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This article was published as a part of the Data Science Blogathon.


In this article, I will show how we can run a regression analysis and optimize rent price in R, then paste the value to Excel, which will subsequently be connected to Tableau again for further calculations with other fields in the original dataset.

Such kind of seamless integration among 3 different analysis tools can help data analysts to run statistical research in R, then migrate results in Tableau and visualize in digestible ways for business readers.

Introduction About Dataset Used and Business Requirements

The dataset in this example is extracted from the Capstone project within “Excel to MySQL: Analytic Techniques for Business”. This is a table containing information about properties for rent of a company, with information about short-term rent occupancy rate and the average nightly rent price. We also have data about the price at the 10th percentile and 90th percentile of similar properties in the same region.

Our business requirement is to find an optimized price for each property so that revenue can be maximized. Since revenue is a function of occupancy rate*nightly rent price*365 (assuming that the property can be rented throughout the year), we need to express occupancy rate as a function of nightly rent price, which can be done by simple linear regression

The next task is to run the R optim function, just like we use Solver in Excel, for each Property, or each row in the dataset.

With optimized price and predicted occupancy rate, we can then calculate the total gross profit for the company and do many other analyses.

Connect R with Tableau

We must first connect Tableau with R.

Before we connect R with Tableau, make sure your R console has installed Rserve already.



Now, R should print ‘Starting Rserve…’. If you observe this output, then R is reaching out to Tableau for a connection.

Now, you should get a dialog box stating, ‘Successfully connected to the R serve service’. It means that you are ready to use R with Tableau

Create a calculated field running R code in Tableau

Create a Calculated Field and paste the below code:

SCRIPT_REAL( "df <- data.frame(.arg1,.arg2,.arg3,.arg4,.arg5) model <-lm(data=df,.arg1 ~ .arg2) Create revenue function.  revenue <- function(data,par) { par_vs_10th <- par-data$.arg3 normalized_price <-0.1+0.8*par_vs_10th/data$.arg5 fcst_occupancy <-coef(model)['(Intercept)']+coef(model)['.arg2']*normalized_price fcst_st_revenue <-fcst_occupancy*365*par fcst_st_revenue } Run optim for each row in df. Find the value of "par"-rent price-that can optimize revenue function for (i in 1:nrow(df)) {df[i,'optimized_price'] <-optim(122,revenue,data=df[i,],method='L-BFGS-B', control=list(fnscale=-1),lower=df[i,'.arg3']) } #return optimized price as output for calculated field df$optimized_price", sum([OccupancyRate]), avg([sample_price_percentile]), avg([Percentile10Th Price]), avg([Percentile 90Th Price]), avg([percentile_90th_vs_10th]), attr([Ws Property Id]))

R-code must be written under function like SCRIPT_REAL, which returns numeric values. There are other similar R-functions in Tableau such as SCRIPT_BOOL and SCRIPT_INT, depending on the values you want to retrieve.

Before running, we must create a table: df<-data.frame(.arg1,.arg2,…)

.arg1, .arg2… are the fields from data sources in Tableau. They are the bolded words in the code. .arg1 is the Occupancy Rate, .arg2 is the sample_price_percentile.

The R_code will be written within brackets (” “). The final line of code: df$optimized_price will determine the return value for this calculation.

For detailed explanation about running optim and linear regression in R, please refer to the link below:

This calculation is a table calculation. Make sure that it is computed along the Property ID.

Let’s create a view to see this measure.

Now we have optimized price for each property.

However, a problem now occurs. This measure is a table calculation and we can only have one single value per property when observing it in a table. We cannot embed it inside another calculation.

For example, I want to normalize the optimized price into percentile value using below formula:

0.1+0.8*(Optimized Price-Percentile 10th Price)/(percentile 90th_vs_10th)

Tableau will trigger an error, saying that we cannot mix an aggregate measure with non-aggregate value. This is really inconvenient and inflexible, since we may want to leverage an R-coded calculation for many more measures.

In order to mitigate this issue, I have come up with a workaround solution: write the R-optimized values into a csv or excel file, then join this new dataset with the original data source in Tableau for other visualizations or measures creation.

