This synthetic dataset is designed specifically for practicing data visualization and exploratory data analysis (EDA) using popular Python libraries like Seaborn, Matplotlib, and Pandas.

Unlike most public datasets, this one includes a diverse mix of column types:

📅 Date columns (for time series and trend plots) 🔢 Numerical columns (for histograms, boxplots, scatter plots) 🏷️ Categorical columns (for bar charts, group analysis)

Whether you are a beginner learning how to visualize data or an intermediate user testing new charting techniques, this dataset offers a versatile playground.

Feel free to:

Create EDA notebooks Practice plotting techniques Experiment with filtering, grouping, and aggregations 🛠️ No missing values, no data cleaning needed — just download and start exploring!

Hope you find this helpful. Looking forward to hearing from you all.

Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

This dataset compiles the top 2500 datasets from Kaggle, encompassing a diverse range of topics and contributors. It provides insights into dataset creation, usability, popularity, and more, offering valuable information for researchers, analysts, and data enthusiasts.

Research Analysis: Researchers can utilize this dataset to analyze trends in dataset creation, popularity, and usability scores across various categories.

Contributor Insights: Kaggle contributors can explore the dataset to gain insights into factors influencing the success and engagement of their datasets, aiding in optimizing future submissions.

Machine Learning Training: Data scientists and machine learning enthusiasts can use this dataset to train models for predicting dataset popularity or usability based on features such as creator, category, and file types.

Market Analysis: Analysts can leverage the dataset to conduct market analysis, identifying emerging trends and popular topics within the data science community on Kaggle.

Educational Purposes: Educators and students can use this dataset to teach and learn about data analysis, visualization, and interpretation within the context of real-world datasets and community-driven platforms like Kaggle.

Column Definitions:

Dataset Name: Name of the dataset. Created By: Creator(s) of the dataset. Last Updated in number of days: Time elapsed since last update. Usability Score: Score indicating the ease of use. Number of File: Quantity of files included. Type of file: Format of files (e.g., CSV, JSON). Size: Size of the dataset. Total Votes: Number of votes received. Category: Categorization of the dataset's subject matter.

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

Specialized collection of 0 free data visualization SVG illustrations from the technology & electronics category. Data visualization illustrations including bar charts, network graphs, and information graphics Examples include: bar chart, network graph.

Snapshot img

Data Visualization Tools Market Size 2025-2029

The data visualization tools market size is forecast to increase by USD 7.95 billion at a CAGR of 11.2% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for business intelligence and AI-powered insights. Companies are recognizing the value of transforming complex data into easily digestible visual representations to inform strategic decision-making. However, this market faces challenges as data complexity and massive data volumes continue to escalate. Organizations must invest in advanced data visualization tools to effectively manage and analyze their data to gain a competitive edge. The ability to automate data visualization processes and integrate AI capabilities will be crucial for companies to overcome the challenges posed by data complexity and volume. By doing so, they can streamline their business operations, enhance data-driven insights, and ultimately drive growth in their respective industries.

What will be the Size of the Data Visualization Tools Market during the forecast period?

Request Free SampleIn today's data-driven business landscape, the market continues to evolve, integrating advanced capabilities to support various sectors in making informed decisions. Data storytelling and preparation are crucial elements, enabling organizations to effectively communicate complex data insights. Real-time data visualization ensures agility, while data security safeguards sensitive information. Data dashboards facilitate data exploration and discovery, offering data-driven finance, strategy, and customer experience. Big data visualization tackles complex datasets, enabling data-driven decision making and innovation. Data blending and filtering streamline data integration and analysis. Data visualization software supports data transformation, cleaning, and aggregation, enhancing data-driven operations and healthcare. On-premises and cloud-based solutions cater to diverse business needs. Data governance, ethics, and literacy are integral components, ensuring data-driven product development, government, and education adhere to best practices. Natural language processing, machine learning, and visual analytics further enrich data-driven insights, enabling interactive charts and data reporting. Data connectivity and data-driven sales fuel business intelligence and marketing, while data discovery and data wrangling simplify data exploration and preparation. The market's continuous dynamism underscores the importance of data culture, data-driven innovation, and data-driven HR, as organizations strive to leverage data to gain a competitive edge.

How is this Data Visualization Tools Industry segmented?

