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 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.

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!

The global market for data lens (visualizations of data) is experiencing robust growth, driven by the increasing adoption of data analytics across diverse industries. This market, estimated at $50 billion in 2025, is projected to achieve a compound annual growth rate (CAGR) of 15% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the rising volume and complexity of data necessitate effective visualization tools for insightful analysis. Businesses are increasingly relying on interactive dashboards and data storytelling techniques to derive actionable intelligence from their data, fostering the demand for sophisticated data visualization solutions. Secondly, advancements in artificial intelligence (AI) and machine learning (ML) are enhancing the capabilities of data visualization platforms, enabling automated insights generation and predictive analytics. This creates new opportunities for vendors to offer more advanced and user-friendly tools. Finally, the growing adoption of cloud-based solutions is further accelerating market growth, offering enhanced scalability, accessibility, and cost-effectiveness. The market is segmented across various types, including points, lines, and bars, and applications, ranging from exploratory data analysis and interactive data visualization to descriptive statistics and advanced data science techniques. Major players like Tableau, Sisense, and Microsoft dominate the market, constantly innovating to meet evolving customer needs and competitive pressures. The geographical distribution of the market reveals strong growth across North America and Europe, driven by early adoption and technological advancements. However, emerging markets in Asia-Pacific and the Middle East & Africa are showing significant growth potential, fueled by increasing digitalization and investment in data analytics infrastructure. Restraints to growth include the high cost of implementation, the need for skilled professionals to effectively utilize these tools, and security concerns related to data privacy. Nonetheless, the overall market outlook remains positive, with continued expansion anticipated throughout the forecast period due to the fundamental importance of data visualization in informed decision-making across all sectors.

The Exploratory Data Analysis (EDA) tools market is experiencing robust growth, driven by the increasing volume and complexity of data across industries. The rising need for data-driven decision-making, coupled with the expanding adoption of cloud-based analytics solutions, is fueling market expansion. While precise figures for market size and CAGR are not provided, a reasonable estimation, based on the prevalent growth in the broader analytics market and the crucial role of EDA in the data science workflow, would place the 2025 market size at approximately $3 billion, with a projected Compound Annual Growth Rate (CAGR) of 15% through 2033. This growth is segmented across various applications, with large enterprises leading the adoption due to their higher investment capacity and complex data needs. However, SMEs are witnessing rapid growth in EDA tool adoption, driven by the increasing availability of user-friendly and cost-effective solutions. Further segmentation by tool type reveals a strong preference for graphical EDA tools, which offer intuitive visualizations facilitating better data understanding and communication of findings. Geographic regions, such as North America and Europe, currently hold a significant market share, but the Asia-Pacific region shows promising potential for future growth owing to increasing digitalization and data generation. Key restraints to market growth include the need for specialized skills to effectively utilize these tools and the potential for data bias if not handled appropriately. The competitive landscape is dynamic, with both established players like IBM and emerging companies specializing in niche areas vying for market share. Established players benefit from brand recognition and comprehensive enterprise solutions, while specialized vendors provide innovative features and agile development cycles. Open-source options like KNIME and R packages (Rattle, Pandas Profiling) offer cost-effective alternatives, particularly attracting academic institutions and smaller businesses. The ongoing development of advanced analytics functionalities, such as automated machine learning integration within EDA platforms, will be a significant driver of future market growth. Further, the integration of EDA tools within broader data science platforms is streamlining the overall analytical workflow, contributing to increased adoption and reduced complexity. The market's evolution hinges on enhanced user experience, more robust automation features, and seamless integration with other data management and analytics tools.

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.

This dataset contains YouTube trending video statistics for various Mediterranean countries. Its primary purpose is to provide insights into popular video content, channels, and viewer engagement across the region over specific periods. It is valuable for analysing content trends, understanding regional audience preferences, and assessing video performance metrics on the YouTube platform.

Columns

• country: The nation where the video was published.
• video_id: A unique identification number assigned to each video.
• title: The name of the video.
• publishedAt: The publication date of the video.
• channelId: The unique identification number for the channel that published the video.
• channelTitle: The name of the channel that published the video.
• categoryId: The category identification number of the video (e.g., '10' for 'music').
• trending_date: The date on which the video was observed to be trending.
• tags: Keywords or phrases associated with the video.
• view_count: The total number of views the video has accumulated.
• comment_count: The total number of comments received on the video.
• thumbnail_link: The URL for the image displayed before the video is played.
• comments_disabled: A boolean indicator showing if comments are disabled for the video.
• ratings_disabled: A boolean indicator showing if ratings (likes/dislikes) are disabled for the video.
• description: The explanatory text provided below the video.

