Excel for the machine

Microsoft Excel is a spreadsheet developed by Microsoft for Windows, macOS, Android, iOS and iPadOS. It features calculation or computation capabilities, graphing tools, pivot tables, and a macro programming language called Visual Basic for Applications (VBA). Excel forms part of the Microsoft 365 suite of software.

Microsoft Excel

A simple bar graph being created in Excel, running on Windows 11
Developer(s)	Microsoft
Initial release	November 19, 1987; 35 years ago
Stable release	2103 (16.0.13901.20400) / April 13, 2021; 23 months ago[1]
Written in	C++ (back-end)[2]
Operating system	Microsoft Windows
Type	Spreadsheet
License	Trialware[3]
Website	microsoft.com/en-us/microsoft-365/excel

Microsoft Excel for Mac

Excel for Mac (version 16.67), running on macOS Big Sur 11.5.2
Developer(s)	Microsoft
Initial release	September 30, 1985; 37 years ago
Stable release	16.70 (Build 23021201) / February 14, 2023; 54 days ago[4]
Written in	C++ (back-end), Objective-C (API/UI)[2]
Operating system	macOS
Type	Spreadsheet
License	Proprietary commercial software
Website	products.office.com/mac

Microsoft Excel for Android

Excel for Android running on Android 13
Developer(s)	Microsoft Corporation
Stable release	16.0.14729.20146 / December 22, 2021; 15 months ago[5]
Operating system	Android Oreo and later
Type	Spreadsheet
License	Proprietary commercial software
Website	products.office.com/en-us/excel

Microsoft Excel for iOS and iPadOS

Developer(s)	Microsoft Corporation
Stable release	2.70.1 / February 15, 2023; 53 days ago[6]
Operating system	iOS 15 or later iPadOS 15 or later
Type	Spreadsheet
License	Proprietary commercial software
Website	products.office.com/en-us/excel

Features

Data storage and communication

<?xml version="1.0"?>
<Workbook xmlns="urn:schemas-microsoft-com:office:spreadsheet"
 xmlns:o="urn:schemas-microsoft-com:office:office"
 xmlns:x="urn:schemas-microsoft-com:office:excel"
 xmlns:ss="urn:schemas-microsoft-com:office:spreadsheet"
 xmlns:html="http://www.w3.org/TR/REC-html40">
 <Worksheet ss:Name="Sheet1">
  <Table ss:ExpandedColumnCount="2" ss:ExpandedRowCount="2" x:FullColumns="1" x:FullRows="1">
   <Row>
    <Cell><Data ss:Type="String">Name</Data></Cell>
    <Cell><Data ss:Type="String">Example</Data></Cell>
   </Row>
   <Row>
    <Cell><Data ss:Type="String">Value</Data></Cell>
    <Cell><Data ss:Type="Number">123</Data></Cell>
   </Row>
  </Table>
 </Worksheet>
</Workbook>

Other platforms

Microsoft Excel Viewer

Quirks

Versions

Impact

Security

Games

See also

References

References

External links

The Office spreadsheet program

Within the Microsoft Office suite, Word and Excel are probably the two most popular applications among users. If the first one is the editor and word processor par excellence, the second one is undoubtedly the most used spreadsheet program in the world, both at home and professionally. Download Microsoft Excel for PC to work with different sheets and tables and all kinds of numerical and alphanumeric data, as well as create graphs and insert formulas, which is very useful for any type of financial, statistical or engineering task.

Its integration with Visual Basic for Applications also makes it very useful for anyone who wants to program small forms or applications with simple graphical user interfaces. And of course its sorting and filtering functions make it perfect for searching and comparing different data.

Display strings of data in the way that suits you best.

With all this it is normal that over the years it has become a standard, replacing other applications of the same style, such as Lotus 1-2-3 or VisiCalc. Therefore, any computer equipped with Windows must have a version of Excel installed or accessible in the cloud. Or if not Microsoft’s solution, at least some alternative from other office packages such as Calc from LibreOffice or OpenOffice.

