Data exploration, also known as exploratory data analysis, provides a set of simple tools to achieve a basic understanding of a dataset. The results of data exploration can be extremely useful in grasping the structure of the data, the distribution of the values, presence of extreme values, and interrelationships within the dataset. Descriptive statistics is the process of condensing key characteristics of the dataset into simple numeric metrics. Some of the common metrics used are mean, standard deviation, and correlation. Visualization is the process of projecting the data, or parts of it, into Cartesian space or into abstract images. In the data science process, data exploration is leveraged in many different steps including preprocessing, modeling, and interpretation of the results.

Cited by 8Open access

Data Exploration

Jalil Villalobos Alva · Apress eBooks · 2024

This chapter looks at the basics of data management through the Wolfram Data Repository online platform and its use in Mathematica. You also learn how data is viewed inside datasets and how to apply user functions and commands.

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Data exploration

Noam Shoresh, Bang Wong · Nature Methods · 2011

Cited by 35Open access

Data exploration.

Lauren Ali (8953682), Elisabeth Grey (8953685), Delezia Singh (8953688) et al. · Figshare · 2020

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Data Exploration

Richard Huntsinger · Cambridge University Press eBooks · 2025

Business analytics is all about leveraging data analysis and analytical modeling methods to achieve business objectives. This is the book for upper division and graduate business students with interest in data science, for data science students with interest in business, and for everyone with interest in both. A comprehensive collection of over 50 methods and cases is presented in an intuitive style, generously illustrated, and backed up by an approachable level of mathematical rigor appropriate to a range of proficiency levels. A robust set of online resources, including software tools, coding examples, datasets, primers, exercise banks, and more for both students and instructors, makes the book the ideal learning resource for aspiring data-savvy business practitioners.

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Data Exploration

Carlo Vercellis · 2009

This chapter contains sections titled: Univariate analysis Bivariate analysis Multivariate analysis Notes and readings

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Data exploration.

Jularat Chumnaul (17537898), Mohammad Sepehrifar (18966564) · Figshare · 2024

<div><p>Data analysis can be accurate and reliable only if the underlying assumptions of the used statistical method are validated. Any violations of these assumptions can change the outcomes and conclusions of the analysis. In this study, we developed Smart Data Analysis V2 (SDA-V2), an interactive and user-friendly web application, to assist users with limited statistical knowledge in data analysis, and it can be freely accessed at <a href="https://jularatchumnaul.shinyapps.io/SDA-V2/" target="_blank">https://jularatchumnaul.shinyapps.io/SDA-V2/</a>. SDA-V2 automatically explores and visualizes data, examines the underlying assumptions associated with the parametric test, and selects an appropriate statistical method for the given data. Furthermore, SDA-V2 can assess the quality of research instruments and determine the minimum sample size required for a meaningful study. However, while SDA-V2 is a valuable tool for simplifying statistical analysis, it does not replace the need for a fundamental understanding of statistical principles. Researchers are encouraged to combine their expertise with the software’s capabilities to achieve the most accurate and credible results.</p></div>

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Data exploration

Sengottuvel Kuppuraj · OSF Preprints (OSF Preprints) · 2017

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Data Exploration

Patrick Ryan · OSF Preprints (OSF Preprints) · 2017

Identify and prepare data sources for analysis and implementation

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Data Exploration

Yangchang Zhao · Elsevier eBooks · 2012

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Ecological Data Exploration

Mohamed Elhadi Rahmani, Abdelmalek Amine · Advances in computational intelligence and robotics book series · 2018

Computer modeling of ecological systems is the activity of implementing computer solutions to analyze data related to the fields of remote sensing, earth science, biology, and oceans. The ecologists analyze the data to identify the relationships between a response and a set of predictors, using statistical models that do not accurately describe the main sources of variation in the response variable. Knowledge discovery techniques are often more powerful, flexible, and effective for exploratory analysis than statistical techniques. This chapter aims to test the use of data mining in ecology. It will discuss the exploration of ecological data by defining at first data mining, its advantages, and its different types. Then the authors detail the field of bio-inspiration and meta-heuristics. And finally, they give case studies from where they applied these two areas to explore ecological data.

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