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.

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

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.

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.

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.

Exploratory data analysis.

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

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.

One more step towards Machine learning! This is a titatic dataset with exploratory data analysis html file. I used pandas-profiling for fast analysis.

Dataset

This dataset was created by Venkatesh

Released under Database: Open Database, Contents: Database Contents

Contents

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.

Dataset

This dataset was created by Rajdeep Kaur Bajwa

Contents

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

Despite exploratory data analysis (EDA) is a powerful approach for uncovering insights from unfamiliar datasets, existing EDA tools face challenges in assisting users to assess the progress of exploration and synthesize coherent insights from isolated findings. To address these challenges, we present FactExplorer, a novel fact-based EDA system that shifts the analysis focus from raw data to data facts. FactExplorer employs a hybrid logical-visual representation, providing users with a comprehensive overview of all potential facts at the outset of their exploration. Moreover, FactExplorer introduces fact-mining techniques, including topic-based drill-down and transition path search capabilities. These features facilitate in-depth analysis of facts and enhance the understanding of interconnections between specific facts. Finally, we present a usage scenario and conduct a user study to assess the effectiveness of FactExplorer. The results indicate that FactExplorer facilitates the understanding of isolated findings and enables users to steer a thorough and effective EDA.

Dataset

This dataset was created by syamalakumar

Contents

Context

This data is publicly available on GitHub here. It can be utilized for EDA, Statistical Analysis, and Visualizations.

Content

The data set ifood_df.csv consists of 2206 customers of XYZ company with data on: - Customer profiles - Product preferences - Campaign successes/failures - Channel performance

Acknowledgement

I do not own this dataset. I am simply making it accessible on this platform via the public GitHub link.

Sensor technologies allow ethologists to continuously monitor the behaviors of large numbers of animals over extended periods of time. This creates new opportunities to study livestock behavior in commercial settings, but also new methodological challenges. Densely sampled behavioral data from large heterogeneous groups can contain a range of complex patterns and stochastic structures that may be difficult to visualize using conventional exploratory data analysis techniques. The goal of this research was to assess the efficacy of unsupervised machine learning tools in recovering complex behavioral patterns from such datasets to better inform subsequent statistical modeling. This methodological case study was carried out using records on milking order, or the sequence in which cows arrange themselves as they enter the milking parlor. Data was collected over a 6-month period from a closed group of 200 mixed-parity Holstein cattle on an organic dairy. Cows at the front and rear of the queue proved more consistent in their entry position than animals at the center of the queue, a systematic pattern of heterogeneity more clearly visualized using entropy estimates, a scale and distribution-free alternative to variance robust to outliers. Dimension reduction techniques were then used to visualize relationships between cows. No evidence of social cohesion was recovered, but Diffusion Map embeddings proved more adept than PCA at revealing the underlying linear geometry of this data. Median parlor entry positions from the pre- and post-pasture subperiods were highly correlated (R = 0.91), suggesting a surprising degree of temporal stationarity. Data Mechanics visualizations, however, revealed heterogeneous non-stationary among subgroups of animals in the center of the group and herd-level temporal outliers. A repeated measures model recovered inconsistent evidence of a relationships between entry position and cow attributes. Mutual conditional entropy tests, a permutation-based approach to assessing bivariate correlations robust to non-independence, confirmed a significant but non-linear association with peak milk yield, but revealed the age effect to be potentially confounded by health status. Finally, queueing records were related back to behaviors recorded via ear tag accelerometers using linear models and mutual conditional entropy tests. Both approaches recovered consistent evidence of differences in home pen behaviors across subsections of the queue.

The data was collected from the famous cookery Youtube channels in India. The major focus was to collect the viewers' comments in Hinglish languages. The datasets are taken from top 2 Indian cooking channel named Nisha Madhulika channel and Kabita’s Kitchen channel.

Both the datasets comments are divided into seven categories:-

Label 1- Gratitude

Label 2- About the recipe

Label 3- About the video

Label 4- Praising

Label 5- Hybrid

Label 6- Undefined

Label 7- Suggestions and queries

All the labelling has been done manually.

Nisha Madhulika dataset:

Dataset characteristics: Multivariate

Number of instances: 4900

Area: Cooking

Attribute characteristics: Real

Number of attributes: 3

Date donated: March, 2019

Associate tasks: Classification

Missing values: Null

Kabita Kitchen dataset:

Dataset characteristics: Multivariate

Number of instances: 4900

Area: Cooking

Attribute characteristics: Real

Number of attributes: 3

Date donated: March, 2019

Associate tasks: Classification

Missing values: Null

There are two separate datasets file of each channel named as preprocessing and main file .

The files with preprocessing names are generated after doing the preprocessing and exploratory data analysis on both the datasets. This file includes:

• Id
• Comment text
• Labels

• Count of stop-words
• Uppercase words
• Hashtags
• Word count
• Char count
• Average words
• Numeric

The main file includes:

• Id
• comment text
• Labels

Please cite the paper

https://www.mdpi.com/2504-2289/3/3/37

MDPI and ACS Style

Kaur, G.; Kaushik, A.; Sharma, S. Cooking Is Creating Emotion: A Study on Hinglish Sentiments of Youtube Cookery Channels Using Semi-Supervised Approach. Big Data Cogn. Comput. 2019, 3, 37.

This exploratory data analysis gives insights from Facebook dataset which consists of identifying users that can be focused more to increase the business. These valuable insights should help Facebook to take intelligent decision to identify its useful users and provide correct recommendations to them.

Context There are fewer public datasets available from Google Play Store apps, and making a Web scraper to scrape as much data of the scraped practice good sense at that time. On closer look I found out the structure is a wonderful appendix like index which makes it terribly easy for bolt web scraping from iTunes App Store page. However, Google Play Store uses more advanced modern-day techniques (e.g. dynamic page load) powered by JQuery which makes scraping harder of course.

Content

The column values for each app (i.e. category, rating, size and so on)

Acknowledgements All of the above information is now scrapped from Google Play Store. It is with help of this only that this app information could have been made available,

Inspiration Data from the Play Store apps data can be a powerful force for creating applications.