Final Integration

Let’s create another calculation field in Tableau, called as Script. This time we will not return a numeric value, but will write outputs to an external CSV file. In my example, I write to CSV for simplicity, but you can also write to an xlsx file if prefer.

SCRIPT_REAL( "df <- data.frame(.arg1,.arg2,.arg3,.arg4,.arg5) model <-lm(data=df,.arg1 ~ .arg2) revenue <- function(data,par){ par_vs_10th <- par-data$.arg3 normalized_price <-0.1+0.8*par_vs_10th/data$.arg5 fcst_occupancy <-coef(model)['(Intercept)']+coef(model)['.arg2']*normalized_price fcst_st_revenue <-fcst_occupancy*365*par fcst_st_revenue } for (i in 1:nrow(df)) {df[i,'optimized_price'] <-optim(122,revenue,data=df[i,],method='L-BFGS-B', control=list(fnscale=-1),lower=df[i,'.arg3']) } df$normalized_optimized_price<-0.1+0.8*(df$optimized_price-df$.arg3)/(df$.arg5) #Create a new dataframe, replacing .arg2(sample_percentile_price) with the normalized optimized price new <-data.frame(.arg2=df$normalized_optimized_price) #Predict the occupancy rate based on optimized price and add as a new column to df df['Forecast Occupancy']=predict.lm(model, newdata=new) #Add Property ID to df df['Ws Property Id']=.arg6 #Write df to a csv file write.table(df,'D:/Documents/Business Analytics/4. Visualization/Business Capstone/Blogathon/new.csv',sep=',',row.names=FALSE,quote=FALSE,col.names = TRUE) ", sum([Occupancy Rate]), avg([sample_price_percentile]), avg([Percentile 10Th Price]), avg([Percentile 90Th Price]), avg([percentile_90th_vs_10th]), attr([Ws Property Id]))

The next step is to create a new Sheet, called Sheet 2 for instance. Then drag Property ID and Script measure to Detail in Mark card.

You should see a message like the following:

Just ignore that error message. Open the folder you specified in the Script calculation and you will see a new CSV file has just been created.

Our next task is simpler, just connect Tableau workbook with this csv file and blend it with the original data source, based on the foreign key: WS Property ID.

Now in the Data Pane, there is a new dataset readily available for use.

Since we have Optimized Price and Forecast Occupancy Rate as normal fields, we can use them for further calculations without any issues related to aggregate level as previously.

Suppose that I want to create a measure called Gross Revenue= Optimized Price * Occupancy Rate *365. The calculation is now valid.

In the future, in case there are changes to the training data (sample nightly price), or you add more features to the linear model. Simply open Sheet 2 again to reactivate the process and retrieve new outputs.

End Notes

The ability to write code in R in a calculation makes Tableau become more flexible than its rival-Power BI in terms of connecting with external data analysis platforms. By combining Tableau, Excel and R, we can utilize the power of many tools simultaneously for our analytical practices.


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How To Create Tableau 3D With Models?

Introduction to Tableau 3D What is Tableau 3D?

Tableau provides unique and exciting features, making it the most popular tool in business intelligence. It aids the user in creating various graphs, charts, dashboards, maps, and stories to visualize and analyze the data to make an effective decision. The salient features which are unique in the tableau are discussed below, and this makes it a powerful tool in business intelligence. In addition, the consequential data exploration and powerful data discovery in tableau enabled the user to respond to essential queries in seconds.

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It doesn’t require any programming knowledge; the users can access it without relevant experience and begin working on the tableau and creating visualization according to the requirement. Tableau associates several data sources where the other business intelligence tools don’t support them. It enables users to create meaningful insights by blending and concatenating variable datasets. The tableau server helps the centralized location handle all the published data sources in the organization.

Tableau 3D Models

To fetch the data, the user doesn’t need to be aware of python or chúng tôi file. It can be brought directly from an excel sheet. chúng tôi format comprises data with X, Y, and Z as vertex coordinates, and every polygon has three chúng tôi is availed as text format where it can be accessed like notepad. chúng tôi file looks like the excel sheet.

The user should give the representation of rows with vertex, and prefixes should be mentioned. In addition, it helps to provide a number for rows.

