89 datasets found
  1. A Journey through Data Cleaning

    • kaggle.com
    zip
    Updated Mar 22, 2024
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    kenanyafi (2024). A Journey through Data Cleaning [Dataset]. https://www.kaggle.com/datasets/kenanyafi/a-journey-through-data-cleaning
    Explore at:
    zip(0 bytes)Available download formats
    Dataset updated
    Mar 22, 2024
    Authors
    kenanyafi
    Description

    Embark on a transformative journey with our Data Cleaning Project, where we meticulously refine and polish raw data into valuable insights. Our project focuses on streamlining data sets, removing inconsistencies, and ensuring accuracy to unlock its full potential.

    Through advanced techniques and rigorous processes, we standardize formats, address missing values, and eliminate duplicates, creating a clean and reliable foundation for analysis. By enhancing data quality, we empower organizations to make informed decisions, drive innovation, and achieve strategic objectives with confidence.

    Join us as we embark on this essential phase of data preparation, paving the way for more accurate and actionable insights that fuel success."

  2. d

    B2B Data Cleansing Services - Verified Records - Updated Every 30 Days

    • datarade.ai
    Updated Nov 16, 2024
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    Thomson Data (2024). B2B Data Cleansing Services - Verified Records - Updated Every 30 Days [Dataset]. https://datarade.ai/data-products/thomson-data-hr-data-reach-hr-professionals-across-the-world-thomson-data
    Explore at:
    .csv, .xls, .sql, .txtAvailable download formats
    Dataset updated
    Nov 16, 2024
    Dataset authored and provided by
    Thomson Data
    Area covered
    Eritrea, Finland, Panama, Palau, Czech Republic, Zimbabwe, Denmark, Micronesia (Federated States of), Andorra, Bulgaria
    Description

    At Thomson Data, we help businesses clean up and manage messy B2B databases to ensure they are up-to-date, correct, and detailed. We believe your sales development representatives and marketing representatives should focus on building meaningful relationships with prospects, not scrubbing through bad data.

    Here are the key steps involved in our B2B data cleansing process:

    1. Data Auditing: We begin with a thorough audit of the database to identify errors, gaps, and inconsistencies, which majorly revolve around identifying outdated, incomplete, and duplicate information.

    2. Data Standardization: Ensuring consistency in the data records is one of our prime services; it includes standardizing job titles, addresses, and company names. It ensures that they can be easily shared and used by different teams.

    3. Data Deduplication: Another way we improve efficiency is by removing all duplicate records. Data deduplication is important in a large B2B dataset as multiple records from the same company may exist in the database.

    4. Data Enrichment: After the first three steps, we enrich your data, fill in the missing details, and then enhance the database with up-to-date records. This is the step that ensures the database is valuable, providing insights that are actionable and complete.

    What are the Key Benefits of Keeping the Data Clean with Thomson Data’s B2B Data Cleansing Service? Once you understand the benefits of our data cleansing service, it will entice you to optimize your data management practices, and it will additionally help you stay competitive in today’s data-driven market.

    Here are some advantages of maintaining a clean database with Thomson Data:

    1. Better ROI for your Sales and Marketing Campaigns: Our clean data will magnify your precise targeting, enabling you to strategize for effective campaigns, increased conversion rate, and ROI.

    2. Compliant with Data Regulations:
      The B2B data cleansing services we provide are compliant to global data norms.

    3. Streamline Operations: Your efforts are directed in the right channel when your data is clean and accurate, as your team doesn’t have to spend their valuable time fixing errors.

    To summarize, we would again bring your attention to how accurate data is essential for driving sales and marketing in a B2B environment. It enhances your business prowess in the avenues of decision-making and customer relationships. Therefore, it is better to have a proactive approach toward B2B data cleansing service and outsource our offerings to stay competitive by unlocking the full potential of your data.

    Send us a request and we will be happy to assist you.