The data visualization tools industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. DeploymentOn-premisesCloudCustomer TypeLarge enterprisesSMEsComponentSoftwareServicesApplicationHuman resourcesFinanceOthersEnd-userBFSIIT and telecommunicationHealthcareRetailOthersGeographyNorth AmericaUSMexicoEuropeFranceGermanyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.The market has experienced notable expansion as businesses across diverse sectors acknowledge the significance of data analysis and representation to uncover valuable insights and inform strategic decisions. Data visualization plays a pivotal role in this domain. On-premises deployment, which involves implementing data visualization tools within an organization's physical infrastructure or dedicated data centers, is a popular choice. This approach offers organizations greater control over their data, ensuring data security, privacy, and adherence to data governance policies. It caters to industries dealing with sensitive data, subject to regulatory requirements, or having stringent security protocols that prohibit cloud-based solutions. Data storytelling, data preparation, data-driven product development, data-driven government, real-time data visualization, data security, data dashboards, data-driven finance, data-driven strategy, big data visualization, data-driven decision making, data blending, data filtering, data visualization software, data exploration, data-driven insights, data-driven customer experience, data mapping, data culture, data cleaning, data-driven operations, data aggregation, data transformation, data-driven healthcare, on-premises data visualization, data governance, data ethics, data discovery, natural language processing, data reporting, data visualization platforms, data-driven innovation, data wrangling, data-driven sales, data connectivit

Context

This dataset contains 8 attributes of the countries visa free travel along with their GDP and incoming tourist.

Content

Attributes: long lat country_name country_connection gdp_percapita incoming_tourist outgoing_tourist iso3_digit_code

Acknowledgements

Data is derived from https://www.passportindex.org/byRank.php

Inspiration

Goal is to find useful insights

Data presentation for scientific publications in small sample size studies has not changed substantially in decades. It relies on static figures and tables that may not provide sufficient information for critical evaluation, particularly of the results from small sample size studies. Interactive graphics have the potential to transform scientific publications from static reports of experiments into interactive datasets. We designed an interactive line graph that demonstrates how dynamic alternatives to static graphics for small sample size studies allow for additional exploration of empirical datasets. This simple, free, web-based tool (http://statistika.mfub.bg.ac.rs/interactive-graph/) demonstrates the overall concept and may promote widespread use of interactive graphics.

The PlantVillage dataset consists of 54303 healthy and unhealthy leaf images divided into 38 categories by species and disease.

NOTE: The original dataset is not available from the original source (plantvillage.org), therefore we get the unaugmented dataset from a paper that used that dataset and republished it. Moreover, we dropped images with Background_without_leaves label, because these were not present in the original dataset.

Original paper URL: https://arxiv.org/abs/1511.08060 Dataset URL: https://data.mendeley.com/datasets/tywbtsjrjv/1

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('plant_village', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/plant_village-1.0.2.png" alt="Visualization" width="500px">

Source code for Juicebox sent to reviewers

This visualization is a dashboard (2 pages), but the focus is on page 1, where there are 3 visuals, a word cloud, bubble chart and a bar chart, that are completely interactive (like every single visual can be interacted with to change the entire dashboard), along with selectable filters, you can use these to see real time, correlations between ingredients and cuisines, and visualize what cuisine leans towards what kind of ingredients, and even variants of specific ingredients. The second page contains, filters that show you more numerical data, where you can see side by side comparisons, of ingredients within two separate cuisines, or even the extent to which, two cuisines can use the same ingredient.This viz was submitted as part of the Data Bloom 2024 Viz competition.This viz was created using PowerBI and is based on the following data source: Kaggle - https://www.kaggle.com/datasets/kaggle/recipe-ingredients-dataset/dataPowerBI or a free viewer is required to render and view the full dynamic visualization within the PBIX file.

This file contains raw data for cameras and wearables of the ConfLab dataset.

./cameras contains the overhead video recordings for 9 cameras (cam2-10) in MP4 files. These cameras cover the whole interaction floor, with camera 2 capturing the bottom of the scene layout, and camera 10 capturing top of the scene layout. Note that cam5 ran out of battery before the other cameras and thus the recordings are cut short. However, cam4 and 6 contain significant overlap with cam 5, to reconstruct any information needed.

Note that the annotations are made and provided in 2 minute segments. 
The annotated portions of the video include the last 3min38sec of x2xxx.MP4 
video files, and the first 12 min of x3xxx.MP4 files for cameras (2,4,6,8,10), 
with "x" being the placeholder character in the mp4 file names. If one wishes 
to separate the video into 2 min segments as we did, the "video-splitting.sh"
script is provided. 

./camera-calibration contains the camera instrinsic files obtained from https://github.com/idiap/multicamera-calibration. Camera extrinsic parameters can be calculated using the existing intrinsic parameters and the instructions in the multicamera-calibration repo. The coordinates in the image are provided by the crosses marked on the floor, which are visible in the video recordings. The crosses are 1m apart (=100cm).

