, and human rights. Since the introduction of the bill, the government has engaged extensively with stakeholders on the novel challenges posed by generative AI. Canada is one of the first countries in the world to propose a law to regulate AI. Learn more .
• The Voluntary Code of Conduct on the Responsible Development and Management of Advanced Generative AI Systems – announced in September 2023 and signed by major tech firms including Cohere, Ada, Coveo, BlackBerry, TELUS, OpenText, and IBM – enables companies to demonstrate that they are developing and using generative AI systems responsibly and strengthen Canadians’ confidence in the technology.
• The Public Awareness Working Group on AI was launched in 2020 under Canada’s Advisory Council on Artificial Intelligence with a mandate to examine avenues to boost public awareness and foster trust in AI. Its objective is to help Canadians have a more grounded conversation around AI, and help citizens better understand the technology, its potential uses, and its associated risks. The Working Group published a report on its public engagement activities in February 2023. A further public report is upcoming specifically on the Working Group’s engagement with First Nations, Inuit, and Métis communities to better understand their needs, interests, and priorities for AI development and use.
• Since the 1990s, Canada has been a leader in AI and deep learning, made possible by the research and innovations of the “Godfathers of AI”, Canadians Yoshua Bengio and Geoffrey Hinton. In the decades since, Canada has built up a robust and growing AI industry across Canada, anchored by our three national AI institutes in Montréal, Toronto, and Edmonton.
• In 2022-23, there were over 140,000 actively engaged AI professionals in Canada, an increase of 29 per cent compared to the previous year.
• Canada has 10 per cent of the world’s top-tier AI researchers, the second most in the world.
• Canada ranks first globally for year-over-year growth of women in AI (67 per cent growth in 2022-23 alone), first in the G7 for year-over-year growth of AI talent, and since 2019, has ranked first in the G7 for the number of AI-related papers published per capita.
• The number of AI patents filed by Canadian investors increased by 57 per cent in 2022-23 compared to the previous year – nearly three times the G7 average of just 23 per cent over the same period.
• In 2022, the Canadian AI sector attracted over $8.6 billion in venture capital, accounting for nearly 30 per cent of all venture capital activity in Canada.
• Canada ranks third in the G7 in total funding per capita raised for AI companies, with more than 670 Canadian AI start-ups and 30 Canadian generative AI companies receiving at least one investment deal valued at more than $1 million USD since 2019.
• Restore generational fairness for renters, particularly Millennials and Gen Z, by taking new action to protect renters’ rights and unlock pathways for them to become homeowners. Learn more .
• Save more young families money and help more moms return to their careers by building more affordable child care spaces and training more early childhood educators across Canada. Learn more .
• Create a National School Food Program to provide meals to about 400,000 kids every year and help ensure every child has the best start in life, no matter their circumstances. Learn more .
• Launch a new $6 billion Canada Housing Infrastructure Fund to accelerate the construction or upgrade of essential infrastructure across the country and get more homes built for Canadians. Learn more .
• Top-up the Apartment Construction Loan Program with $15 billion, make new reforms so it is easier to access, and launch Canada Builds to call on all provinces and territories to join a Team Canada effort to build more homes, faster. Learn more .
• Support renters by launching a new $1.5 billion Canada Rental Protection Fund to preserve more rental homes and make sure they stay affordable. Learn more .
• Change the way we build homes in Canada by announcing over $600 million to make it easier and cheaper to build more homes, faster, including through a new Homebuilding Technology and Innovation Fund and a new Housing Design Catalogue. Learn more .

Associated Links

• Responsible use of artificial intelligence (AI)
• Sectoral Workforce Solutions Program

Read our research on: Gun Policy | International Conflict | Election 2024

Regions & Countries

3. problems students are facing at public k-12 schools.

We asked teachers about how students are doing at their school. Overall, many teachers hold negative views about students’ academic performance and behavior.

• 48% say the academic performance of most students at their school is fair or poor; a third say it’s good and only 17% say it’s excellent or very good.
• 49% say students’ behavior at their school is fair or poor; 35% say it’s good and 13% rate it as excellent or very good.

Teachers in elementary, middle and high schools give similar answers when asked about students’ academic performance. But when it comes to students’ behavior, elementary and middle school teachers are more likely than high school teachers to say it’s fair or poor (51% and 54%, respectively, vs. 43%).

Teachers from high-poverty schools are more likely than those in medium- and low-poverty schools to say the academic performance and behavior of most students at their school are fair or poor.