Distribution

The dataset is structured in a tabular format, typically provided as a CSV file. It consists of 15 distinct columns detailing various aspects of YouTube trending videos. While the exact total number of rows or records is not specified, the data includes trending video counts for several date ranges in 2022: * 06/04/2022 - 06/08/2022: 31 records * 06/08/2022 - 06/11/2022: 56 records * 06/11/2022 - 06/15/2022: 57 records * 06/15/2022 - 06/19/2022: 111 records * 06/19/2022 - 06/22/2022: 130 records * 06/22/2022 - 06/26/2022: 207 records * 06/26/2022 - 06/29/2022: 321 records * 06/29/2022 - 07/03/2022: 523 records * 07/03/2022 - 07/07/2022: 924 records * 07/07/2022 - 07/10/2022: 861 records The dataset features 19 unique countries and 1347 unique video IDs. View counts for videos in the dataset range from approximately 20.9 thousand to 123 million.

Usage

This dataset is well-suited for a variety of analytical applications and use cases: * Exploratory Data Analysis (EDA): Discovering patterns, anomalies, and relationships within YouTube trending content. * Data Manipulation and Querying: Practising data handling using libraries such as Pandas or Numpy in Python, or executing queries with SQL. * Natural Language Processing (NLP): Analysing video titles, tags, and descriptions to extract key themes, sentiment, and trending topics. * Trend Prediction: Developing models to forecast future trending videos or content categories. * Cross-Country Comparison: Examining how trending content varies across different Mediterranean nations.

Coverage

• Geographic Scope: The dataset covers YouTube trending video statistics for 19 specific Mediterranean countries. These include Italy (IT), Spain (ES), Greece (GR), Croatia (HR), Turkey (TR), Albania (AL), Algeria (DZ), Egypt (EG), Libya (LY), Tunisia (TN), Morocco (MA), Israel (IL), Montenegro (ME), Lebanon (LB), France (FR), Bosnia and Herzegovina (BA), Malta (MT), Slovenia (SI), Cyprus (CY), and Syria (SY).
• Time Range: The data primarily spans from 2022-06-04 to 2022-07-10, providing detailed daily trending information. A specific snapshot of the dataset is also available for 2022-11-07.

License

CC0

Who Can Use It

• Data Scientists and Analysts: For conducting in-depth research, building predictive models, and generating insights on social media trends.
• Researchers: Those studying online content consumption patterns, regional cultural influences, and digital media behaviour.
• Marketing Professionals: To identify popular content types, inform content strategy, and understand audience engagement on YouTube.
• Students: For academic projects focusing on web data analysis, natural language processing, and statistical modelling.

Dataset Name Suggestions

• Mediterranean YouTube Trends 2022
• YouTube Trending Videos: Mediterranean Insights
• Regional YouTube Viral Content
• Mediterranean Social Media Video Data
• YouTube Trends in Southern Europe & North Africa

Attributes

Original Data Source: YouTube Trending Videos of the Day

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

This dataset offers insights into job postings, primarily focusing on roles in Data Engineering, Data Analysis, Data Science, and Machine Learning Engineering. It contains approximately 1583 records of job information, providing a snapshot of the employment landscape in these fields. The dataset is ideal for understanding market demands and trends.

Columns

• job_title: The specific title of the job post.
• company: The name of the hiring company.
• job_location: The city and state where the job is located.
• job_summary: A detailed description outlining the purpose of the hiring.
• post_date: The date when the job was posted on Indeed.
• today: The date when the data was collected.
• job_salary: The expected salary range for the position.
• job_url: A direct link to the job posting for further details.

Distribution

The dataset is provided as a single CSV file, named 'job_dataset.csv'. It comprises 1583 rows and 8 columns, representing the structure of the collected job information. The data collection occurred around 26th July 2022.

Usage

This dataset is well-suited for various analytical tasks: * Cleaning and refining job data. * Identifying the most in-demand skills within the data and machine learning sectors. * Analysing the geographical distribution of jobs. * Conducting Natural Language Processing (NLP) and research on job descriptions. * Market analysis for job seekers, recruiters, and educational institutions.

Coverage

The dataset has a global scope, with notable concentrations of job postings in locations such as Bengaluru, Karnataka (30%) and Gurgaon, Haryana (7%). The records primarily cover job postings for data-related roles, including Data Engineer, Data Analyst, Data Scientist, and ML Engineer, with data collected around July 2022. Some postings were listed over 30 days prior to the collection date.

License

CC0

Who Can Use It

This dataset is valuable for: * Data Scientists and Analysts: For market research, trend analysis, and skill demand assessment. * Machine Learning Engineers: To understand job requirements and role distributions. * Researchers: For academic studies on labour markets and skill development. * Job Seekers: To identify popular roles, required skills, and geographical opportunities. * Companies and Recruiters: For talent acquisition strategies and competitor analysis.