./wearables subdirectory includes the IMU, proximity and audio data from each participant at the Conflab event (48 in total). In the directory numbered by participant ID, the following data are included: 1. raw audio file 2. proximity (bluetooth) pings (RSSI) file (raw and csv) and a visualization 3. Tri-axial accelerometer data (raw and csv) and a visualization 4. Tri-axial gyroscope data (raw and csv) and a visualization 5. Tri-axial magnetometer data (raw and csv) and a visualization 6. Game rotation vector (raw and csv), recorded in quaternions.

All files are timestamped.
The sampling frequencies are:
  - audio: 1250 Hz
  - rest: around 50Hz. However, the sample rate is not fixed
  and instead the timestamps should be used. 

For rotation, the game rotation vector's output frequency is limited by the 
actual sampling frequency of the magnetometer. For more information, please refer to 
https://invensense.tdk.com/wp-content/uploads/2016/06/DS-000189-ICM-20948-v1.3.pdf

Audio files in this folder are in raw binary form. The following can be used to convert
them to WAV files (1250Hz):

  ffmpeg -f s16le -ar 1250 -ac 1 -i /path/to/audio/file

Synchronization of cameras and werables data Raw videos contain timecode information which matches the timestamps of the data in the "wearables" folder. The starting timecode of a video can be read as: ffprobe -hide_banner -show_streams -i /path/to/video

./audio ./sync: contains wav files per each subject ./sync_files: auxiliary csv files used to sync the audio. Can be used to improve the synchronization. The code used for syncing the audio can be found here:
https://github.com/TUDelft-SPC-Lab/conflab/tree/master/preprocessing/audio

Data Visualization Market Outlook

As per our latest research, the global data visualization market size reached USD 12.8 billion in 2024, reflecting robust adoption across diverse industries. The market is projected to expand at a strong CAGR of 10.4% from 2025 to 2033, reaching an estimated USD 31.2 billion by 2033. This remarkable growth is primarily driven by the increasing need for actionable insights from big data, the proliferation of advanced analytics tools, and the growing emphasis on real-time decision-making within enterprises worldwide.

One of the primary growth factors propelling the data visualization market is the exponential increase in data generation across all sectors. Organizations are now inundated with structured and unstructured data from multiple sources such as IoT devices, social media platforms, enterprise applications, and transactional systems. The sheer volume and complexity of this data make traditional reporting tools inadequate for deriving meaningful insights. As a result, businesses are turning to advanced data visualization solutions that enable them to quickly interpret complex datasets, identify trends, and make informed decisions. The integration of artificial intelligence and machine learning into visualization platforms further enhances their capability to deliver predictive analytics and automated insights, which is fueling market expansion.

Another significant driver is the growing adoption of business intelligence (BI) and analytics platforms across organizations of all sizes. Companies are increasingly recognizing the value of data-driven decision-making, which has led to the widespread implementation of BI tools that rely heavily on effective data visualization. These platforms not only facilitate the exploration of large datasets but also enable users to create interactive dashboards and reports that can be easily shared across departments. The democratization of data analytics, where non-technical users can generate their own visualizations without relying on IT teams, has further accelerated market growth. Additionally, the shift towards cloud-based deployment models is making these solutions more accessible and cost-effective for small and medium enterprises (SMEs), broadening the market’s reach.

The rapid digital transformation initiatives undertaken by enterprises, particularly in emerging economies, are also contributing to the robust growth of the data visualization market. Digitalization efforts have led to the modernization of legacy IT infrastructure, the adoption of cloud computing, and the implementation of advanced analytics solutions. Governments and regulatory bodies are also encouraging the use of data analytics for transparency and efficiency, especially in sectors such as healthcare, public services, and finance. The increasing focus on customer experience, operational efficiency, and competitive differentiation is compelling organizations to invest in visualization tools that provide real-time insights and facilitate agile business processes. These factors collectively underpin the sustained growth trajectory of the global data visualization market.

From a regional perspective, North America continues to dominate the data visualization market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The region’s leadership is attributed to the high adoption rate of advanced analytics solutions, the presence of major technology providers, and a mature digital ecosystem. Meanwhile, Asia Pacific is witnessing the fastest growth, driven by rapid industrialization, increasing IT investments, and the proliferation of cloud computing across countries like China, India, and Japan. Latin America and the Middle East & Africa are also experiencing steady growth, fueled by digital transformation initiatives and the rising demand for data-driven decision-making in both public and private sectors.

Component Analysis

The data visualization market is segmented by component into software

🔍 Total Sales: Achieved $456,000 in revenue across 1,000 transactions, with an average transaction value of $456.00.