The differences between high- and low-poverty schools are particularly striking. Most teachers from high-poverty schools say the academic performance (73%) and behavior (64%) of most students at their school are fair or poor. Much smaller shares of teachers from low-poverty schools say the same (27% for academic performance and 37% for behavior).

In turn, teachers from low-poverty schools are far more likely than those from high-poverty schools to say the academic performance and behavior of most students at their school are excellent or very good.

Lasting impact of the COVID-19 pandemic

Among those who have been teaching for at least a year, about eight-in-ten teachers say the lasting impact of the pandemic on students’ behavior, academic performance and emotional well-being has been very or somewhat negative. This includes about a third or more saying that the lasting impact has been very negative in each area.

Shares ranging from 11% to 15% of teachers say the pandemic has had no lasting impact on these aspects of students’ lives, or that the impact has been neither positive nor negative. Only about 5% say that the pandemic has had a positive lasting impact on these things.

A smaller majority of teachers (55%) say the pandemic has had a negative impact on the way parents interact with teachers, with 18% saying its lasting impact has been very negative.

These results are mostly consistent across teachers of different grade levels and school poverty levels.

Major problems at school

When we asked teachers about a range of problems that may affect students who attend their school, the following issues top the list:

• Poverty (53% say this is a major problem at their school)
• Chronic absenteeism – that is, students missing a substantial number of school days (49%)
• Anxiety and depression (48%)

One-in-five say bullying is a major problem among students at their school. Smaller shares of teachers point to drug use (14%), school fights (12%), alcohol use (4%) and gangs (3%).

Differences by school level

Similar shares of teachers across grade levels say poverty is a major problem at their school, but other problems are more common in middle or high schools:

• 61% of high school teachers say chronic absenteeism is a major problem at their school, compared with 43% of elementary school teachers and 46% of middle school teachers.
• 69% of high school teachers and 57% of middle school teachers say anxiety and depression are a major problem, compared with 29% of elementary school teachers.
• 34% of middle school teachers say bullying is a major problem, compared with 13% of elementary school teachers and 21% of high school teachers.

Not surprisingly, drug use, school fights, alcohol use and gangs are more likely to be viewed as major problems by secondary school teachers than by those teaching in elementary schools.

Differences by poverty level

Teachers’ views on problems students face at their school also vary by school poverty level.

Majorities of teachers in high- and medium-poverty schools say chronic absenteeism is a major problem where they teach (66% and 58%, respectively). A much smaller share of teachers in low-poverty schools say this (34%).

Bullying, school fights and gangs are viewed as major problems by larger shares of teachers in high-poverty schools than in medium- and low-poverty schools.

When it comes to anxiety and depression, a slightly larger share of teachers in low-poverty schools (51%) than in high-poverty schools (44%) say these are a major problem among students where they teach.  

Discipline practices

About two-thirds of teachers (66%) say that the current discipline practices at their school are very or somewhat mild – including 27% who say they’re very mild. Only 2% say the discipline practices at their school are very or somewhat harsh, while 31% say they are neither harsh nor mild.

We also asked teachers about the amount of influence different groups have when it comes to determining discipline practices at their school.

• 67% say teachers themselves don’t have enough influence. Very few (2%) say teachers have too much influence, and 29% say their influence is about right.

• 31% of teachers say school administrators don’t have enough influence, 22% say they have too much, and 45% say their influence is about right.
• On balance, teachers are more likely to say parents, their state government and the local school board have too much influence rather than not enough influence in determining discipline practices at their school. Still, substantial shares say these groups have about the right amount of influence.

Teachers from low- and medium-poverty schools (46% each) are more likely than those in high-poverty schools (36%) to say parents have too much influence over discipline practices.

In turn, teachers from high-poverty schools (34%) are more likely than those from low- and medium-poverty schools (17% and 18%, respectively) to say that parents don’t have enough influence.

Social Trends Monthly Newsletter

Sign up to to receive a monthly digest of the Center's latest research on the attitudes and behaviors of Americans in key realms of daily life

Report Materials

Table of contents, ‘back to school’ means anytime from late july to after labor day, depending on where in the u.s. you live, among many u.s. children, reading for fun has become less common, federal data shows, most european students learn english in school, for u.s. teens today, summer means more schooling and less leisure time than in the past, about one-in-six u.s. teachers work second jobs – and not just in the summer, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .