The Exploratory Data Analysis (EDA) tools market is experiencing robust growth, driven by the increasing volume and complexity of data across industries. The market, estimated at $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising adoption of big data analytics across large enterprises and SMEs necessitates efficient tools for data exploration and visualization. Secondly, the shift towards data-driven decision-making across various sectors, including finance, healthcare, and retail, is creating substantial demand. The increasing availability of user-friendly, graphical EDA tools further contributes to market growth, lowering the barrier to entry for non-technical users. While the market faces constraints such as the need for skilled data analysts and potential integration challenges with existing systems, these are being mitigated by the development of more intuitive interfaces and cloud-based solutions. The segmentation reveals a strong preference for graphical EDA tools due to their enhanced visual representation and improved insights compared to non-graphical alternatives. Large enterprises currently dominate the market share, however, the increasing adoption of data analytics by SMEs presents a significant growth opportunity in the coming years. Geographic expansion is also a key driver; North America currently holds the largest market share, but the Asia-Pacific region is projected to witness the fastest growth due to increasing digitalization and data generation in countries like China and India. The competitive landscape is characterized by a mix of established players like IBM and emerging innovative companies. The key players are actively engaged in strategic initiatives such as product development, partnerships, and mergers and acquisitions to consolidate their market position. The future of the EDA tools market hinges on continuous innovation, particularly in areas like artificial intelligence (AI) integration for automated insights and improved user experience features. The market will continue to mature, creating opportunities for specialized niche players focusing on specific industry requirements. This will drive further fragmentation of the market, pushing existing major players to adopt new strategies focused on customer retention and the development of high-value services alongside their core offerings. This market evolution promises to make data exploration and analysis more accessible and valuable across industries, leading to further improvements in decision-making and business outcomes.

Comprehensive exploratory data analysis

This dataset supports a study examining how students perceive the usefulness of artificial intelligence (AI) in educational settings. The project involved analyzing an open-access survey dataset that captures a wide range of student responses on AI tools in learning.

The data underwent cleaning and preprocessing, followed by an exploratory data analysis (EDA) to identify key trends and insights. Visualizations were created to support interpretation, and the results were summarized in a digital poster format to communicate findings effectively.

This resource may be useful for researchers, educators, and technologists interested in the evolving role of AI in education.

Keywords: Artificial Intelligence, Education, Student Perception, Survey, Data Analysis, EDA

Subject: Computer and Information Science

License: CC0 1.0 Universal Public Domain Dedication

DOI: https://doi.org/10.18738/T8/RXUCHK

The global Exploratory Data Analysis (EDA) Tools market is anticipated to experience significant growth in the coming years, driven by the increasing adoption of data-driven decision-making and the growing need for efficient data exploration and analysis. The market size is valued at USD XX million in 2025 and is projected to reach USD XX million by 2033, registering a CAGR of XX% during the forecast period. The increasing complexity and volume of data generated by businesses and organizations have necessitated the use of advanced data analysis tools to derive meaningful insights and make informed decisions. Key trends driving the market include the rising adoption of AI and machine learning technologies, the growing need for self-service data analytics, and the increasing emphasis on data visualization and storytelling. Non-graphical EDA tools are gaining traction due to their ability to handle large and complex datasets. Graphical EDA tools are preferred for their intuitive and interactive user interfaces that simplify data exploration. Large enterprises are major consumers of EDA tools as they have large volumes of data to analyze. SMEs are also increasingly adopting EDA tools as they realize the importance of data-driven insights for business growth. The North American region holds a significant market share due to the presence of established technology companies and a high adoption rate of data analytics solutions. The Asia Pacific region is expected to witness substantial growth due to the rising number of businesses and organizations in emerging economies.

Explore the booming Exploratory Data Analysis (EDA) Tools market, projected to reach $10.5 billion by 2025 with a 12.5% CAGR. Discover key drivers, trends, and market share for large enterprises, SMEs, graphical & non-graphical tools across North America, Europe, APAC, and more.

Understand the satellite dataset before training. Explore distributions, preprocessing steps, and key insights to evaluate 3rd-party models effectively.