👥 Customer Demographics:

Average Age: 41.39 years Gender Distribution: 51% male, 49% female Most active age groups: 31-40 & 41-50 years 🏷️ Product Performance:

Top Categories: Electronics and Clothing led the sales, each contributing $160,000, followed by Beauty products with $140,000. Quantity Sold: Clothing topped the charts with 894 units sold. 📈 Sales Trends: Identified key sales peaks, especially in May 2023, indicating the success of targeted promotional strategies.

Why This Matters:

Understanding these metrics allows for better-targeted marketing, efficient inventory management, and strategic planning to capitalize on peak sales periods. This project demonstrates the power of data-driven decision-making in retail!

💡 Takeaway: Power BI continues to be a game-changer in visualizing and interpreting complex data, helping businesses to not just see numbers but to translate them into actionable insights.

I’m always looking forward to new challenges and projects that push my skills further. If you're interested in diving into the details or discussing data insights, feel free to reach out!

PowerBI #DataAnalysis #RetailSales #DataVisualization #BusinessIntelligence #DataDriven

Learning Data Visualization Tools Market Outlook

According to our latest research, the global Learning Data Visualization Tools Market size reached USD 2.8 billion in 2024, demonstrating robust growth driven by the increasing demand for data literacy and analytics skills across various sectors. The market is expected to grow at a CAGR of 13.7% from 2025 to 2033, projecting a value of USD 8.8 billion by 2033. This surge is primarily attributed to the rapid digitization of education and corporate learning environments, the proliferation of big data, and the critical need for interactive, accessible analytical tools to foster effective data comprehension and decision-making.

One of the most significant growth factors for the Learning Data Visualization Tools Market is the widespread integration of data-driven decision-making processes within organizations and educational institutions. As businesses and academic settings increasingly rely on data to guide strategies, there is a parallel surge in the demand for professionals who possess strong data visualization skills. This has led to a marked increase in the adoption of user-friendly data visualization tools such as Tableau, Power BI, and Google Data Studio in both formal education and corporate training programs. The ability of these tools to simplify complex datasets into intuitive visual representations is a key driver, enabling learners to grasp intricate concepts more efficiently and apply them in real-world scenarios.

Technological advancements and the evolution of cloud-based learning platforms have further propelled the market. The shift toward digital and remote learning, especially post-pandemic, has accelerated the adoption of cloud-based data visualization tools, which offer scalability, accessibility, and seamless integration with other e-learning resources. Cloud deployment eliminates geographical barriers, allowing learners and organizations from diverse regions to access advanced visualization tools and resources at any time. Additionally, the increasing availability of free and open-source visualization libraries such as D3.js has democratized access to these technologies, further expanding the market’s reach across different socioeconomic segments.

Another crucial growth driver is the rising emphasis on upskilling and reskilling initiatives across industries. As automation and artificial intelligence reshape job requirements, data literacy has become a fundamental skill for both students and working professionals. Enterprises are investing heavily in learning platforms that incorporate data visualization tools to train their workforce, ensuring they remain competitive in the digital economy. The trend is mirrored in higher education, where curricula are being revamped to include data visualization modules, reflecting the growing recognition of its importance in fostering analytical and critical thinking skills among learners.

From a regional perspective, North America dominates the Learning Data Visualization Tools Market, accounting for the largest revenue share in 2024. This can be attributed to the presence of leading technology providers, a mature e-learning ecosystem, and high levels of digital adoption in both educational and corporate sectors. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digital transformation, government initiatives to enhance digital literacy, and the increasing penetration of internet and mobile devices. Europe also contributes significantly, with a strong focus on educational innovation and enterprise training. These regional dynamics are shaping the competitive landscape and driving the global expansion of learning data visualization tools.

Tool Type Analysis

The Tool Type segment of the Learning Data Visualization Tools Market is highly diverse, encompassing established platforms like Tableau, Power BI, and Qlik, as well as newer entrants such as Google Data Studio and open-source solutions like D3.js. Tableau remains a market leader due to its intuitive drag-and-drop interface, robust analytics capabilities, and widespread adoption in both academic and corporate settings. Its ability to handle large datasets and integrate seamlessly with various data sources makes it a preferred choice for institutions aiming to provide hands-on, practical training in data visualization. Power BI, backed by Microsoft’s ecosystem, is gaining significant traction, particularly among enterpr

Snapshot img

Data Science Platform Market Size 2025-2029

The data science platform market size is valued to increase USD 763.9 million, at a CAGR of 40.2% from 2024 to 2029. Integration of AI and ML technologies with data science platforms will drive the data science platform market.

Major Market Trends & Insights

North America dominated the market and accounted for a 48% growth during the forecast period.
By Deployment - On-premises segment was valued at USD 38.70 million in 2023
By Component - Platform segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 1.00 million
Market Future Opportunities: USD 763.90 million
CAGR : 40.2%
North America: Largest market in 2023

Market Summary

The market represents a dynamic and continually evolving landscape, underpinned by advancements in core technologies and applications. Key technologies, such as machine learning and artificial intelligence, are increasingly integrated into data science platforms to enhance predictive analytics and automate data processing. Additionally, the emergence of containerization and microservices in data science platforms enables greater flexibility and scalability. However, the market also faces challenges, including data privacy and security risks, which necessitate robust compliance with regulations.
According to recent estimates, the market is expected to account for over 30% of the overall big data analytics market by 2025, underscoring its growing importance in the data-driven business landscape.

What will be the Size of the Data Science Platform Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Data Science Platform Market Segmented and what are the key trends of market segmentation?

The data science platform industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  On-premises
  Cloud


Component

  Platform
  Services


End-user

  BFSI
  Retail and e-commerce
  Manufacturing
  Media and entertainment
  Others


Sector

  Large enterprises
  SMEs


Application

  Data Preparation
  Data Visualization
  Machine Learning
  Predictive Analytics
  Data Governance
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

In the dynamic and evolving the market, big data processing is a key focus, enabling advanced model accuracy metrics through various data mining methods. Distributed computing and algorithm optimization are integral components, ensuring efficient handling of large datasets. Data governance policies are crucial for managing data security protocols and ensuring data lineage tracking. Software development kits, model versioning, and anomaly detection systems facilitate seamless development, deployment, and monitoring of predictive modeling techniques, including machine learning algorithms, regression analysis, and statistical modeling. Real-time data streaming and parallelized algorithms enable real-time insights, while predictive modeling techniques and machine learning algorithms drive business intelligence and decision-making.

Cloud computing infrastructure, data visualization tools, high-performance computing, and database management systems support scalable data solutions and efficient data warehousing. ETL processes and data integration pipelines ensure data quality assessment and feature engineering techniques. Clustering techniques and natural language processing are essential for advanced data analysis. The market is witnessing significant growth, with adoption increasing by 18.7% in the past year, and industry experts anticipate a further expansion of 21.6% in the upcoming period. Companies across various sectors are recognizing the potential of data science platforms, leading to a surge in demand for scalable, secure, and efficient solutions.

API integration services and deep learning frameworks are gaining traction, offering advanced capabilities and seamless integration with existing systems. Data security protocols and model explainability methods are becoming increasingly important, ensuring transparency and trust in data-driven decision-making. The market is expected to continue unfolding, with ongoing advancements in technology and evolving business needs shaping its future trajectory.

Request Free Sample

The On-premises segment was valued at USD 38.70 million in 2019 and showed

The MNIST database of handwritten digits.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('mnist', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/mnist-3.0.1.png" alt="Visualization" width="500px">

Management Dashboard - Fantasie GmbH

Description:

This is an interactive management dashboard I created in Microsoft Excel using fictional data. It visualizes key business metrics such as annual revenue, sales by employees and branches, as well as product trends. The dashboard incorporates VBA-powered buttons for navigation and control, along with functions like IF and VLOOKUP for dynamic data processing.

This dashboard is intended for orientation and inspiration for your own projects. The dataset used is entirely fictional and is not included.

License: CC0 - Free to use and adapt.

Dataset Card for MathVista

Dataset Description Paper Information Dataset Examples Leaderboard Dataset Usage Data Downloading Data Format Data Visualization Data Source Automatic Evaluation

License Citation

  Dataset Description

MathVista is a consolidated Mathematical reasoning benchmark within Visual contexts. It consists of three newly created datasets, IQTest, FunctionQA, and PaperQA, which address the missing visual domains and are tailored to evaluate logical… See the full description on the dataset page: https://huggingface.co/datasets/AI4Math/MathVista.

The Global Marine Environment Datasets (GMED) is a compilation of publicly available climatic, biological and geophysical environmental layers featuring present, past and future environmental conditions. GMED covers the widest available range of environmental layers from a variety of sources and depths from the surface to the deepest part of the ocean. It has a uniform spatial extent, high-resolution land mask (to eliminate land areas in the marine regions), and high spatial resolution (5 arc-minute, c. 9.2 km near equator). The free online availability of GMED enables rapid map overlay of species of interest (e.g. endangered or invasive) against different environmental conditions of the past, present and the future, and expedites mapping distribution ranges of species using popular SDM algorithms. This archive features a snapshot of GMED dataset in 2019 as a single archive.