To implement this, follow the given steps:

First, fix the space as a delimiter. Then, the STL file should be opened in Excel.

After finishing this, filter the data and select the vertex from the column by eliminating the other rows. Instead of choosing the filter, the user can sort the column and delete every row which is not vertex.

The new column should be added before the column and the vertice_id and the finite numerate rows. It should be unique to identify the vertex quickly.

Add other columns, the polygon_id should be calculated, and the result helps to identify the polygon’s properties.

Then rename all the columns or remove the column apart from the value set.

Save the file in .csv format or .xlsx and link to data in tableau.

Now mention the fields of X, Y, and Z to create a preview and ensure the model is correct.

In the image, the polygons and their values are sorted in ascending order on Y-axis and can be explained in the painter’s algorithm. Here, the depth of the painter’s algorithm is arranged in descending to ascending values. For the rotation of images, the user has to provide the value for three vertices, angle XY, angle XZ, and angle YZ. The calculations for z-axis rotation, y-axis rotation, and x-axis rotation should be given to calculate the plane projection. Value of X, Y, and Z should be used for model rotation. It is up to the user to choose to add colors.

Hence the 3D model is completed.

How to Create Tableau 3D?

All the standards are dependent on. For example, STL models don’t hold any data about the properties and color of the polygon. There is a varied format in 3D like KMZ, FBX, DAE, and OBJ, and all it lies when the triangle has greater than three vertices which hold the data about groups and texture of polygons. But it is easy and efficient to work in OBJ format; hence the format is open. It can be accessed in text format and opened in notepad. To convert and understand the. CSV format works better than other formats. Hence it is widely available with different kinds of models.

There are five significant steps involved in 3D tableau creation. They are data preparation, bar calculation, and shape calculation, and the final step is 3D chart creation.

The custom shape should be downloaded according to the user’s preference.

The data source should be imported from the website.

The path should be created.

Then 0, else 1000 a chosen end.

The bin should be created from the path. The minimum value should be given to the bin.

The row should be set along with the path.

The country column should be moved, and it should be filled with missing values.

Finally, shapes should be selected per the data marks and applied to the worksheet.


The.STL format with a 3D model works in tableau, and the rendering performance is chúng tôi model and the performance monitoring are chúng tôi format. The discrepancies chúng tôi format has occurred in a cyclic overlapping format. chúng tôi format is in sequential order, with several meshes, faces, and vertices. Each part is enumerated, and it works effectively on STL files. The excel files can be scripted as the same as python files. For every 3D image, the goal should be achieved with proper vertices, faces, groups, meshes, and colors.

Recommended Articles

This is a guide to Tableau 3D. Here we discuss the introduction, models, and how to create tableau 3D. You may also have a look at the following articles to learn more –

Why Integrate Your Web Store With Your Erp System?

As brick-and-mortar retailers become less prominent and online businesses flourish, the importance of enterprise resource planning (ERP) solutions grows.

There are many reasons why you should integrate your ecommerce platform with an ERP system, ranging from boosting your revenue to improving the quality of your customer relationships and increasing your productivity.

ERP developers are highly involved in this process and have a big role in developing components, which means you should hire professionals from web development company new york to deliver the best quality performance.

We’ll discuss these and the other benefits of using an ERP with your web store in detail below.

Why Integrate Your Web Store with Your ERP System?

Most Notable Benefits of ERP Integration

Successful online stores usually operate at a high volume. Using a dedicated system equipped with financial management capabilities and automation functions can make a world of difference.

Here are some of the most significant benefits that online store owners should consider when integrating their platforms with an ERP solution.

1. Keeping Sales Information Centralized

Keeping your sales information organized can be a challenge, particularly if you run several platforms. An ERP will help you consolidate the data and will ensure that everything runs smoothly. This will create more relevant data and, in turn, will increase the accuracy of any automated system you might have in place.

Also read: Top 10 Job Search Websites of 2023

2. Increasing Operational Productivity

ERP software can automate the exchange of information by connecting several workflows. This enables you to increase your overall productivity. Some ERP solutions can even automate functions like shipping notifications and bank reconciliation.

Boosting your productivity will enable you to conduct more operations within the same timeframe, thus increasing your output. You’ll waste less time spot-checking for human errors, and your workflow will be optimized through the automation capabilities of a high-quality ERP.

