Acknowledgements

Click here for original dataset: https://community.tableau.com/docs/DOC-1236

Here are the files to download the example Visualization Using the 2014 American Community Survey Data. Users will need to download Tableau Reader (http://www.tableau.com/products/reader) to view the Dashboard, but it is Free to Download and allows users the interactivity that dashboards provide.

This file is used by the SampleQC tableau workbook to provide insights on which samples passed QC. It is a subset of the file that is generated by the RNASeq pipeline where all the genes are dropped out.

This dataset had adapted from 'Credit Card Churn Prediction: https://www.kaggle.com/datasets/anwarsan/credit-card-bank-churn ' for visualization in our university project. We have modified customer information, spending behavior, and also added revenue targets.

Scenario 🕶️ In 2019, the marketing team launched a campaign to attract millennial customers (born 1980-1996) with the goal of increasing revenue and enhancing the brand's appeal to a younger audience.
As the BI team, your task is to create a dashboard for users. 1. The Vice President of Sales wants to view the performance of the credit business. 2. The marketing team is interested in understanding customer segments and customer spending to measure Customer Lifetime Value (CLV) and Marketing Cost per Acquired Customer (MCAC).

⚠️Note: This is just a suggestion to guide the creation of the dashboard

Example in Tableau

Executive summary https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10099382%2F508a2d2d89dabdfd368743f86c2a71e1%2Fexecutive%20overview.JPG?generation=1696110593484137&alt=media" alt=""> Customer behavior https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10099382%2F1e4a1f62a25eab3c6707d002243894c7%2Fcustomer_behaviour.JPG?generation=1696110689732332&alt=media" alt="">

This article discusses how to make statistical graphics a more prominent element of the undergraduate statistics curricula. The focus is on several different types of assignments that exemplify how to incorporate graphics into a course in a pedagogically meaningful way. These assignments include having students deconstruct and reconstruct plots, copy masterful graphs, create one-minute visual revelations, convert tables into “pictures,” and develop interactive visualizations, for example, with the virtual earth as a plotting canvas. In addition to describing the goals and details of each assignment, we also discuss the broader topic of graphics and key concepts that we think warrant inclusion in the statistics curricula. We advocate that more attention needs to be paid to this fundamental field of statistics at all levels, from introductory undergraduate through graduate level courses. With the rapid rise of tools to visualize data, for example, Google trends, GapMinder, ManyEyes, and Tableau, and the increased use of graphics in the media, understanding the principles of good statistical graphics, and having the ability to create informative visualizations is an ever more important aspect of statistics education. Supplementary materials containing code and data for the assignments are available online.

This data set contains a list of Olympic medals won by USA athletes between 2000 and 2012. The data elements are Athlete name, Year, Age, Country, Gold, Silver, Bronze, Total medals. Source of this data set from http://www.tableau.com/public/community/sample-data-sets.

Context

With growing demands and cut-throat competitions in the market, a Superstore Giant is seeking your knowledge in understanding what works best for them. They would like to understand which products, regions, categories and customer segments they should target or avoid.

You can even take this a step further and try and build a Regression model to predict Sales or Profit.

Go crazy with the dataset, but also make sure to provide some business insights to improve.

Metadata

Row ID => Unique ID for each row. Order ID => Unique Order ID for each Customer. Order Date => Order Date of the product. Ship Date => Shipping Date of the Product. Ship Mode=> Shipping Mode specified by the Customer. Customer ID => Unique ID to identify each Customer. Customer Name => Name of the Customer. Segment => The segment where the Customer belongs. Country => Country of residence of the Customer. City => City of residence of of the Customer. State => State of residence of the Customer. Postal Code => Postal Code of every Customer. Region => Region where the Customer belong. Product ID => Unique ID of the Product. Category => Category of the product ordered. Sub-Category => Sub-Category of the product ordered. Product Name => Name of the Product Sales => Sales of the Product. Quantity => Quantity of the Product. Discount => Discount provided. Profit => Profit/Loss incurred.

Acknowledgements

I do not own this data. I merely found it from the Tableau website. All credits to the original authors/creators. For educational purposes only.

Snapshot img

The visual analytics market has the potential to grow by USD 4.39 billion during 2021-2025, and the market’s growth momentum will accelerate at a CAGR of 11.32%.

This visual analytics market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers market segmentation by end-user (BFSI, CPG and retail, healthcare, manufacturing, and others) and geography (North America, APAC, Europe, MEA, and South America). The visual analytics market report also offers information on several market vendors, including Altair Engineering Inc., Alteryx Inc., Arcadia Data Inc., Datameer Inc., International Business Machines Corp., Microsoft Corp., QlikTech international AB, SAP SE, SAS Institute Inc., and Tableau Software LLC among others.

What will the Visual Analytics Market Size be in 2021?

Browse TOC and LoE with selected illustrations and example pages of Visual Analytics Market

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Visual Analytics Market: Key Drivers and Trends

The growing availability and complexity of data are notably driving the visual analytics market growth, although factors such as data privacy and security concerns may impede market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the visual analytics industry. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

      The growing availability and complexity of data will fuel the growth of the visual analytics market size.
      The availability of a large volume of data and rapidly growing data complexity in organizations are the major drivers for the development of various intelligence-based data analysis techniques.
      Intelligent techniques involving technologies such as ML and AI can help companies retrieve the huge amount of complex data in a useful manner and use that data to enhance their services and business processes. This, in turn, is expected to drive the growth of the market for visual analytics.




      The increased dependency on Internet for critical operations will drive the visual analytics market growth during the forecast period.
      E-commerce vendors are posting advertisements on search engines and other websites to attract several customers. This will increase the demand for visual analytics to help e-commerce vendors track customers, analyze customer behavior, and ensure proper decision-making.
      With the rising popularity and use of e-commerce, the number of digital media advertisements by e-commerce vendors is expected to increase, which will drive the growth of the market during the forecast period.

This visual analytics market analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2021-2025.

Who are the Major Visual Analytics Market Vendors?

The report analyzes the market’s competitive landscape and offers information on several market vendors, including:

Altair Engineering Inc.
Alteryx Inc.
Arcadia Data Inc.
Datameer Inc.
International Business Machines Corp.
Microsoft Corp.
QlikTech international AB
SAP SE
SAS Institute Inc.
Tableau Software LLC

This statistical study of the visual analytics market encompasses successful business strategies deployed by the key vendors. The visual analytics market is fragmented and the vendors are deploying growth strategies such as providing customized solutions to compete in the market.

To make the most of the opportunities and recover from post COVID-19 impact, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

The visual analytics market forecast report offers in-depth insights into key vendor profiles. The profiles include information on the production, sustainability, and prospects of the leading companies.

Which are the Key Regions for Visual Analytics Market?

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35% of the market’s growth will originate from North America during the forecast period. The US is a key market for visual analytics in North America. Market growth in this region will be faster than the growth of the market in Europe, MEA, and South America.

This market research report entails detailed information on the competitive intelligence, marketing gaps, and regional opportunities in store for vendors, which will assist in creating efficient business plans.

What are the Revenue-generating End-user Segments

Statistical profiles of the transportation infrastructure and mobility patterns found in each county in the United States.

There are the data currently shown on the deprecated, Tableau-based version of the BTS County Transportation Profiles formerly available at https://www.bts.gov/ctp.

Data for replication package Paper Title: Drivers of firm-government engagement for technology ventures Authors: Lauren Lanahan; Iman Hemmatian; Amol M. Joshi; Evan E. Johnson Lead Author of Data Curation, Code, & Analysis: Lauren Lanahan (llanahan@uoregon.edu) Software: Stata 18; Tableau Computational Requirements: We utilized a powerful server (32-core processors, 384 GB memory, 32 TB storage) to construct the sample and run the analyses. We provide the code and data for all empirical assessments. And for additional transparency, we provide the log file for the set of empirical assessments (i.e., descriptive statistics and regression assessments). Replication package includes: [1] do file Do File_READ FIRST.do [3] dta files Descriptive Statistics Data File.dta (Table 4, Table 5; S1 Table; S3 Table) Regression Data File.dta (Table 6; S2 Table; S4 Table; S5 Table; S6 Table; Table 7; Table 8) Data for Tableau.dta (Figure 2) [1] log file Replication Log.smcl Comment: Code is organized in manner that reflects ordering of the empirical results presented in paper. Note, we report the raw data for Table 1 in the table itself.

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This statistic denotes the global market size across several regions including North America, Europe, APAC, South America, and MEA. The workforce analytics market size was estimated to be at USD 983.73 mn in 2020-2024.

The size of the global workforce analytics market has been derived by triangulating data from multiple sources and approaches. While arriving at the market size, we have considered data points, such as the size of the parent market and the revenues of key market participants, such as Automatic Data Processing Inc., Cornerstone OnDemand Inc., Infor Inc., International Business Machines Corp., Kronos Inc., Oracle Corp., Paycor Inc., SAP SE, Tableau Software LLC, and Workday Inc.