Benchmark and compare 3rd-party AI models for breast cancer screening image classification. Focus on sensitivity, false-positive control and enterprise-grade de

This dataset provides an overview of various AI tools, capturing key attributes that highlight their popularity, subscription models, and the categories they fall under. It can serve as a valuable resource for analyzing trends in AI tool usage, comparing different tools based on user feedback, and understanding the market positioning of these tools.

Columns: Name: The name of the AI tool, representing various applications and services in the AI domain. Votes: The number of votes or ratings each tool has received, reflecting its popularity and user acceptance. Subscription: The type of subscription model the tool offers, indicating whether it is free, freemium (a mix of free and paid features), or paid. Category: A list of categories associated with each tool, identifying the primary industries or use cases it caters to, such as: Human Resources Legal AI Chatbots Marketing Education Video Generators Writing Generators Storytellers Presentations Startup Tools Dataset Use Cases: Market Analysis: Understand which AI tools are most popular based on user votes and explore trends across different categories. Product Comparison: Compare AI tools based on their subscription models, identifying which tools offer free or freemium options versus paid-only models. Category Insights: Analyze the distribution of AI tools across various categories to see where innovation and adoption are most concentrated.

Discover, test and benchmark 3rd-party AI models for drone-based crowd and traffic detection — accuracy, latency & rare-object performance for enterprise use.

Benchmark and compare 3rd-party AI models for weld defect detection & NDT in automotive production lines. Focus on recall, latency and enterprise deployment.

Dataset Description This dataset contains opinions about Generative AI collected from social media platform Twitter. The data was gathered from 2021 to 2024 using various relevant keywords, such as: - generative ai opinion - generative ai thought - generative ai controversy - generative ai impact - generative ai ethics - generative ai risks - generative ai vs human - ai generated misinformation - generative ai backlash - generative ai opportunities - generative ai policy - #AIFailure - ai helps human - future of generative ai

The dataset aims to provide insights into public opinions about Generative AI, highlighting both its opportunities and challenges, and is expected to be valuable for research or analysis on public opinion, ethics, policies, and the impacts of Generative AI.

Citation Falif, Muhammad Sya'bani. Generative AI Opinion Dataset on Twitter. 2024. Kaggle.

Discover the explosive growth of the data visualization market, projected at a 15% CAGR to reach $153 billion by 2033. This in-depth analysis reveals key trends, leading companies like Tableau and Sisense, and regional market breakdowns. Learn how data visualization is transforming business intelligence.

Context: Artificial Intelligence is evolving faster than ever, with new tools and models being released every few months. From large language models (LLMs) like ChatGPT, Claude, and Gemini, to image generation tools like Midjourney and Stable Diffusion, the AI ecosystem is expanding across multiple modalities like text, image, video, audio, and beyond. This dataset was created to provide a single, structured source of information about the most important AI tools available today. It captures their companies, categories, capabilities, release years, and availability (API/open-source), making it easier for researchers, developers, and AI enthusiasts to explore and analyze the rapidly changing AI landscape.

Content: This dataset includes 113 AI tools and models, with 22 attributes that describe their features and availability:

• Tool Name & Company

• Category (LLMs, Image Gen, Productivity, etc.)

• Modalities (Text, Image, Video, Audio, Code, Multimodal…)

• Open-Source Status

• API Availability & Website

• Release Year

• Other metadata (domains, API status, modality counts, etc.)

• It’s designed to give a structured overview of the AI landscape for 2022–2025.

Acknowledgements:

This dataset is compiled from publicly available information about AI tools and companies. Special thanks to the open-source and AI research community for making insights about generative AI accessible.

Inspiration:

• Trend Analysis: Study how AI tools have evolved over time

• Comparison: Compare companies and their approaches to multimodality

• Visualization: Build charts showing AI adoption across modalities

• Innovation: Use the dataset as a foundation for new AI research or projects

• This dataset is perfect for anyone interested in exploring the current state of generative AI and its future directions.

This dataset simulates the future of work in the age of artificial intelligence. It models how various professions, skills, and education levels might be impacted by AI-driven automation by the year 2030.

The goal is to enable research, machine learning modeling, and data visualization around the question:

“Which types of jobs are most at risk of automation — and why?”