3. Generating More Revenue

Tying in with our previous point, ERPs can generate more revenue for your company through automation and increased productivity. There isn’t a business in the world that doesn’t have “more revenue” high on its list of priorities, so this benefit must be worth considering.

Automating specific tasks through your ERP allows your employees’ time to be spent more creatively. According to a Nucleus study, every dollar a business spends on an ERP system results in $7.23 in revenue.

With numbers such as these, it is clear that the benefits of successfully integrating your ERP into your online store far outweigh the costs.

4. Improving Customer Relationships

Customer relationship management (CRM) is included in many ERP systems, either as an add-on feature or out-of-the-box. This functionality allows ecommerce companies to have a centralized database of customer information.

Also read: The Top 10 In-Demand Tech Skills you need to have in 2023

5. Aiding Project Management

These systems often provide customizable dashboards containing labor allocations, project status notifications, and finance-related information.

Having your insights in an easy-to-digest graphical format will allow you to identify bottlenecks more quickly and will help facilitate your operations.

6. Key Features of ERPs

Different ERP systems offer different features. For ecommerce businesses, some functions are more important than others. Here are a few key ERP features for ecommerce businesses:

7. Customer Relationship Management

The importance of customer relationship management (CRM) is hardly unique to ecommerce companies. In fact, it is essential for any business. That said, good CRM software can put you ahead of the competition in the ecommerce world.

Try to put yourself in your customers’ shoes. Would you rather do business with a company that puts your needs first or one that doesn’t seem to care about you?

Also read: Top 9 WordPress Lead Generation Plugins in 2023

8. Financial Management and Accounting

It isn’t easy to run a business without efficient, real-time financial data. This is why the accounting and financial management tools are vital components of any ERP solution worth its salt. Financial data is key to being able to plan for the future.

This ERP feature allows you to create efficient forecasting models, anticipate delivery lead times, plan for reorders, and centralize your processes overall. Ultimately, financial management will enable you to keep your profit margins tight, and the more you can do that, the more likely you are to keep making a profit.

In case you already use accounting software for your online store, you should think about integrating it with an ERP system. This way, you can benefit from more specialized information and share insights across departments more quickly.

9. Inventory Management

Inventory management is one of the critical ERP features for an ecommerce platform. As we mentioned earlier, successful ecommerce businesses tend to operate at a high volume, making it easier to make mistakes related to inventory management.

Also read: Best Video Editing Tips for Beginners in 2023

10. Distribution and Shipping

In any business that involves delivering items, you want to be able to look at the entire fulfillment workflow. ERP solutions often partner with shipping companies like FedEx and UPS, allowing you to track shipments and include them in the system you use to notify your customers.

How To Install And Configure � R� On Ubuntu 16 04

Output: Executing: chúng tôi — –recv-key– E298A3A825C0D65DFD57CBB651716619E084DAB9 gpg: Total number processed: 1 gpg: imported: 1 (RSA: 1)

Once the trusted key is to install on the server database, we can add the repository to the machine with the below command.

Once the repository is added, we will now update the machine with the below command