Cancer, the second-leading cause of mortality, kills 16% of people worldwide. Unhealthy lifestyles, smoking, alcohol abuse, obesity, and a lack of exercise have been linked to cancer incidence and mortality. However, it is hard. Cancer and lifestyle correlation analysis and cancer incidence and mortality prediction in the next several years are used to guide people’s healthy lives and target medical financial resources. Two key research areas of this paper are Data preprocessing and sample expansion design Using experimental analysis and comparison, this study chooses the best cubic spline interpolation technology on the original data from 32 entry points to 420 entry points and converts annual data into monthly data to solve the problem of insufficient correlation analysis and prediction. Factor analysis is possible because data sources indicate changing factors. TSA-LSTM Two-stage attention design a popular tool with advanced visualization functions, Tableau, simplifies this paper’s study. Tableau’s testing findings indicate it cannot analyze and predict this paper’s time series data. LSTM is utilized by the TSA-LSTM optimization model. By commencing with input feature attention, this model attention technique guarantees that the model encoder converges to a subset of input sequence features during the prediction of output sequence features. As a result, the model’s natural learning trend and prediction quality are enhanced. The second step, time performance attention, maintains We can choose network features and improve forecasts based on real-time performance. Validating the data source with factor correlation analysis and trend prediction using the TSA-LSTM model Most cancers have overlapping risk factors, and excessive drinking, lack of exercise, and obesity can cause breast, colorectal, and colon cancer. A poor lifestyle directly promotes lung, laryngeal, and oral cancers, according to visual tests. Cancer incidence is expected to climb 18–21% between 2020 and 2025, according to 2021. Long-term projection accuracy is 98.96 percent, and smoking and obesity may be the main cancer causes.

The global market size of Manufacturing Analytics is $XX million in 2018 with XX CAGR from 2014 to 2018, and it is expected to reach $XX million by the end of 2024 with a CAGR of XX% from 2019 to 2024.
Global Manufacturing Analytics Market Report 2019 - Market Size, Share, Price, Trend and Forecast is a professional and in-depth study on the current state of the global Manufacturing Analytics industry. The key insights of the report:
1.The report provides key statistics on the market status of the Manufacturing Analytics manufacturers and is a valuable source of guidance and direction for companies and individuals interested in the industry.
2.The report provides a basic overview of the industry including its definition, applications and manufacturing technology.
3.The report presents the company profile, product specifications, capacity, production value, and 2013-2018 market shares for key vendors.
4.The total market is further divided by company, by country, and by application/type for the competitive landscape analysis.
5.The report estimates 2019-2024 market development trends of Manufacturing Analytics industry.
6.Analysis of upstream raw materials, downstream demand, and current market dynamics is also carried out
7.The report makes some important proposals for a new project of Manufacturing Analytics Industry before evaluating its feasibility.
There are 4 key segments covered in this report: competitor segment, product type segment, end use/application segment and geography segment.
For competitor segment, the report includes global key players of Manufacturing Analytics as well as some small players. At least 9 companies are included:
* SAS Institute (U.S.)
* Tableau Software (U.S.)
* Tibco Software (U.S.)
* Oracle Corporation (U.S.)
* IBM Corporation (U.S.)
* Computer Science Corporation (U.S.)
For complete companies list, please ask for sample pages.
The information for each competitor includes:
* Company Profile
* Main Business Information
* SWOT Analysis
* Sales, Revenue, Price and Gross Margin
* Market Share

For product type segment, this report listed main product type of Manufacturing Analytics market
* Solution
* Services
For end use/application segment, this report focuses on the status and outlook for key applications. End users sre also listed.
* Application I
* Application II
* Application III

For geography segment, regional supply, application-wise and type-wise demand, major players, price is presented from 2013 to 2023. This report covers following regions:
* North America
* South America
* Asia & Pacific
* Europe
* MEA (Middle East and Africa)
The key countries in each region are taken into consideration as well, such as United States, China, Japan, India, Korea, ASEAN, Germany, France, UK, Italy, Spain, CIS, and Brazil etc.

Reasons to Purchase this Report:
* Analyzing the outlook of the market with the recent trends and SWOT analysis
* Market dynamics scenario, along with growth opportunities of the market in the years to come
* Market segmentation analysis including qualitative and quantitative research incorporating the impact of economic and non-economic aspects
* Regional and country level analysis integrating the demand and supply forces that are influencing the growth of the market.
* Market value (USD Million) and volume (Units Million) data for each segment and sub-segment
* Competitive landscape involving the market share of major players, along with the new projects and strategies adopted by players in the past five years
* Comprehensive company profiles covering the product offerings, key financial information, recent developments, SWOT analysis, and strategies employed by the major market players
* 1-year analyst support, along with the data support in excel format.
We also can offer customized report to fulfill special requirements of our clients. Regional and Countries report can be provided as well.