  3. o

    Messy data for data cleaning exercise - Dataset - openAFRICA

    • open.africa
    Updated Oct 6, 2021
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    (2021). Messy data for data cleaning exercise - Dataset - openAFRICA [Dataset]. https://open.africa/dataset/messy-data-for-data-cleaning-exercise
    Explore at:
    Dataset updated
    Oct 6, 2021
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    A messy data for demonstrating "how to clean data using spreadsheet". This dataset was intentionally formatted to be messy, for the purpose of demonstration. It was collated from here - https://openafrica.net/dataset/historic-and-projected-rainfall-and-runoff-for-4-lake-victoria-sub-regions

  4. Data Cleaning Tools Market Report | Global Forecast From 2025 To 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Jan 7, 2025
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    Dataintelo (2025). Data Cleaning Tools Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/data-cleaning-tools-market
    Explore at:
    pptx, pdf, csvAvailable download formats
    Dataset updated
    Jan 7, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Data Cleaning Tools Market Outlook



    As of 2023, the global market size for data cleaning tools is estimated at $2.5 billion, with projections indicating that it will reach approximately $7.1 billion by 2032, reflecting a robust CAGR of 12.1% during the forecast period. This growth is primarily driven by the increasing importance of data quality in business intelligence and analytics workflows across various industries.



    The growth of the data cleaning tools market can be attributed to several critical factors. Firstly, the exponential increase in data generation across industries necessitates efficient tools to manage data quality. Poor data quality can result in significant financial losses, inefficient business processes, and faulty decision-making. Organizations recognize the value of clean, accurate data in driving business insights and operational efficiency, thereby propelling the adoption of data cleaning tools. Additionally, regulatory requirements and compliance standards also push companies to maintain high data quality standards, further driving market growth.



    Another significant growth factor is the rising adoption of AI and machine learning technologies. These advanced technologies rely heavily on high-quality data to deliver accurate results. Data cleaning tools play a crucial role in preparing datasets for AI and machine learning models, ensuring that the data is free from errors, inconsistencies, and redundancies. This surge in the use of AI and machine learning across various sectors like healthcare, finance, and retail is driving the demand for efficient data cleaning solutions.



    The proliferation of big data analytics is another critical factor contributing to market growth. Big data analytics enables organizations to uncover hidden patterns, correlations, and insights from large datasets. However, the effectiveness of big data analytics is contingent upon the quality of the data being analyzed. Data cleaning tools help in sanitizing large datasets, making them suitable for analysis and thus enhancing the accuracy and reliability of analytics outcomes. This trend is expected to continue, fueling the demand for data cleaning tools.



    In terms of regional growth, North America holds a dominant position in the data cleaning tools market. The region's strong technological infrastructure, coupled with the presence of major market players and a high adoption rate of advanced data management solutions, contributes to its leadership. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. The rapid digitization of businesses, increasing investments in IT infrastructure, and a growing focus on data-driven decision-making are key factors driving the market in this region.



    As organizations strive to maintain high data quality standards, the role of an Email List Cleaning Service becomes increasingly vital. These services ensure that email databases are free from invalid addresses, duplicates, and outdated information, thereby enhancing the effectiveness of marketing campaigns and communications. By leveraging sophisticated algorithms and validation techniques, email list cleaning services help businesses improve their email deliverability rates and reduce the risk of being flagged as spam. This not only optimizes marketing efforts but also protects the reputation of the sender. As a result, the demand for such services is expected to grow alongside the broader data cleaning tools market, as companies recognize the importance of maintaining clean and accurate contact lists.



    Component Analysis



    The data cleaning tools market can be segmented by component into software and services. The software segment encompasses various tools and platforms designed for data cleaning, while the services segment includes consultancy, implementation, and maintenance services provided by vendors.