$ sudo apt-get update Output: Reading package lists... Done Output: Reading package lists... Done Building dependency tree Reading state information... Done The following packages were automatically installed and are no longer required: linux-headers-4.4.0-21 linux-headers-4.4.0-21-generic linux-image-4.4.0-21-generic linux-image-extra-4.4.0-21-generic Use 'sudo apt autoremove' to remove them. The following additional packages will be installed: bzip2-doc cdbs cpp-5 dh-translations fontconfig fontconfig-config fonts-dejavu-core g++-5 gcc-5 gcc-5-base gfortran gfortran-5 icu-devtools intltool libasan2 libatomic1 libauthen-sasl-perl libblas-common libblas-dev libblas3 libbz2-dev libcairo2 libcc1-0 libcilkrts5 libcurl3 libdatrie1 libdrm-amdgpu1 libdrm-intel1 libdrm-nouveau2 libdrm-radeon1 libelf1 libencode-locale-perl libfont-afm-perl libfontconfig1 libfontenc1 libgcc-5-dev libgfortran-5-dev libgfortran3 libgl1-mesa-dri libgl1-mesa-glx libglapi-mesa libgomp1 libgraphite2-3 libharfbuzz0b libhtml-form-perl libio-html-perl libio-socket-ssl-perl libipc-system-simple-perl libitm1 libjbig0 libjpeg-dev libjpeg-turbo8 libjpeg-turbo8-dev libjpeg8 libjpeg8-dev liblapack-dev liblapack3 libllvm3.8 liblsan0 … … … Creating config file /etc/R/Renviron with new version Setting up r-cran-boot (1.3-18-1cran1xenial0) ... Setting up r-cran-cluster (2.0.5-1xenial0) ... Setting up r-cran-foreign (0.8.67-1xenial0) ... Setting up r-cran-mass (7.3-45-1xenial0) ... Setting up r-cran-kernsmooth (2.23-15-2xenial0) ... Setting up r-cran-lattice (0.20-34-1xenial0) ... Setting up r-cran-nlme (3.1.128-2xenial0) ... Setting up r-cran-matrix (1.2-7.1-1xenial0) ... Setting up r-cran-mgcv (1.8-15-1cran1xenial0) ... Setting up r-cran-survival (2.39-4-2xenial0) ... Setting up r-cran-rpart (4.1-10-1) ... Setting up r-cran-class (7.3-14-1xenial0) ... Setting up r-cran-nnet (7.3-12-1xenial0) ... Setting up r-cran-spatial (7.3-11-1xenial0) ... Setting up r-cran-codetools (0.2-15-1cran1xenial0) ... Setting up r-recommended (3.3.2-1xenial0) ... Setting up r-base (3.3.2-1xenial0) ... Setting up liblzma-dev:amd64 (5.1.1alpha+20120614-2ubuntu2) ... Setting up r-doc-html (3.3.2-1xenial0) ... Setting up x11-utils (7.7+3) ... Setting up x11-xserver-utils (7.7+7) ... Setting up libauthen-sasl-perl (2.1600-1) ... Setting up r-base-html (3.3.2-1xenial0) ... Setting up libwww-perl (6.15-1) ... Setting up libxml-parser-perl (2.44-1build1) ... Setting up intltool (0.51.0-2) ... Setting up dh-translations (129) ... Setting up cdbs (0.4.130ubuntu2) ... Setting up libxml-twig-perl (1:3.48-1) ... Setting up libnet-dbus-perl (1.1.0-3build1) ... Setting up r-base-dev (3.3.2-1xenial0) ... Processing triggers for libc-bin (2.23-0ubuntu3) ... Processing triggers for systemd (229-4ubuntu10) ... Processing triggers for ureadahead (0.100.0-19) ...

Once the package is installed, we can verify the packages using the below command

$ sudo -i R Output: R version 3.3.2 (2023-10-31) -- "Sincere Pumpkin Patch" Copyright (C) 2023 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > Install the additional R Packages from CRAN Repository

As R has a lot of packages or add-on, where here in the demo we will install the txtplot which is an ASCII graph package which also includes the scatterplot, below is the command to be run from the R console.

> install.packages('txtplot') Output: Installing package into ‘/usr/local/lib/R/site-library’ (as ‘lib’ is unspecified) --- Please select a CRAN mirror for use in this session --- HTTPS CRAN mirror 53: (HTTP mirrors) Selection:1 Content type 'application/x-gzip' length 6152 bytes ================================================== downloaded 6152 bytes * installing *source* package ‘txtplot’ ... ** package ‘txtplot’ successfully unpacked and MD5 sums checked ** R ** preparing package for lazy loading ** help *** installing help indices ** building package indices ** testing if installed package can be loaded * DONE (txtplot) The downloaded source packages are in Output: 120                                                  + * + |                                                        | d 100                                   + * + s 80                                + * *                + a 60 + * * * * * + c 40 + * * * * * * * + 20 + * * * * * + | * * *                                                  | 5 10 15 20 25 speed >

In the above article we have installed R uses the CRAN repository which is an open-source from RStudio Server as we have completed the server side installation and test some sample date with graphs.

Is The Tableau Era Coming To An End?

