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.

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.

Dataset

This dataset was created by Monis Ahmad

Contents

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.

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.

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

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.

Airbnb® is an American company operating an online marketplace for lodging, primarily for vacation rentals. The purpose of this study is to perform an exploratory data analysis of the two datasets containing Airbnb® listings and across 10 major cities. We aim to use various data visualizations to gain valuable insight on the effects of pricing, covid, and more!

Content

This is a transactions data from an Electronics store chain in the US. The data contains 12 CSV files for each month of 2019. The naming convention is as follows: Sales_[MONTH_NAME]_2019 Each file contains anywhere from around 9000 to 26000 rows and 6 columns. The columns are as follows: Order ID, Product, Quantity Ordered, Price Each, Order Date, Purchase Address There are around 186851 data points combining all the 12-month files. There may be null values in some rows.

Inspiration

Keith Galli

Acknowledgements

• Keith Galli's Youtube video - Solving real world data science tasks with Python Pandas!
• Keith Galli's GitHub Repo - Pandas-Data-Science-Tasks

Dataset

This dataset was created by Saubhagya Mishra

Released under MIT

Contents

Question Paper Solutions of chapter Exploratory Data Analytics and Descriptive Statistics of Data Analytics Skills for Managers, 5th Semester , Bachelor in Business Administration 2020 - 2021

Exploratory Data Analysis for the Physical Properties of Lakes

This lesson was adapted from educational material written by Dr. Kateri Salk for her Fall 2019 Hydrologic Data Analysis course at Duke University. This is the first part of a two-part exercise focusing on the physical properties of lakes.

Introduction

Lakes are dynamic, nonuniform bodies of water in which the physical, biological, and chemical properties interact. Lakes also contain the majority of Earth's fresh water supply. This lesson introduces exploratory data analysis using R statistical software in the context of the physical properties of lakes.

Learning Objectives

After successfully completing this exercise, you will be able to:

1. Apply exploratory data analytics skills to applied questions about physical properties of lakes
2. Communicate findings with peers through oral, visual, and written modes

This short activity can be used to introduce the concept of exploratory data analysis and get participants to think about how this data science strategy is complementary to having students interpret graphs.

This dataset contains clinical and diagnostic features related to Breast Cancer, designed for comprehensive Exploratory Data Analysis (EDA) and subsequent predictive modeling.

It is derived from digitized images of Fine Needle Aspirates (FNA) of breast masses.

The dataset features quantitative measurements, typically calculated from the characteristics of cell nuclei, including: - Radius - Texture - Perimeter - Area - Smoothness - Compactness - Concavity - Concave Points - Symmetry - Fractal Dimension

These features are provided as mean, standard error, and "worst" (largest) values.

The primary goal of this resource is to support the validation of EDA techniques necessary for clinical data science: - Data quality assessment (missing values, inconsistencies). - Feature assessment (distributions, correlations). - Visualization for diagnostic modeling.

The primary target variable is the binary classification of the tissue sample: Malignant vs. Benign.

Identifying and dealing with outliers is an important part of data analysis. A new visualization, the O3 plot, is introduced to aid in the display and understanding of patterns of multivariate outliers. It uses the results of identifying outliers for every possible combination of dataset variables to provide insight into why particular cases are outliers. The O3 plot can be used to compare the results from up to six different outlier identification methods. There is anRpackage OutliersO3 implementing the plot. The article is illustrated with outlier analyses of German demographic and economic data. Supplementary materials for this article are available online.

Recent calls to take up data science either revolve around the superior predictive performance associated with machine learning or the potential of data science techniques for exploratory data analysis. Many believe that these strengths come at the cost of explanatory insights, which form the basis for theorization. In this paper, we show that this trade-off is false. When used as a part of a full research process, including inductive, deductive and abductive steps, machine learning can offer explanatory insights and provide a solid basis for theorization. We present a systematic five-step theory-building and theory-testing cycle that consists of: 1. Element identification (reduction); 2. Exploratory analysis (induction); 3. Hypothesis development (retroduction); 4. Hypothesis testing (deduction); and 5. Theorization (abduction). We demonstrate the usefulness of this approach, which we refer to as co-duction, in a vignette where we study firm growth with real-world observational data.

Recent calls to take up data science either revolve around the superior predictive performance associated with machine learning or the potential of data science techniques for exploratory data analysis. Many believe that these strengths come at the cost of explanatory insights, which form the basis for theorization. In this paper, we show that this trade-off is false. When used as a part of a full research process, including inductive, deductive and abductive steps, machine learning can offer explanatory insights and provide a solid basis for theorization. We present a systematic five-step theory-building and theory-testing cycle that consists of: 1. Element identification (reduction); 2. Exploratory analysis (induction); 3. Hypothesis development (retroduction); 4. Hypothesis testing (deduction); and 5. Theorization (abduction). We demonstrate the usefulness of this approach, which we refer to as co-duction, in a vignette where we study firm growth with real-world observational data.

Recent calls to take up data science either revolve around the superior predictive performance associated with machine learning or the potential of data science techniques for exploratory data analysis. Many believe that these strengths come at the cost of explanatory insights, which form the basis for theorization. In this paper, we show that this trade-off is false. When used as a part of a full research process, including inductive, deductive and abductive steps, machine learning can offer explanatory insights and provide a solid basis for theorization. We present a systematic five-step theory-building and theory-testing cycle that consists of: 1. Element identification (reduction); 2. Exploratory analysis (induction); 3. Hypothesis development (retroduction); 4. Hypothesis testing (deduction); and 5. Theorization (abduction). We demonstrate the usefulness of this approach, which we refer to as co-duction, in a vignette where we study firm growth with real-world observational data.

Feature contributions and top-three feature interactions (MFIs).