    The software segment holds the largest market share and is expected to continue leading during the forecast period. This dominance can be attributed to the increasing adoption of automated data cleaning solutions that offer high efficiency and accuracy. These software solutions are equipped with advanced algorithms and functionalities that can handle large volumes of data, identify errors, and correct them without manual intervention. The rising adoption of cloud-based data cleaning software further bolsters this segment, as it offers scalability and ease of

  5. food data cleaning

    • kaggle.com
    zip
    Updated Apr 13, 2024
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    AbdElRahman16 (2024). food data cleaning [Dataset]. https://www.kaggle.com/datasets/abdelrahman16/food-n
    Explore at:
    zip(0 bytes)Available download formats
    Dataset updated
    Apr 13, 2024
    Authors
    AbdElRahman16
    Description

    Dataset

    This dataset was created by AbdElRahman16

    Contents

  6. B

    Data Cleaning Sample

    • borealisdata.ca
    Updated Jul 13, 2023
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    Rong Luo (2023). Data Cleaning Sample [Dataset]. http://doi.org/10.5683/SP3/ZCN177
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 13, 2023
    Dataset provided by
    Borealis
    Authors
    Rong Luo
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Sample data for exercises in Further Adventures in Data Cleaning.

  7. I

    Data for A Conceptual Model for Transparent, Reusable, and Collaborative...

    • databank.illinois.edu
    Updated Jul 12, 2023
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    Nikolaus Parulian (2023). Data for A Conceptual Model for Transparent, Reusable, and Collaborative Data Cleaning [Dataset]. http://doi.org/10.13012/B2IDB-6827044_V1
    Explore at:
    Dataset updated
    Jul 12, 2023
    Authors
    Nikolaus Parulian
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The dissertation_demo.zip contains the base code and demonstration purpose for the dissertation: A Conceptual Model for Transparent, Reusable, and Collaborative Data Cleaning. Each chapter has a demo folder for demonstrating provenance queries or tools. The Airbnb dataset for demonstration and simulation is not included in this demo but is available to access directly from the reference website. Any updates on demonstration and examples can be found online at: https://github.com/nikolausn/dissertation_demo

  8. d

    B2B Intent Data - ABM Data - 152M+ Profiles - 13M+ Companies - 150+ Data...

    • datarade.ai
    .csv, .xls
    Updated Nov 16, 2024
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    Thomson Data (2024). B2B Intent Data - ABM Data - 152M+ Profiles - 13M+ Companies - 150+ Data points - Updated monthly [Dataset]. https://datarade.ai/data-products/b2b-data-cleansing-services-thomson-data
    Explore at:
    .csv, .xlsAvailable download formats
    Dataset updated
    Nov 16, 2024
    Dataset authored and provided by
    Thomson Data
    Area covered
    Guadeloupe, Saudi Arabia, Vietnam, Brazil, Kenya, Peru, Western Sahara, Panama, Virgin Islands (U.S.), Malawi
    Description

    What is Account-Based-Marketing? Account-based marketing, or ABM, is a business strategy that focuses your resources on a specific segment of customer accounts. It's all about understanding your customers on a personal level and delivering personalized campaigns that resonate with their needs and preferences.

    Why should you use Thomson Data’s Data solution for Account Based Marketing (ABM)? Utilizing Account-based marketing data for your marketing campaign might seem like a long-draw-out approach, but it is absolutely worth the hassle.

    Here are some of the benefits you will definitely be interested in.

    Boost Lead Generation: Our database is designed for effective account-based marketing that will boost lead generation. We enable you to target specific accounts, and our data insights will help you tailor the messages according to their needs and pain points.

    Retain Email Subscribers: Retaining your subscribers is also a concerning challenge. Using our database for account-based marketing will help you to connect with your clients on a personal level. Enabling you to keep them engaged will encourage these clients to consider your products and services whenever they need one.

    Increases profits: As Thomson Data’s records heighten the tone for personalization, you can connect with your prospective clientele on a personal level. When you do it in the right way, it is significantly reflected in your sales figures.

    Gain Insights: Get 100+ insights from our data to make better decision making and implement in your Account based marketing strategies.