Main features

These are the main features you can start enjoying once you download Microsoft Excel for PC:

• Microsoft Office native spreadsheet editor.
• Create custom spreadsheets or import data from databases or other documents.
• Inserts tables with numeric and alphanumeric values and applies formulas and mathematical equations to treat those data.
• Filter table values according to different criteria and sort them in a customized way.
• Generate charts of all types to represent cell values: column charts, bar charts, scatter charts, area charts, line charts, pie charts, etc.
• Program macros to automate tasks that you perform repeatedly.
• Work collaboratively with several users editing the same file at the same time.
• Access all your spreadsheets from any device thanks to its synchronization with OneDrive.
• Compatible with a multitude of formats including .xls, .xml, and .csv.

What does the Excel 365 version have that previous editions did not have?

This version of Excel 365 replaces the edition that was included in the Office 2019 package. With respect to this version, we can list the following new features:

• New linked data types are added: the new linked data types show data on hundreds of topics to help the user achieve their goals in Excel.
• Includes a new Accessibility ribbon where to find all the necessary tools to create accessible content.
• Conditional formatting dialog boxes are improved.
• You can create a custom data type in Power Query that allows you to load multiple related parts in a column.
• Multiple spreadsheets can be displayed at the same time.
• The user can create variables for use in formulas.
• An Excel file can be sorted and filtered while collaborating with others using Sheet View. This prevents sorting and filtering performed by other users from being affected.
• You can have a conversation directly in the spreadsheet using the integrated reply box and alert colleagues using @mentions.

What’s new in the latest version

• Microsoft has not announced the changes included in this release.

Requirements and additional information:

• Minimum operating system requirements:
  Windows 7.

This article is part of “AI education”, a series of posts that review and explore educational content on data science and machine learning. (In partnership with Paperspace)

Machine learning and deep learning have become an important part of many applications we use every day. There are few domains that the fast expansion of machine learning hasn’t touched. Many businesses have thrived by developing the right strategy to integrate machine learning algorithms into their operations and processes. Others have lost ground to competitors after ignoring the undeniable advances in artificial intelligence.

But mastering machine learning is a difficult process. You need to start with a solid knowledge of linear algebra and calculus, master a programming language such as Python, and become proficient with data science and machine learning libraries such as Numpy, Scikit-learn, TensorFlow, and PyTorch.

And if you want to create machine learning systems that integrate and scale, you’ll have to learn cloud platforms such as Amazon AWS, Microsoft Azure, and Google Cloud.

Naturally, not everyone needs to become a machine learning engineer. But almost everyone who is running a business or organization that systematically collects and processes can benefit from some knowledge of data science and machine learning. Fortunately, there are several courses that provide a high-level overview of machine learning and deep learning without going too deep into math and coding.

But in my experience, a good understanding of data science and machine learning requires some hands-on experience with algorithms. In this regard, a very valuable and often-overlooked tool is Microsoft Excel.

To most people, MS Excel is a spreadsheet application that stores data in tabular format and performs very basic mathematical operations. But in reality, Excel is a powerful computation tool that can solve complicated problems. Excel also has many features that allow you to create machine learning models directly into your workbooks.

While I’ve been using Excel’s mathematical tools for years, I didn’t come to appreciate its use for learning and applying data science and machine learning until I picked up Learn Data Mining Through Excel: A Step-by-Step Approach for Understanding Machine Learning Methods by Hong Zhou.

Learn Data Mining Through Excel takes you through the basics of machine learning step by step and shows how you can implement many algorithms using basic Excel functions and a few of the application’s advanced tools.

While Excel will in no way replace Python machine learning, it is a great window to learn the basics of AI and solve many basic problems without writing a line of code.

Linear regression machine learning with Excel

Linear regression is a simple machine learning algorithm that has many uses for analyzing data and predicting outcomes. Linear regression is especially useful when your data is neatly arranged in tabular format. Excel has several features that enable you to create regression models from tabular data in your spreadsheets.

One of the most intuitive is the data chart tool, which is a powerful data visualization feature. For instance, the scatter plot chart displays the values of your data on a cartesian plane. But in addition to showing the distribution of your data, Excel’s chart tool can create a machine learning model that can predict the changes in the values of your data. The feature, called Trendline, creates a regression model from your data. You can set the trendline to one of several regression algorithms, including linear, polynomial, logarithmic, and exponential. You can also configure the chart to display the parameters of your machine learning model, which you can use to predict the outcome of new observations.

You can add several trendlines to the same chart. This makes it easy to quickly test and compare the performance of different machine learning models on your data.

In addition to exploring the chart tool, Learn Data Mining Through Excel takes you through several other procedures that can help develop more advanced regression models. These include formulas such as LINEST and LINREG formulas, which calculate the parameters of your machine learning models based on your training data.

The author also takes you through the step-by-step creation of linear regression models using Excel’s basic formulas such as SUM and SUMPRODUCT. This is a recurring theme in the book: You’ll see the mathematical formula of a machine learning model, learn the basic reasoning behind it, and create it step by step by combining values and formulas in several cells and cell arrays.

While this might not be the most efficient way to do production-level data science work, it is certainly a very good way to learn the workings of machine learning algorithms.

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Other machine learning algorithms with Excel

Beyond regression models, you can use Excel for other machine learning algorithms. Learn Data Mining Through Excel provides a rich roster of supervised and unsupervised machine learning algorithms, including k-means clustering, k-nearest neighbor, naïve Bayes classification, and decision trees.

The process can get a bit convoluted at times, but if you stay on track, the logic will easily fall in place. For instance, in the k-means clustering chapter, you’ll get to use a vast array of Excel formulas and features (INDEX, IF, AVERAGEIF, ADDRESS, and many others) across several worksheets to calculate cluster centers and refine them. This is not a very efficient way to do clustering, you’ll be able to track and study your clusters as they become refined in every consecutive sheet. From an educational standpoint, the experience is very different from programming books where you provide a machine learning library function your data points and it outputs the clusters and their properties.

In the decision tree chapter, you will go through the process calculating entropy and selecting features for each branch of your machine learning model. Again, the process is slow and manual, but seeing under the hood of the machine learning algorithm is a rewarding experience.

In many of the book’s chapters, you’ll use the Solver tool to minimize your loss function. This is where you’ll see the limits of Excel, because even a simple model with a dozen parameters can slow your computer down to a crawl, especially if your data sample is several hundred rows in size. But the Solver is an especially powerful tool when you want to finetune the parameters of your machine learning model.

Deep learning and natural language processing with Excel

Learn Data Mining Through Excel shows that Excel can even advanced machine learning algorithms. There’s a chapter that delves into the meticulous creation of deep learning models. First, you’ll create a single layer artificial neural network with less than a dozen parameters. Then you’ll expand on the concept to create a deep learning model with hidden layers. The computation is very slow and inefficient, but it works, and the components are the same: cell values, formulas, and the powerful Solver tool.

In the last chapter, you’ll create a rudimentary natural language processing (NLP) application, using Excel to create a sentiment analysis machine learning model. You’ll use formulas to create a “bag of words” model, preprocess and tokenize hotel reviews and classify them based on the density of positive and negative keywords. In the process you’ll learn quite a bit about how contemporary AI deals with language and how much different it is from how we humans process written and spoken language.

Excel as a machine learning tool

Whether you’re making C-level decisions at your company, working in human resources, or managing supply chains and manufacturing facilities, a basic knowledge of machine learning will be important if you will be working with data scientists and AI people. Likewise, if you’re a reporter covering AI news or a PR agency working on behalf a company that uses machine learning, writing about the technology without knowing how it works is a bad idea (I will write a separate post about the many awful AI pitches I receive every day). In my opinion, Learn Data Mining Through Excel is a smooth and quick read that will help you gain that important knowledge.

Beyond learning the basics, Excel can be a powerful addition to your repertoire of machine learning tools. While it’s not good for dealing with big data sets and complicated algorithms, it can help with the visualization and analysis of smaller batches of data. The results you obtain from a quick Excel mining can provide pertinent insights in choosing the right direction and machine learning algorithm to tackle the problem at hand.

Every wondered how Machine learning works?  In this 2 part article we are going to explain machine learning with Excel

Introduction

The world is full with a lot of data, pictures, music, text, video, spreadsheets.  Machine learning brings the promise of delivering meaning from that data.  (although Excel is not normally used as a tool for ML, in this article we will explain machine learning with Excel)

Human’s learn from experience. if we touch something hot, that burning sense is stored in memory and we quickly learn not to touch it again.  Machine learning is like human learning as it learns from the past.  We feed a computer data, which represents some past experience, and then with the use of different statistical methods, we can make predictions.

Machine learning is all around us, from using Google Search, to diagnosis of skin cancer.  There are few industries yet to feel the effect.

This include the banking and finance sector.  One of the core uses of machine learning in banking has been to combat fraud and comply with AML regulations.  Banks can also use machine learning algorithms to analyse an applicant for credit, be that an individual or a business, and make approvals according to a set of pre-defined parameters. Some of these algorithms simply look at a customers credit score, age and postcode to make a decision in seconds.

To get an idea how machine learning works, we are going to focus on a bank and how they are using machine learning to make approval on loans.  We will dive into some theory and we will set up a simple algorithm to decided if a loan should be approved or rejected.

Types of Machine Learning

There are generally 4 types of Machine learning based on their purpose:

Supervised:  This is a type of learning where by both inputs and outputs are known. For example, in supervised learning for image recognition a computer might be fed a series of pictures with labels, such as cars, vans and trucks. After feeding the computer with so many images, it should be able to classify an unlabeled image as a car, van or truck. 

It is called supervised learning because the process of an algorithm learning from the training data set is like a teacher supervising the learning of a student.

Supervised learning is used for both classification and regression problems

A classification problem is where a label is the output. Is it a car or van? Should you approve the loan or reject the loan?

A Regression problem deals with values and quantities.  Given the size of a house, predict the value. 

Unsupervised Learning:  Unsupervised learning has unlabeled input data only.  The output is not known, and it is often used as exploratory analysis.   One aim is to classify & cluster data, for example grouping customers by purchasing behaviors.  Another aim is to find association rules.  For example, if you buy product Y you are also likely to buy product Z

Semi-supervised: machine learning algorithms that are semi supervised are fed small amounts of labeled data and taught how to classify a large batch of unprocessed data.

Reinforcement Learning: When an agent interacts with an environment where some actions give rewards.  For example in a computer game, the player would be the agent.  The agent must navigate a maze, at the end, is the key to the next level.  The key is the reward, the incentive.  However, it might take the agent 2-3 attempts, each time learning from their mistake in the environment, until the finally get out of the maze.

What is a Decision Tree?

A decision tree is a set of questions used to filter information allowing you make a more informed decision. It is used in supervised learning to classify data. 

Let’s look at our bank example. For a loan to be approved by a bank, the applicant completes a list of questions and these questions are used to judge if it is safe to give the loan or not.  To keep things at a very simple level, our sample bank asks 3 questions? 

1. Do you own a home? Yes or No

2. What is your income bracket? Low, Average, Above average

3. What is your credit score? Below average, average, high

This process is the most basic form of decision tree, each question is asked and at the end, the loan is either approved or rejected.

Machine Learning and Decision Trees

Looking at our sample banks decision tree, how does the bank know which question, or attribute to start the tree with?  How can the bank ensure that they lend to people who won’t default and do it with the least amount of questions possible?

Using existing data on loan defaults, the bank can use this data as training data to teach a machine how to classify data and come up with a best direction to take with the questions that will minimize the number of loan defaults.

The bank will have existing data from loan application and will be able to tie this into a table showing who defaulted and who didn’t.  Like the sample shown. This existing data is known as training data because the machine will learn from this data.

Machine learning will use algorithms to establish the best route to take in the decision tree, based on this past experience.

Entropy and Information Gain

Entropy is a concept from Information Theory.  It is a measure of randomness of the information being processed.  In general, the more random the event, the more information it will contain. Entropy of 1 would suggest high probability and randomness and lots of information.  The closer is to 0, the less the less randomness and a lower probability.

The formula entropy for Entropy is:

Entropy of the data set(D) = -P1*log2(P1)-P2*log2(P2)…..

Where P= Probability

Looks a little complicated so let’s use it with our bank.

We want to use our training data to predict if an applicant should be approved or rejected for the loan.  The entropy of the data set would therefore be the entropy of the ‘default’ column, as this is the label we want to predict.  If they person is likely to default, then we will not lend the money.

We have 18 observations.  10 default on the loans (yes) and 8 do not default (no).  Based on this we can calculate some probabilities

Entropy and Information gain Calculations

Probability of not defaulting = 8/18 = 0.444

Probability of default = 10/18 =0.556

Now we have our probabilities we can plug these into our entropy formula

D=-0.444*log2(0.444) – 0.556*log2(.556) = 0.991

0.991 is rather close to 1.  This suggests there is a lot of randomness in the data. If there is a lot of randomness it would be very hard to decide if the loan application should be approved or rejected.  There is a lot of information in these, such as if they own a home and their credit score. If we narrow our focus to just one piece of information, can we reduce this randomness?

Let’s calculated the entropy for the ‘home owners’ column.

We have 18 observations. 8 of which are homeowners, 10 are not.

Looking at just the home owners, 8 in total, there are 4 defaulter and 4 do not default

Therefore, the probability of a home owner defaulting is 4/8 = 0.5 and the probability of a home owner not defaulting is also 4/8 = 0.5

Entropy Home Owner  =-0.5*log2(0.5) – 0.5*log2(.5) = 1

Now looking at those that do not own homes.  There are 10 in total

The probability of a non home owner defaulting is 6/10 = 0.60 and the probability of a non home owner not defaulting is 4/10 = 0.40

Entropy Non Home Owner  =-0.60*log2(0.60) – 0.40*log2(.40) = 0.971

As there were two possible answers to the home owner question, we now have two entropy values. To get the entropy for the total column, we must combine these entropys proportionally.

Probability of being a home owner * entropy home owner = 8/18 * 1= 0.444

Probability of not being a home owner * entropy of not being a home owner  = 10/18 * 0.971 = 0.539

Entropy = 0.444 + 0.539= 0.984

We can see now that the entropy for the data set has been reduced from 0.991 to 0.984.  Although a small reduction, we can see by reducing the information we have reduced the randomness. By reducing the randomness, we increase the changes of not approving someone that will default.

There is a value attached to this reduction in randomness and this is known as information gain.  Information gain can be calculated by deducting the entropy for the restricted information from the entropy for the data set.

Information gain = 0.991 – 0.984 = 0.007

This information gain calculation comes from Information theory.

How to Calculated Entropy and Information Gain In Excel (Explain Machine learning with Excel)

Now that you have be introduced to some of the probability calculations that can be used in algorithms, lets take a look at how these can be calculated in Excel.  Remember, this sample data is extremely simply and so we can easily use Excel for this demonstration.

Using Pivot tables in Excel is a very quick way of calculating probability.  All of the above seems complicated, but when you watch this video you will see how quickly you can calculated entropy and information gain

Excel is a powerful tool that helps you make quick calculations and derive insights from data gathered from various sources such as marketing data platforms. Most people see it as a spreadsheet application that stores data in tabular form, but in reality, it’s a robust computational tool that can solve complex problems.

However, to be able to use it to its full potential, you need to familiarize yourself with tons of complicated formulas. Most people don’t realize the full power of Excel because they are intimidated by the vast amount of knowledge needed to master the tool. 

This doesn’t have to be the case. With the development of AI (artificial intelligence), Excel users can intuitively enter complex formulas and extract comprehensive analyses without memorizing hundreds of long-winding Excel formulas. 

Let’s explore the different built-in and third-party AI tools for Excel. 

Built-In AI Tools in Excel

Excel is primarily composed of rows and columns of data, a powerful calculation engine, and various tools to manipulate the data entered. It’s a flexible app used by millions worldwide, from students to top-level C-executives. 

To further enhance the functionalities of Excel, Microsoft has released several AI tools to help even novice users use the tool to the next level. Here are some of them:

Analyze Data

Analyze Data (previously Ideas) is an AI-powered insights tool that gives suggestions suited to your current Excel task. It detects if you need suggestions for data visualizations, trends analysis, pattern identification, and many others. The more data you input into your Excel workbook and follow some of the suggestions, the smarter the tool becomes.

Analyze Data also accepts NLP (natural language processing) queries so you can ask questions about what the data mean and watch Excel return the answers with high-level visual summaries, patterns, and trends. 

If you’re unsure what questions to ask, the tool comes with suggested questions based on its analysis of your existing data. You can use these questions as the springboard for further analysis. 

Analyze Data helps you save time by giving focused analysis using only relevant data fields. This is incredibly helpful when working with a huge bunch of data, since you can quickly filter out irrelevant information. 

To fully realize the potential of this AI feature, you need to have clean tabular data in Excel. Avoid merged cells and use unique headers. Analyze Data is available to all users of Microsoft 365. 

Linked Data Types

Excel now makes it easier to gather data by linking certain data inputs to reputable external online resources. For example, you can quickly extract Stocks information, such as current price, price changes, etc., by typing a company name. Or you can extract Geography information, such as population, tax rates, etc., by typing a geographic country, city, state, or territory. 

There are other supported linked data types, and harnessing this AI capability lets you minimize the time spent on manual data entry while ensuring the most accurate data at all times. When a data entry is converted to a linked data type, it’s refreshed every time there are changes to the data—you only need to hit refresh. 

This AI feature also allows you to run formulas with the generated data making calculations faster and easier. 

Insert Data from Picture

Say goodbye to manual data entry because Excel converts photos into actual data in cells. When you take a photo of a data table, Excel recognizes the rows and columns and automatically adds them to individual cells.

This lets you create fully editable text from a phone image capture and helps reduce the time spent manually copying and entering data. Further, it increases accuracy and reduces error from mistakenly copying the wrong information. 

Dynamic Arrays

This AI Excel feature lets you use advanced Excel formulas with ease. When you type one formula in a cell, it automatically detects neighboring cells that need the same formula and automatically adds that formula to those cells. 

With dynamic arrays, Excel can automatically adjust the size of a range of cells to fit the data that is entered into them.

Dynamic arrays allow you to use functions that return multiple results in a single cell without the need to use formulas such as INDEX and TRANSPOSE or to manually copy and paste formulas. Some examples of functions that return dynamic arrays include SORT, UNIQUE, FILTER, and SEQUENCE.

For example, you can use the UNIQUE function to return a list of unique items from a range of data. Instead of returning a single value, the UNIQUE function will return an array of values that spill into multiple cells.

Dynamic arrays also introduce new functions that work seamlessly with these dynamic ranges, such as the SORT, FILTER, and SORTBY functions, which allow you to sort, filter, and sort by specific criteria, respectively. 

External Excel AI Tools Plugins

Aside from built-in Excel tools, there are multiple third-party external plugins that help enhance the AI capabilities of Excel. These plugins extend what you can do with Excel and lets you enjoy the benefits of AI features. 

Excel Formula Bot

One of the most popular AI tools for Excel is Excelformulabot. It’s a plugin both for Excel and Google Sheets that lets you enter a text instruction and convert it to a formula. It also works the other way – by explaining complex formula in plain English. 

You can use it as an add-in or as a web-based dashboard. They let you run five queries for free each month but additional queries require a monthly subscription. 

Excelformulabot uses GPT-3 AI technology which is a neural network machine learning technology developed from billions of data points. They boast that the tool has 97% accuracy and supports multiple languages. It also works with older versions of Excel. 

Ajelix

Another worthy AI tool to consider is Ajelix which lets you write and explain formulas fast and intuitively. You can also collaborate with others to work on your spreadsheets and translate Excel files into other languages. 

It also recognizes keywords and generates Excel formulas based on them. Ajelix also translates text to VBA scripts to take your Excel use to the next level. 

They have a free version which is great for personal use, and a paid version for teams and businesses. 

PowerExcel.ai

If you need a no-fuss AI plugin that auto-generates power queries for common data prep needs, you can purchase PowerExcel.ai. There’s a free basic version and more robust paid ones that let you simplify and automate common and repetitive Excel tasks. 