Dataset Name Suggestions

• Indeed Data Science & ML Job Postings
• Global Data Roles Dataset
• Job Market Insights: Data Careers
• Data Analytics & AI Job Data
• UK Data Professional Vacancies

Attributes

Original Data Source: Indeed job (Data science /data analyst/ ML)

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.

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

Data from Game of Thrones series

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

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.

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

Abstract

Are you looking for a career transition to data science? Looking for a job in data science? This dataset can help you.😃

Content

Using this data one can try to find out the skills and trends that are most sought in the industry right now for data scientist. This whole data consists of information related to only Data science jobs in India. The data have been gathered from the top job hunt website in India- > Naukri.com, which almost every job aspirant uses these days. Selenium-python is been used for web scraping. The scrapped data consists of these 5 important features(columns): - Job Roles. - Company name. - Experience required. - Location. - Key Skills

Answers that you can find here:

• What are the top skills companies are looking for?
• What is the most desired experience level in the industry?
• What are the companies that are actively offering jobs in this field?
• What are the locations that have more openings?

Dataset

This dataset was created by Akalya Subramanian

Contents

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.

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

This dataset provides customer reviews for PIA Experience, gathered through web scraping from airlinequality.com. It is specifically designed for data science and analytics applications, offering valuable insights into customer sentiment and feedback. The data is suitable for various analytical tasks, including modelling, predictive analysis, feature engineering, and exploratory data analysis (EDA). Users should note that the data requires an initial cleaning phase due to the presence of null values.

Columns

• reviews: Contains individual customer feedback entries pertaining to their experience with PIA. This column features approximately 160 distinct review entries.

Distribution

The dataset is provided as a CSV file. While the 'reviews' column contains 160 unique values, the exact total number of rows or records in the dataset is not explicitly detailed. It is structured in a tabular format, making it straightforward for data processing.

Usage

This dataset is ideally suited for a variety of applications, including: * Modelling * Predictive analysis * Feature engineering * Exploratory Data Analysis (EDA) * Natural Language Processing (NLP) tasks, such as sentiment analysis or topic modelling.

Coverage

The dataset's focus is primarily on customer reviews from the Asia region. It was listed on 17 June 2025, and the content relates specifically to the experiences of customers using PIA.

License

CC0

Who Can Use It

This dataset is beneficial for a range of users, including: * Data scientists looking to develop predictive models or perform advanced feature engineering. * Data analysts interested in conducting exploratory data analysis to uncover trends and patterns. * Researchers studying customer satisfaction, service quality, or airline industry performance. * Developers working on natural language processing solutions, particularly those focused on text analytics from customer feedback.

Dataset Name Suggestions

• PIA Customer Feedback
• PIA Experience Reviews
• Airline Customer Sentiment - PIA
• PIA Passenger Reviews
• PIA Service Review Data

Attributes

Original Data Source: PIA Customer Reviews

This dataset focuses on Twitter engagement metrics related to the Coronavirus disease (COVID-19), an infectious disease caused by the SARS-CoV-2 virus [1]. It provides a detailed collection of tweets, including their text content, the accounts that posted them, any hashtags used, and the geographical locations associated with the accounts [1]. The dataset is valuable for understanding public discourse, information dissemination, and engagement patterns on Twitter concerning COVID-19, particularly for analysing how people experience mild to moderate symptoms and recover, or require medical attention [1].

Columns

• Datetime: Represents the exact date and time a tweet was posted [2].
• Tweet Id: A unique identifier assigned to each tweet [2].
• Text: The actual content of the tweet [2].
• Username: The display name of the tweet author [2].
• Permalink: The direct link to the tweet on Twitter [2].
• User: A link to the author's Twitter account [2].
• Outlinks: Any external links included within the tweet [2].
• CountLinks: The number of links present in the tweet [2].
• ReplyCount: The total number of replies to that specific tweet [2].
• RetweetCount: The total number of retweets of that specific tweet [2].
• DateTime Count: A daily count of tweets, aggregated by date ranges [2].
• Label Count: A count associated with specific ranges of tweet IDs or other engagement metrics, indicating the distribution of tweets within those ranges [3-5].

