The Exploratory Data Analysis (EDA) tools market is experiencing robust growth, driven by the increasing need for businesses to derive actionable insights from their ever-expanding datasets. The market, currently estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated $45 billion by 2033. This growth is fueled by several factors, including the rising adoption of big data analytics, the proliferation of cloud-based solutions offering enhanced accessibility and scalability, and the growing demand for data-driven decision-making across diverse industries like finance, healthcare, and retail. The market is segmented by application (large enterprises and SMEs) and type (graphical and non-graphical tools), with graphical tools currently holding a larger market share due to their user-friendly interfaces and ability to effectively communicate complex data patterns. Large enterprises are currently the dominant segment, but the SME segment is anticipated to experience faster growth due to increasing affordability and accessibility of EDA solutions. Geographic expansion is another key driver, with North America currently holding the largest market share due to early adoption and a strong technological ecosystem. However, regions like Asia-Pacific are exhibiting high growth potential, fueled by rapid digitalization and a burgeoning data science talent pool. Despite these opportunities, the market faces certain restraints, including the complexity of some EDA tools requiring specialized skills and the challenge of integrating EDA tools with existing business intelligence platforms. Nonetheless, the overall market outlook for EDA tools remains highly positive, driven by ongoing technological advancements and the increasing importance of data analytics across all sectors. The competition among established players like IBM Cognos Analytics and Altair RapidMiner, and emerging innovative companies like Polymer Search and KNIME, further fuels market dynamism and innovation.

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.

Thorough knowledge of the structure of analyzed data allows to form detailed scientific hypotheses and research questions. The structure of data can be revealed with methods for exploratory data analysis. Due to multitude of available methods, selecting those which will work together well and facilitate data interpretation is not an easy task. In this work we present a well fitted set of tools for a complete exploratory analysis of a clinical dataset and perform a case study analysis on a set of 515 patients. The proposed procedure comprises several steps: 1) robust data normalization, 2) outlier detection with Mahalanobis (MD) and robust Mahalanobis distances (rMD), 3) hierarchical clustering with Ward’s algorithm, 4) Principal Component Analysis with biplot vectors. The analyzed set comprised elderly patients that participated in the PolSenior project. Each patient was characterized by over 40 biochemical and socio-geographical attributes. Introductory analysis showed that the case-study dataset comprises two clusters separated along the axis of sex hormone attributes. Further analysis was carried out separately for male and female patients. The most optimal partitioning in the male set resulted in five subgroups. Two of them were related to diseased patients: 1) diabetes and 2) hypogonadism patients. Analysis of the female set suggested that it was more homogeneous than the male dataset. No evidence of pathological patient subgroups was found. In the study we showed that outlier detection with MD and rMD allows not only to identify outliers, but can also assess the heterogeneity of a dataset. The case study proved that our procedure is well suited for identification and visualization of biologically meaningful patient subgroups.

This dataset contains 55,000 entries of synthetic customer transactions, generated using Python's Faker library. The goal behind creating this dataset was to provide a resource for learners like myself to explore, analyze, and apply various data analysis techniques in a context that closely mimics real-world data.

About the Dataset: - CID (Customer ID): A unique identifier for each customer. - TID (Transaction ID): A unique identifier for each transaction. - Gender: The gender of the customer, categorized as Male or Female. - Age Group: Age group of the customer, divided into several ranges. - Purchase Date: The timestamp of when the transaction took place. - Product Category: The category of the product purchased, such as Electronics, Apparel, etc. - Discount Availed: Indicates whether the customer availed any discount (Yes/No). - Discount Name: Name of the discount applied (e.g., FESTIVE50). - Discount Amount (INR): The amount of discount availed by the customer. - Gross Amount: The total amount before applying any discount. - Net Amount: The final amount after applying the discount. - Purchase Method: The payment method used (e.g., Credit Card, Debit Card, etc.). - Location: The city where the purchase took place.

Use Cases: 1. Exploratory Data Analysis (EDA): This dataset is ideal for conducting EDA, allowing users to practice techniques such as summary statistics, visualizations, and identifying patterns within the data. 2. Data Preprocessing and Cleaning: Learners can work on handling missing data, encoding categorical variables, and normalizing numerical values to prepare the dataset for analysis. 3. Data Visualization: Use tools like Python’s Matplotlib, Seaborn, or Power BI to visualize purchasing trends, customer demographics, or the impact of discounts on purchase amounts. 4. Machine Learning Applications: After applying feature engineering, this dataset is suitable for supervised learning models, such as predicting whether a customer will avail a discount or forecasting purchase amounts based on the input features.

This dataset provides an excellent sandbox for honing skills in data analysis, machine learning, and visualization in a structured but flexible manner.

This is not a real dataset. This dataset was generated using Python's Faker library for the sole purpose of learning

Discover the booming Exploratory Data Analysis (EDA) tools market! Our in-depth analysis reveals key trends, growth drivers, and top players shaping this $3 billion industry, projected for 15% CAGR through 2033. Learn about market segmentation, regional insights, and future opportunities.

Many upcoming and proposed missions to ocean worlds such as Europa, Enceladus, and Titan aim to evaluate their habitability and the existence of potential life on these moons. These missions will suffer from communication challenges and technology limitations. We review and investigate the applicability of data science and unsupervised machine learning (ML) techniques on isotope ratio mass spectrometry data (IRMS) from volatile laboratory analogs of Europa and Enceladus seawaters as a case study for development of new strategies for icy ocean world missions. Our driving science goal is to determine whether the mass spectra of volatile gases could contain information about the composition of the seawater and potential biosignatures. We implement data science and ML techniques to investigate what inherent information the spectra contain and determine whether a data science pipeline could be designed to quickly analyze data from future ocean worlds missions. In this study, we focus on the exploratory data analysis (EDA) step in the analytics pipeline. This is a crucial unsupervised learning step that allows us to understand the data in depth before subsequent steps such as predictive/supervised learning. EDA identifies and characterizes recurring patterns, significant correlation structure, and helps determine which variables are redundant and which contribute to significant variation in the lower dimensional space. In addition, EDA helps to identify irregularities such as outliers that might be due to poor data quality. We compared dimensionality reduction methods Uniform Manifold Approximation and Projection (UMAP) and Principal Component Analysis (PCA) for transforming our data from a high-dimensional space to a lower dimension, and we compared clustering algorithms for identifying data-driven groups (“clusters”) in the ocean worlds analog IRMS data and mapping these clusters to experimental conditions such as seawater composition and CO2 concentration. Such data analysis and characterization efforts are the first steps toward the longer-term science autonomy goal where similar automated ML tools could be used onboard a spacecraft to prioritize data transmissions for bandwidth-limited outer Solar System missions.

Data Analysis is the process that supports decision-making and informs arguments in empirical studies. Descriptive statistics, Exploratory Data Analysis (EDA), and Confirmatory Data Analysis (CDA) are the approaches that compose Data Analysis (Xia & Gong; 2014). An Exploratory Data Analysis (EDA) comprises a set of statistical and data mining procedures to describe data. We ran EDA to provide statistical facts and inform conclusions. The mined facts allow attaining arguments that would influence the Systematic Literature Review of DL4SE.

The Systematic Literature Review of DL4SE requires formal statistical modeling to refine the answers for the proposed research questions and formulate new hypotheses to be addressed in the future. Hence, we introduce DL4SE-DA, a set of statistical processes and data mining pipelines that uncover hidden relationships among Deep Learning reported literature in Software Engineering. Such hidden relationships are collected and analyzed to illustrate the state-of-the-art of DL techniques employed in the software engineering context.

Our DL4SE-DA is a simplified version of the classical Knowledge Discovery in Databases, or KDD (Fayyad, et al; 1996). The KDD process extracts knowledge from a DL4SE structured database. This structured database was the product of multiple iterations of data gathering and collection from the inspected literature. The KDD involves five stages:

Selection. This stage was led by the taxonomy process explained in section xx of the paper. After collecting all the papers and creating the taxonomies, we organize the data into 35 features or attributes that you find in the repository. In fact, we manually engineered features from the DL4SE papers. Some of the features are venue, year published, type of paper, metrics, data-scale, type of tuning, learning algorithm, SE data, and so on.

Preprocessing. The preprocessing applied was transforming the features into the correct type (nominal), removing outliers (papers that do not belong to the DL4SE), and re-inspecting the papers to extract missing information produced by the normalization process. For instance, we normalize the feature “metrics” into “MRR”, “ROC or AUC”, “BLEU Score”, “Accuracy”, “Precision”, “Recall”, “F1 Measure”, and “Other Metrics”. “Other Metrics” refers to unconventional metrics found during the extraction. Similarly, the same normalization was applied to other features like “SE Data” and “Reproducibility Types”. This separation into more detailed classes contributes to a better understanding and classification of the paper by the data mining tasks or methods.

Transformation. In this stage, we omitted to use any data transformation method except for the clustering analysis. We performed a Principal Component Analysis to reduce 35 features into 2 components for visualization purposes. Furthermore, PCA also allowed us to identify the number of clusters that exhibit the maximum reduction in variance. In other words, it helped us to identify the number of clusters to be used when tuning the explainable models.