The tools are install-free and embedded easily to Excel workbooks. It includes dynamic templates that can be shared across different workbooks allowing for easy collaboration. There are dozens of dynamic array functions and Excel formulas that expand your capabilities of using the Excel spreadsheet.

PowerExcel.ai is part of the Power Web 5 Suite of tools that hosts other Ai tools for different programs. It’s a no-code/low-code app and database platform that also lets expert users build their own apps without getting bogged down with codes. 

Lumelixr.ai

If you’re tired of googling Excel formulas, you can conveniently use Lumelixr to generate them. Simply ask a question using natural language and watch the tool generate the exact Excel formula you need to achieve the answer. 

You can save time spent on figuring out long formulas and debugging errors. With this tool, you’d be able to focus on the data analysis instead of data manipulation. The way the tool works is like having an expert Excel user by your side, ready to answer all your Excel queries. 

It comes with a simple monthly or yearly price and a 7-day unlimited free trial. It works with both Excel and Google Sheet as an add-in and Chrome extension. 

The tool is made by indie developers and as a simple Excel AI tool, it does one job well – give the formulas you need. You can also pay for a higher plan which gives you Notion and Coda formulas. You can also save favorite queries and look back on the history of past questions. 

This tool is ideal for novice users who only need a simple and quick answer to their Excel questions. As long as you have basic foundational knowledge, then this tool will work for you. 

The Many Uses of Excel

Excel is a powerful tool that is widely used for various purposes. It is widely used for data analysis because it provides a wide range of functions and tools that can be used to manipulate, organize, and analyze data. It is a popular tool for financial modeling because it allows users to create detailed financial models and perform complex financial calculations.

The tool is often used for budgeting and forecasting because it allows users to create detailed financial projections and track progress against budget.

Excel lets you create charts, graphs, and other data visualizations that can help to communicate complex data in an easy-to-understand way. It can even help predict and visualize trends. It can be used to create and manage small databases by creating tables, forms, and queries.

It’s also used to create Gantt charts, timelines, and other project management tools to help plan and track progress on projects. It is used to share data and collaborate on projects with others by using features such as shared worksheets, data linking, and cloud-based storage.

Excel has many built-in features, such as macros and VBA (Visual Basic for Applications), which can be used to automate repetitive tasks and streamline workflows.

For all the wonderful features and functions of Excel, many people fail to utilize its full potential because they find writing formulas intimidating and confusing. But with the advent of AI tools for Excel, everyone can harness the true potential of the software by letting AI quickly generate the right formulas. 

With AI, novice users are able to tap the expertise of advanced users and gain the required knowledge to use Excel for their requirements successfully. 

Benefits of AI Tools in Excel

Using AI tools have loads of benefits for any Excel user, both novice and expert. AI tools can automate repetitive tasks and processes, allowing you to focus on more important tasks. It reduces errors and improves the accuracy of data analysis. 

It enhances data insights by uncovering hidden patterns and insights in your data that would be difficult to find manually. AI tools also give you greater flexibility by letting you connect and work with data from various sources, making it easier to integrate data from different systems.

AI tools create interactive data visualizations that make it easier to understand and communicate complex data. Best of all, you don’t need advanced technical skills to apply data analysis techniques efficiently and quickly.

By using AI tools in Excel, you can make better decisions using more accurate and actionable insights.

Final Thoughts

AI is a powerful innovation that makes lives easier. Even cumbersome Excel spreadsheets become easier to use and manipulate with the help of AI tools. 

AI tools for Excel can help users to increase productivity, improve accuracy, uncover hidden insights, and make better decisions. Using AI tools in Excel can save users time and effort by automating repetitive tasks and streamlining data-intensive workflows.

With the advancements in AI technology, it is expected that Excel will continue to integrate more AI features in the future, making it an even more powerful tool for data analysis and management. However, it is important to note that some of the AI features may require an Office 365 subscription or a separate purchase.

Make the most of Excel by taking advantage of the built-in and third-party AI tools described above. You can be a power Excel user in no time.