Distribution

The dataset is structured with daily tweet counts and covers a period from 10 January 2020 to 28 February 2020 [2, 6, 7]. It includes approximately 179,040 daily tweet entries during this timeframe, derived from the sum of daily counts and tweet ID counts [2, 3, 6-11]. Tweet activity shows distinct peaks, with notable increases in late January (e.g., 6,091 tweets between 23-24 January 2020) [2] and a significant surge in late February, reaching 47,643 tweets between 26-27 February 2020, followed by 42,289 and 44,824 in subsequent days [7, 10, 11]. The distribution of certain tweet engagement metrics, such as replies or retweets, indicates that a substantial majority of tweets (over 152,500 records) fall within lower engagement ranges (e.g., 0-43 or 0-1628.96), with fewer tweets showing very high engagement (e.g., only 1 record between 79819.04-81448.00) [4, 5]. The data file would typically be in CSV format [12].

Usage

This dataset is ideal for: * Data Science and Analytics projects focused on social media [1]. * Visualization of tweet trends and engagement over time. * Exploratory data analysis to uncover patterns in COVID-19 related discussions [1]. * Natural Language Processing (NLP) tasks, such as sentiment analysis or topic modelling on tweet content [1]. * Data cleaning and preparation exercises for social media data [1].

Coverage

The dataset has a global geographic scope [13]. It covers tweet data from 10 January 2020 to 28 February 2020 [2, 6, 7]. The content is specific to the Coronavirus disease (COVID-19) [1].

License

CC0

Who Can Use It

This dataset is particularly useful for: * Data scientists and analysts interested in social media trends and public health discourse [1]. * Researchers studying information spread and public sentiment during health crises. * Developers building AI and LLM data solutions [13]. * Individuals interested in exploratory analysis and data visualization of real-world social media data [1].

Dataset Name Suggestions

• COVID-19 Twitter Engagement Data
• SARS-CoV-2 Tweet Activity Log
• Pandemic Social Media Discourse
• Coronavirus Tweets Analytics
• Global COVID-19 Tweet Metrics

Attributes

Original Data Source: Covid_19 Tweets Dataset

This dataset provides valuable insights into the US data science job market, containing detailed job listings scraped from the Indeed web portal on 20th November 2022. It is ideal for those seeking to understand job trends, analyse salary expectations, or develop skills in data analysis, machine learning, and natural language processing. The dataset's purpose is to offer a snapshot of available positions across various data science roles, including data scientists, machine learning engineers, and business analysts. It serves as a rich resource for exploratory data analysis, feature engineering, and predictive modelling tasks.

Columns

• Title: The job title of the listed position.
• Company: The hiring company posting the job.
• Location: The geographic location of the job within the US.
• Rating: The rating associated with the job or company.
• Date: Indicates how long the job had been posted prior to 20th November 2022.
• Salary: The salary information provided in US Dollars ($). Please note that many entries in this column may be missing as salary details are often not disclosed in job listings.
• Description: A brief summary description of the job.
• Links: The direct link to the original job posting on the Indeed platform.
• Descriptions: The full-length description of the job, encompassing all details found in the complete job posting.

Distribution

This dataset is provided as a single data file, typically in CSV format. It comprises 1200 rows (records) and 9 distinct columns. The file name is data_science_jobs_indeed_us.csv.

Usage

This dataset is perfectly suited for a variety of analytical tasks and applications: * Data Cleaning and Preparation: Practise handling missing values, especially in the 'Salary' column. * Exploratory Data Analysis (EDA): Discover trends in job titles, company types, and locations. * Feature Engineering: Extract new features from the 'Descriptions' column, such as required skills, education levels, or experience. * Classification and Clustering: Develop models for salary prediction, or perform skill clustering analysis to guide curriculum development. * Text Processing and Natural Language Processing (NLP): Analyse job descriptions to identify common skill demands or industry buzzwords.

Coverage

The dataset's geographic scope is limited to job postings within the United States. All data was collected on 20th November 2022, with the 'Date' column providing information on how long each job had been active before this date. The dataset covers a wide range of data science positions, including roles such as data scientist, machine learning engineer, data engineer, business analyst, and data science manager. It is important to note the presence of many missing entries in the 'Salary' column, reflecting common data availability challenges in job listings.

License

CCO

Who Can Use It

This dataset is an excellent resource for: * Aspiring Data Scientists and Machine Learning Engineers: To sharpen their data cleaning, EDA, and model deployment skills. * Educators and Curriculum Developers: To inform and guide the development of relevant data science and analytics courses based on real-world job market demands. * Job Seekers: To understand the current landscape of data science roles, required skills, and potential salary ranges. * Researchers and Analysts: To glean insights into labour market trends in the data science domain. * Human Resources Professionals: To benchmark job roles, skill requirements, and compensation within the industry.

Dataset Name Suggestions

• Indeed US Data Science Job Insights
• US Data Science Job Market Analysis
• Data Professional Job Postings (Indeed USA)
• Data Science Career Opportunities (USA)

Attributes

Original Data Source: Data Science Job Postings (Indeed USA)