It can be used for:

Predictive modeling of automation probability

Explainable AI (XAI) on social and economic impacts

Interactive dashboards exploring the future of work

Educational and research purposes

📖 Description

This dataset captures the emerging trends in AI-generated art, analyzing views, likes, shares, comments, and pricing across popular digital art platforms like Opensea, Instagram, DeviantArt, and SuperRare.
It is useful for:
- Trend analysis: Identifying what styles and platforms drive the most engagement.
- Market prediction: Understanding pricing patterns for AI-generated artwork.

We did data analysis on a open dataset which contained responses regarding a survey about how useful students find AI in the educational process. We cleaned the data, preprocessed and then did analysis on it. We did an EDA (Exploratory Data Analysis) on the dataset and visualized the results and our findings. Then we interpreted the findings into our digital poster.

🏆 100 AI Companies in 2024

This dataset was scraped from Datamation(🔗Link), containing insights into top AI firms.

Columns

🏢 Company Name – Name of the company
📍 Headquarters – Location of HQ
📅 Founded – Year of establishment
💰 Annual Revenue – Reported revenue
⭐ Glassdoor Score – Employee rating

Possible Uses

🔹 Data Cleaning (removing symbols, normalizing data)
🔹 Predictive Analysis (estimating revenue trends)
🔹 AI Industry Insights

In modern centrifugal pump machines (CPM), a data acquisition system encompassing software- hardware interfacing is essential for parameter recording. The quality of recorded data plays a crucial role and directly influences the data transformation phase in machine learning (ML) and deep learning (DL) models. The Dewesoft FFT DAQ system is designed to extract the high-quality data from the CPM based on sensor fusion technology. The data recorded from DAQ system undergoes thorough in-depth analysis, processing & transformation before being incorporated into machine learning (ML) or artificial intelligence models. This paper emphasizes the importance of data cleaning, pre-processing, and applying appropriate methodologies to transform raw data into a valuable resource that can be utilized by ML and AI models. Key techniques include Exploratory Data Analysis (EDA), Data Visualization, and Feature Engineering (FE), which collectively enhance data interpretability. Following these transformations, hypothesis testing validates the data’s integrity, ensuring reliability for subsequent modeling. The validated data is employed to train machine learning classifiers and deep learning algorithms, targeting a 27.25% enhancement in operational efficiency based on F1 score. Additionally, it decreases model training time by 180 seconds, facilitating predictive maintenance of critical performance metrics and minimizing downtime. The assessment of model performance relies on Precision, Recall, and F1 score. This approach leverages recent advancements in data science to derive actionable insights from CPM data, facilitating more informed decision-making and optimization of pump operations.

Analysis of ‘Latest Covid-19 india statewise data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/swatikhedekar/latest-covid19-india-statewise-data on 12 November 2021.

--- Dataset description provided by original source is as follows ---

This dataset contains latest covid-19 data of India state-wise as on September 30, 2021.This dataset can be used for data analysis of covid-19 in India. This dataset is also used for Exploratory data analysis and Data visualization.

--- Original source retains full ownership of the source dataset ---

In modern centrifugal pump machines (CPM), a data acquisition system encompassing software- hardware interfacing is essential for parameter recording. The quality of recorded data plays a crucial role and directly influences the data transformation phase in machine learning (ML) and deep learning (DL) models. The Dewesoft FFT DAQ system is designed to extract the high-quality data from the CPM based on sensor fusion technology. The data recorded from DAQ system undergoes thorough in-depth analysis, processing & transformation before being incorporated into machine learning (ML) or artificial intelligence models. This paper emphasizes the importance of data cleaning, pre-processing, and applying appropriate methodologies to transform raw data into a valuable resource that can be utilized by ML and AI models. Key techniques include Exploratory Data Analysis (EDA), Data Visualization, and Feature Engineering (FE), which collectively enhance data interpretability. Following these transformations, hypothesis testing validates the data’s integrity, ensuring reliability for subsequent modeling. The validated data is employed to train machine learning classifiers and deep learning algorithms, targeting a 27.25% enhancement in operational efficiency based on F1 score. Additionally, it decreases model training time by 180 seconds, facilitating predictive maintenance of critical performance metrics and minimizing downtime. The assessment of model performance relies on Precision, Recall, and F1 score. This approach leverages recent advancements in data science to derive actionable insights from CPM data, facilitating more informed decision-making and optimization of pump operations.