The announcement last week that Tableau’s CEO Adam Selipsky is stepping down felt more significant than the casual media coverage it received. To me, it was a signal that the murmurings of discontent I’ve been hearing were true: Era of Tableau is over.

The Glory Days

While Tableau first came about in 2003, they really hit their stride in the early 2010s — and what astride it was. Users heralded the tool as ‘revolutionary’ and ‘life-changing.’ Their annual conferences sold out in minutes. Participants would come together with hundreds of others, proudly brandishing swag that read ‘We Are Data People’ as they attended roller-blading socials and “Iron Viz” competitions. As I said, it was having a real moment.

For many of us (I, too drank the kool-aid), it was affirming and exciting to see data being celebrated, not relegated to the sidelines. Tableau told us being in data was not just cool, but also irrefutably important.

What’s Changed?

But instead of this being an even more glorious Glory Days, it’s an all-too-often underwhelming experience all around:

“Machine learning specialists topped its list of developers who said they were looking for a new job, at 14.3 per cent. Data scientists were a close second, at 13.2 per cent.” [1]

And even more damning:

“Among the 90% of companies that have made some investment in AI, fewer than 2 out of 5 report business gains from AI in the past three years.” [2]

Eesh. Clearly, there’s work to be done.

The Haunting

So what are these ghosts that are getting in our way?

Data === Dashboard

To many business users data is now synonymous with dashboards. While a seemingly benign misunderstanding, this actually causes a whole slew of downstream effects, namely:

Thinking Tableau will ‘fix’ your data problems. Many companies make the mistake of assuming the only thing your data team needs is Tableau (or Power BI). This kind of thinking ignores the more common pain points of bringing data sources together, cleaning and transforming the data, and doing the actual analysis itself, which, if you ask any analyst, are the most traumatic parts of any analysis. By not investing in these problems, you’re telling your data team that their work is less important than the business’s interpretation of it.

Asking dashboards to do too much. Since Tableau is the only tool many teams have to present data they are forced to turn everything into a dashboard which significantly reduces the impact a more nuanced, thoughtful analysis could have. By stripping away context, explanation, and narrative from the analyst, dashboards become a Rorschach test where everyone can see what they want to see.

While users are now more comfortable looking at basic charts, we’ve made little progress in educating our business partners in fundamental data concepts. Dashboards don’t give us the stage needed to explain, for example, why correlation does not equal causation. This means it’s become nearly impossible to explain the significance of our more complicated predictive models or statistical analysis which are required to realize the dreams of our current era.

Hyper Specialization of Tools

One of the great things about Tableau at the start was that it just sat on top of your database, making it easy to ‘plug in’ to your existing stack of data tools without much effort. This model has been used by pretty much every data tool since, creating separate tools for data pipelines, data cleaning, data transformation, data analysis, and of course, data visualization. This approach is completely fragmenting analyst’s workflows, causing significant pain and delays in each analysis. As a result, most analysts and data scientists have adopted a ‘not my data tool’ mentality — acknowledging Tableau as a necessary evil to get their work noticed. Check out this Reddit thread to see for yourself.

“If there were a button that would nuke all the Tableau servers in the world, I am pressing that button.” -Anynomous Data Professional

Remember those ‘murmurings of discontent’ I mentioned at the start…


We have an increasingly urgent need to find solutions to these issues before we find ourselves again fighting for relevancy and attention to data. To do that, we need to start focusing on the following two areas:

Present more than numbers

It’s time to give data more of a voice. Dashboards are great for things where there is a shared context and a straightforward decision. But for many things, those conditions are not met, and therefore we need a new approach.

I, and others, have been banging the drum on data notebooks as a solution for some time now. They can tell the story, explain the methodology, and build nice visuals without sacrificing interactivity or presentability.

By using more notebooks we can start to wean off a culture that’s been jonesing for dashboards. We can start to work with our business partners instead of lobbing questions and charts back and forth over an imaginary wall.

Pick tools the data team wants

Data analysts and scientists see a red flag when a potential employer has Tableau and little else in the way of data engineering, or data analysis tools (e.g. running Tableau on your un-transformed MySQL 5 database). This signals that they aren’t prioritizing the work that these analysts will do. This needs to stop. ASAP.