Data Mining. In this stage, we used three distinct data mining tasks: Correlation Analysis, Association Rule Learning, and Clustering. We decided that the goal of the KDD process should be oriented to uncover hidden relationships on the extracted features (Correlations and Association Rules) and to categorize the DL4SE papers for a better segmentation of the state-of-the-art (Clustering). A clear explanation is provided in the subsection “Data Mining Tasks for the SLR od DL4SE”. 5.Interpretation/Evaluation. We used the Knowledge Discover to automatically find patterns in our papers that resemble “actionable knowledge”. This actionable knowledge was generated by conducting a reasoning process on the data mining outcomes. This reasoning process produces an argument support analysis (see this link).

We used RapidMiner as our software tool to conduct the data analysis. The procedures and pipelines were published in our repository.

Overview of the most meaningful Association Rules. Rectangles are both Premises and Conclusions. An arrow connecting a Premise with a Conclusion implies that given some premise, the conclusion is associated. E.g., Given that an author used Supervised Learning, we can conclude that their approach is irreproducible with a certain Support and Confidence.

Support = Number of occurrences this statement is true divided by the amount of statements Confidence = The support of the statement divided by the number of occurrences of the premise

Exploratory Data Analysis (EDA) Tools play a pivotal role in the modern data-driven landscape, transforming raw data into actionable insights. As businesses increasingly recognize the value of data in informing decisions, the market for EDA tools has witnessed substantial growth, driven by the rapid expansion of dat

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.

This dataset contains the analysis and questionnaire of the material collected during the workshops conducted educators to evaluate the usuability of the exploratory tool inspiraconciencia. It is part of a study by Calvera-Isabal M. (to be published).

This work has been funded by PID2020-112584RB-C33 funded by MCIN/AEI/10.13039/501100011033, the CS Track project, EU Horizon 2020 programme [grant agreement No 872522] and H2O Learn project PID2020-112584RB-C33 funded by MCIN/ AEI / 10.13039/501100011033.

The dataset accompanying the paper titled "An Exploratory Study on Build Issue Resolution Among Computer Science Students" has been provided to facilitate the reproduction of the results presented. Please note that due to local IRB restrictions, certain sensitive data may not be publicly accessible.

2022-Resolution Strategies- Build Issue Report-2022: Experimental reports of build tasks from participants- Log file and Recording-2022: Log files from participants can be found in report folders. Terminal recording using the script command can be found in script folders.- Surveys-2022: feedback survey questions- Survey and Analysis-2022.xlsx: Survey results and analysis in spreadsheet format# 2023-Intervention- Build Issue Report-2023: Experimental reports of build tasks from participants- Log file and Recording-2023: Log files from participants can be found in report folders. Terminal recording using the script command can be found in script folders.- Survey and Analysis-2023.xlsx: Survey results and analysis in spreadsheet format- Survey-2023.pdf: feedback survey questions

Exploratory data analysis.

The size of the Exploratory Testing Tool market was valued at USD XXX million in 2023 and is projected to reach USD XXX million by 2032, with an expected CAGR of XX% during the forecast period.

At least 350k posts are published on X, 510k comments are posted on Facebook, and 66k pictures and videos are shared on Instagram each minute. These large datasets require substantial processing power, even if only a percentage is collected for analysis and research. To face this challenge, data scientists can now use computer clusters deployed on various IaaS and PaaS services in the cloud. However, scientists still have to master the design of distributed algorithms and be familiar with using distributed computing programming frameworks. It is thus essential to generate tools that provide analysis methods to leverage the advantages of computer clusters for processing large amounts of social network text. This paper presents Whistlerlib, a new Python library for conducting exploratory analysis on large text datasets on social networks. Whistlerlib implements distributed versions of various social media, sentiment, and social network analysis methods that can run atop computer clusters. We experimentally demonstrate the scalability of the various Whistlerlib distributed methods when deployed on a public cloud platform. We also present a practical example of the analysis of posts on the social network X about the Mexico City subway to showcase the features of Whistlerlib in scenarios where social network analysis tools are needed to address issues with a social dimension.

Presentation Date: Sunday, January 8th, 2023 Location: Seattle, Washington, USA Abstract: A talk introducing glue software and its function with astronomy at the 2023 AAS meeting. Files included are Keynote slides (in .key and .pdf formats)

Description:

Dive into the dynamic world of the software industry with this comprehensive dataset featuring key metrics from top software companies for the years 2022 to 2023.