MS-EXCEL is a part of Microsoft Office suite software. It is an electronic spreadsheet with numerous rows and columns, used for organizing data, graphically represent data(s), and performing different calculations.  It consists of 1048576 rows and 16384 columns, a row and column together make a cell. Each cell has an address defined by column name and row number example A1, D2, etc. this is also known as a cell reference.

Cell references: The address or name of a cell or a range of cells is known as Cell reference. It helps the software to identify the cell from where the data/value is to be used in the formula. We can reference the cell of other worksheets and also of other programs.

• Referencing the cell of other worksheets is known as External referencing.
• Referencing the cell of other programs is known as Remote referencing.

There are three types of cell references in Excel:  

1. Relative reference.
2. Absolute reference.
3. Mixed reference.

The Ribbon in MS-Excel is the topmost row of tabs that provide the user with different facilities/functionalities. These tabs are:

1. Home Tab: It provides the basic facilities like changing the font, size of text, editing the cells in the spreadsheet, autosum, etc.
2. Insert Tab: It provides the facilities like inserting tables, pivot tables, images, clip art, charts, links, etc.
3. Page layout: It provides all the facilities related to the spreadsheet-like margins, orientation, height, width, background etc. The worksheet appearance will be the same in the hard copy as well.
4. Formulas: It is a package of different in-built formulas/functions which can be used by user just by selecting the cell or range of cells for values.
5. Data: The Data Tab helps to perform different operations on a vast set of data like analysis through what-if analysis tools and many other data analysis tools, removing duplicate data, transpose the row and column, etc. It also helps to access data(s) from different sources as well, such as from Ms-Access, from web, etc.
6. Review: This tab provides the facility of thesaurus, checking spellings, translating the text, and helps to protect and share the worksheet and workbook.
7. View: It contains the commands to manage the view of the workbook, show/hide ruler, gridlines, etc, freezing panes, and adding macros.

Creating a new spreadsheet: 

In Excel 3 sheets are already opened by default, now to add a new sheet :

• In the lowermost pane in Excel, you can find a button.
• Click on that button to add a new sheet.

• We can also achieve the same by Right-clicking on the sheet number before which you want to insert the sheet.
• Click on Insert.

• Select Worksheet.
• Click OK.

Opening previous spreadsheet: 

On the lowermost pane in Excel, you can find the name of the current sheet you have opened.

On the left side of this sheet, the name of previous sheets are also available like Sheet 2, Sheet 3 will be available at the left of sheet4, click on the number/name of the sheet you want to open and the sheet will open in the same workbook.

For example, we are on Sheet 4, and we want to open Sheet 2 then simply just click on Sheet2 to open it.

Managing the spreadsheets: 

You can easily manage the spreadsheets in Excel simply by :

• Simply navigating between the sheets.

• Right-clicking on the sheet name or number on the pane.
• Choose among the various options available like, move, copy, rename, add, delete etc.
• You can move/copy your sheet to other workbooks as well just by selecting the workbook in the To workbook and the sheet before you want to insert the sheet in Before sheet.

To save the workbook:

1. Click on the Office Button or the File tab.
2. Click on Save As option.
3. Write the desired name of your file.
4. Click OK.

To share your workbook:

1. Click on the Review tab on the Ribbon.
2. Click on the share workbook (under Changes group).
3. If you want to protect your workbook and then make it available for another user then click on Protect and Share Workbook option.
4. Now check the option “Allow changes by more than one user at the same time. This also allows workbook merging” in the Share Workbook dialog box.
5. Many other options are also available in the Advanced like track, update changes.
6. Click OK.

Ms-Excel shortcuts:

1. Ctrl+N: To open a new workbook.
2. Ctrl+O: To open a saved workbook.
3. Ctrl+S: To save a workbook.
4. Ctrl+C: To copy the selected cells.
5. Ctrl+V: To paste the copied cells.
6. Ctrl+X: To cut the selected cells.
7. Ctrl+W: To close the workbook.
8. Delete: To remove all the contents from the cell.
9. Ctrl+P: To print the workbook.
10. Ctrl+Z: To undo.