In January 2018, Arcep and the Government announced commitments from operators to accelerate mobile coverage of the territories. These commitments were transcribed in their current licences in July 2018 in order to make them legally enforceable. Furthermore, Arcep adopted on 15 November 2018 the decision on the outcome of the procedure for the allocation of frequencies in the 900 MHz, 1 800 MHz and 2.1 GHz bands and 4 decisions to authorise the use of frequencies at Bouygues Telecom, Free Mobile, Orange and SFR. Arcep has the power to impose penalties in the event of failure to comply with the obligations laid down in the frequency authorisations: It ensures the proper execution and implementation of the New Deal by operators in this context. As announced by the President of the Republic in Bastia on 7 February 2018, this monitoring is accompanied by a scoreboard of Arcep measuring the correct implementation of the commitments. This tool brings together a set of six indicators showing in a transparent way the progress of operators on each of the axes of the New Deal mobile: — The 4G for all — Targeted coverage — Indoor cover — The 4G by car — The state of mobile networks — Fixed 4G The aim is to provide elected officials and all observers with information that allows them to have both a national and a territorial view of the progress of commitments. For example, the tool presents interactive maps that allow, for example, for a given territory to visualise the deployment of new sites (including those requested by communities under the New Deal Mobile). In order to allow their wide re-use, in particular by the territories, data (national and local) are available in open data. More details on data, formats and projection systems can be found in the resource descriptions. Find the publication calendar of Arcep. More

An example: Animal KB.

this graph was created in Tableau,PowerBi and Loocker Studio :

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F099789bafdb425411ad3412c939a5066%2Ffoto1.jpg?generation=1742675759012936&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F54ee4e4fd6f38968a6fcaced3af854ca%2Ffoto2.jpg?generation=1742675765079037&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fe0ffb01789711422f6a32d1d02fc3d62%2Ffoto3.png?generation=1742675771960362&alt=media" alt="">

The mission of the Food Surveys Research Group is to study and understand what people in the United States eat and how they behave when it comes to food. To do this, they conduct surveys, which means they ask many people questions about what they eat, how much they eat, and other habits related to food. Then, they collect all this information and analyze it to see patterns and trends. This information is very important because it helps experts and government officials make decisions about food and nutrition programs that can improve people's health. For example, if they notice that many people are not eating enough fruits and vegetables, they can create programs to encourage healthier eating. The data can also help shape public policies, like rules about school lunches or food safety. In short, this group works to understand what people eat so that better decisions can be made to help everyone have a healthier diet.

Cancer, the second-leading cause of mortality, kills 16% of people worldwide. Unhealthy lifestyles, smoking, alcohol abuse, obesity, and a lack of exercise have been linked to cancer incidence and mortality. However, it is hard. Cancer and lifestyle correlation analysis and cancer incidence and mortality prediction in the next several years are used to guide people’s healthy lives and target medical financial resources. Two key research areas of this paper are Data preprocessing and sample expansion design Using experimental analysis and comparison, this study chooses the best cubic spline interpolation technology on the original data from 32 entry points to 420 entry points and converts annual data into monthly data to solve the problem of insufficient correlation analysis and prediction. Factor analysis is possible because data sources indicate changing factors. TSA-LSTM Two-stage attention design a popular tool with advanced visualization functions, Tableau, simplifies this paper’s study. Tableau’s testing findings indicate it cannot analyze and predict this paper’s time series data. LSTM is utilized by the TSA-LSTM optimization model. By commencing with input feature attention, this model attention technique guarantees that the model encoder converges to a subset of input sequence features during the prediction of output sequence features. As a result, the model’s natural learning trend and prediction quality are enhanced. The second step, time performance attention, maintains We can choose network features and improve forecasts based on real-time performance. Validating the data source with factor correlation analysis and trend prediction using the TSA-LSTM model Most cancers have overlapping risk factors, and excessive drinking, lack of exercise, and obesity can cause breast, colorectal, and colon cancer. A poor lifestyle directly promotes lung, laryngeal, and oral cancers, according to visual tests. Cancer incidence is expected to climb 18–21% between 2020 and 2025, according to 2021. Long-term projection accuracy is 98.96 percent, and smoking and obesity may be the main cancer causes.