Depending on the analysis your team is doing, the ‘right’ tools will differ. But there are so many options out there, you just need to make sure you’re investing in the work it takes to make the great analysis as much as you are on a tool make the business look at it.

And hey, you’ll probably end up keeping some of those data scientists that are, according to the stats, most likely shopping around.


We all owe a great deal to Tableau for the current attention data receives in our businesses. To make good on this opportunity though, and move into a new Golden Age of data, we need to address and remedy some of the ghosts of the Tableau era that are holding us back.

Data notebooks present an option that can give your team the flexibility it needs to start to move past the Tableau and into the next era.

At Count, we’re excited to be part of this new movement of data tools designed for modern challenges. You can learn more about the Count notebook here.


[1] Walter, Richard, “

[1] Walter, Richard, “ How machine learning creates new professions — and problems ,” Financial Times, November 2023. [2] S. Ransbotham, S. Khodabandeh, R. Fehling, B. LaFountain, D. Kiron, “ Winning With AI, ” MIT Sloan Management Review and Boston Consulting Group, October 2023.

[3] Header image by Luke Chesser on Unsplash

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.


How To Lock And Protect Formula In Excel?

Excel Lock Formula (Table of Contents)

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Lock Formula in Excel How to Lock and Protect Formulas?

It is a very simple and easy task to lock and protect formulas. Let’s understand how to lock and protect the formulas with an example.

You can download this Lock Formulas Excel Template here – Lock Formulas Excel Template

Excel Lock Formula – Example #1

Consider the below example, which shows the data of the sales team members.

In the below image, the total has been calculated in column D by inserting the formula =B2+C2 in cell D2.

The Result will be as shown below:

The formula in the total column has been copied from cells D2:D5.

Please select all the cells by pressing 


 and unlock them.

Select the cells or the entire columns or rows where you must apply the formula.

Lock the cells which contain the formula.

Protect the worksheet.

Let us in detail show how are the above steps executed.

Step 1: Unlocking all the cells

The cells in Excel are protected and locked in Excel. We must unlock particular cells in the workbook as we need to lock them. So let us see how to unlock all the cells. The steps to unlock all the cells are as follows:



 to select the entire worksheet.

Step 2: Select and lock the cells containing the formula

Now, we need to lock the cells where we entered the formula. The steps to lock the cells containing the formula in Excel are as follows:

Select all the cells in the worksheet by pressing 

Ctrl +A


Go to the 


 tab and select 

Find & Select

 option from the Editing menu.

After selecting the Find & Select option, other options will appear under it, from which select the 

Go To Special


Go To Special

 dialog box will appear as shown below.

Step 3: Protection of the Worksheet

This function ensures that the locked property is enabled for cells with formulas and all the cells in the workbook. Let us see the steps followed to implement the protection for the worksheet:

First, go to the 


 tab and select 

Protect Sheet


After this, the 

Protect Sheet

 dialog box will appear.

This ensures that the “

Protect Worksheet and contents of locked cells

” is selected.

The user can also type a password in the text box under the Password to unprotect the sheet to make the worksheet safer.

Advantages of Lock Formulas in Excel

It helps the user keep their data secure when sending their files to other recipients.

It helps users hide their work when sharing the file with other readers and users.

The user can use a password in the case to protect the entire workbook, which can be written in the text box named ‘Password to unprotect the sheet.’

A new user will need help understanding the function in Excel.

It becomes easier if the user remembers to enter the password to unprotect the file.

It could be more efficient in terms of time as it consumes the time of a user to protect and unprotect the cells of the worksheet.

Things to Remember

All the cells are protected by default; remember to unlock the cells to lock formulas in Excel.

After locking formulas in Excel, make sure to lock the worksheet again.

The entire workbook can be protected by using the option restricted or unrestricted access from the “

Protect Workbook

” option.

In case the user needs to hide their work or formulas from others, they can tick the option “Hidden” by selecting the “Protection” tab from the “Format Cells” dialog box.

If the user must unprotect the complete file, type the password by selecting the “Unprotect Sheet” option.

Users can save time by moving the formulas to separate worksheets and hiding them instead of protecting and unprotecting the worksheet.

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