This dataset provides valuable insights into:

• 1. Organizations: A list of leading software companies shaping the tech landscape. Sales: Annual sales figures, showcasing the revenue generated by each company. -2.**Market Cap**: Important market capitalization data reflecting the companies' financial health and investor confidence. -3.**Headquarters**: Geographical information about where these companies are headquartered, highlighting regional influence. Harness this rich dataset to conduct exploratory data analysis (EDA), visualize trends, and uncover valuable business insights. Whether you're an analyst, researcher, or data enthusiast, this dataset is perfect for understanding the performance and positioning of key players in the software sector.

Benefits:

Comprehensive: Data covering essential metrics for informed analysis. Recent: Insights from the latest two years (2022-2023) for current market trends. User-Friendly: Organized structure for easy integration with data manipulation tools like Pandas. Take your data analysis to the next level and explore the competitive landscape of the software industry!

Unsupervised exploratory data analysis (EDA) is often the first step in understanding complex data sets. While summary statistics are among the most efficient and convenient tools for exploring and describing sets of data, they are often overlooked in EDA. In this paper, we show multiple case studies that compare the performance, including clustering, of a series of summary statistics in EDA. The summary statistics considered here are pattern recognition entropy (PRE), the mean, standard deviation (STD), 1-norm, range, sum of squares (SSQ), and X4, which are compared with principal component analysis (PCA), multivariate curve resolution (MCR), and/or cluster analysis. PRE and the other summary statistics are direct methods for analyzing datathey are not factor-based approaches. To quantify the performance of summary statistics, we use the concept of the “critical pair,” which is employed in chromatography. The data analyzed here come from different analytical methods. Hyperspectral images, including one of a biological material, are also analyzed. In general, PRE outperforms the other summary statistics, especially in image analysis, although a suite of summary statistics is useful in exploring complex data sets. While PRE results were generally comparable to those from PCA and MCR, PRE is easier to apply. For example, there is no need to determine the number of factors that describe a data set. Finally, we introduce the concept of divided spectrum-PRE (DS-PRE) as a new EDA method. DS-PRE increases the discrimination power of PRE. We also show that DS-PRE can be used to provide the inputs for the k-nearest neighbor (kNN) algorithm. We recommend PRE and DS-PRE as rapid new tools for unsupervised EDA.

The containment of the global epidemic increase of chronic diseases represents a major objective of health care systems worldwide. However, the fulfillment of this objective is complicated by the multifactorial origin of many frequent chronic diseases. Comprehensive investigations are necessary to grasp the complexity of the pathophysiological mechanisms of chronic diseases. However, this frequently results in the acquisition of complex data with numerous highly correlated variables. The statistical analysis of such complex data to identify disease associated markers is a daunting challenge. In general the application of regression methods to complex data is accompanied by problems of multiple testing and of multicollinearity. A promising approach for the survival time analysis of complex data represents the machine learning method Random Survival Forest (RSF).
Against this background, the present thesis aimed to evaluate the applicability of RSF for survival analysis of complex data in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study. A RSF backward selection algorithm was developed for the purpose of variable selection. A simulation study was then performed to evaluate the RSF method and the RSF backward algorithm. Subsequently, the RSF backward algorithm was applied to prospective observational data of the EPIC-Potsdam study to identify metabolites associated with incident T2D and to identify food groups associated with incident hypertension. The conducted simulation study confirmed the suitability of the RSF method and the implemented RSF backward algorithm as a tool for variable selection. It was demonstrated that the RSF method is able to identify predictive variables while taking into account possible confounders and can handle also the problem of multicollinearity. The subsequent application of the RSF backward algorithm to data of the EPIC-Potsdam study resulted in the successful identification of several metabolites and food groups which were associated with incident T2D and incident hypertension, respectively. Beside hexose, the metabolite diacyl-phosphatidylcholine (PC) C38:3, acyl-alkyl-PC C34:4, the amino acids valine, tyrosine, and glycine and a correlation pattern of five acyl-alkyl-PC and two diacyl-PC were associated with the incidence of T2D. Regarding the incidence of hypertension, a lunch and dinner pattern was most informative in women. In addition, a pattern reflecting dairy fat and cheese consumption and the consumption of spirits were also associated with incident hypertension in women and men. By using partial plots the direction of non-linear associations between identified variables and incident T2D and hypertension were visualised which enhanced the interpretability of the findings. In conclusion, the findings of the present thesis demonstrated that the RSF method and the implemented RSF backward algorithm represent a sensible complement to existing survival analysis methods. The RSF backward algorithm is particularly useful for exploratory analysis of complex survival data to identify unknown biomarkers associated with time until event of interest. However, the verification of the implemented RSF backward algorithm and of the present findings in external cohorts as well as the translation of the present findings for clinical diagnosis, prevention strategies and dietary recommendations should be a matter for future research.