    Our ABM data can be used for improving your conversions by 3x times.

    Our Account based marketing data can be used by: 1. B2b companies 2. Sales Teams 3. Marketing Teams 4. C- suite Executives 5. Agencies and Service providers 6. Enterprise Level Organizations and more.

    Thomson Data is perfect for ABM and will certainly help you run campaigns that target customer acquisition as well as customer retention. We provide you an access to the complete data solution to help you connect and impress your target audience.

    Send us a request to know more details about our Account based marketing data and we will be happy to assist you.

  9. RAAAP-2 SPSS Data Cleansing syntax files

    • figshare.com
    txt
    Updated May 16, 2023
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    Simon Kerridge (2023). RAAAP-2 SPSS Data Cleansing syntax files [Dataset]. http://doi.org/10.6084/m9.figshare.18972992.v2
    Explore at:
    txtAvailable download formats
    Dataset updated
    May 16, 2023
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    Simon Kerridge
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    These two syntax files were used to convert the SPSS data output from the Qualtrics survey tool into the 17 cleansed and anonymised RAAAP-2 datasets form the 2019 international survey of research managers and administrators. The first creates and interim cleansed and anonymised datafile, the latter splits these into separate datasets to ensure anonymisation. Errata (16/6/23): v13 of the main Data Cleansing file has an error (two variables were missing value labels). This file has now been replaced with v14, and the Main Dataset has also been updated with the new data.

  10. Teaching & Learning Team Data Cleaning and Visualization Workshop

    • figshare.com
    pdf
    Updated May 31, 2023
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    Elizabeth Joan Kelly (2023). Teaching & Learning Team Data Cleaning and Visualization Workshop [Dataset]. http://doi.org/10.6084/m9.figshare.6223541.v1
    Explore at:
    pdfAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    Elizabeth Joan Kelly
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Materials from workshop conducted for Monroe Library faculty as part of TLT/Faculty Development/Digital Scholarship on 2018-04-05. Objectives:Clean dataAnalyze data using pivot tablesVisualize dataDesign accessible instruction for working with dataAssociated Research Guide at http://researchguides.loyno.edu/data_workshopData sets are from the following:

    BaroqueArt Dataset by CulturePlex Lab is licensed under CC0 What's on the Menu? Menus by New York Public Library is licensed under CC0 Dog movie stars and dog breed popularity by Ghirlanda S, Acerbi A, Herzog H is licensed under CC BY 4.0 NOPD Misconduct Complaints, 2016-2018 by City of New Orleans Open Data is licensed under CC0 U.S. Consumer Product Safety Commission Recall Violations by CU.S. Consumer Product Safety Commission, Violations is licensed under CC0 NCHS - Leading Causes of Death: United States by Data.gov is licensed under CC0 Bob Ross Elements by Episode by Walt Hickey, FiveThirtyEight, is licensed under CC BY 4.0 Pacific Walrus Coastal Haulout 1852-2016 by U.S. Geological Survey, Alaska Science Center is licensed under CC0 Australia Registered Animals by Sunshine Coast Council is licensed under CC0

  11. t

    Data Cleaning Tools Global Market Report 2025

    • thebusinessresearchcompany.com
    pdf,excel,csv,ppt
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    The Business Research Company, Data Cleaning Tools Global Market Report 2025 [Dataset]. https://www.thebusinessresearchcompany.com/report/data-cleaning-tools-global-market-report
    Explore at:
    pdf,excel,csv,pptAvailable download formats
    Dataset authored and provided by
    The Business Research Company
    License

    https://www.thebusinessresearchcompany.com/privacy-policyhttps://www.thebusinessresearchcompany.com/privacy-policy

    Description

    Global Data Cleaning Tools market size is expected to reach $6.78 billion by 2029 at 16.9%, segmented as by audio, music, voice, environmental sounds

  12. SQL Data Cleaning Project1

    • kaggle.com
    Updated Nov 12, 2024
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    christopher alverio (2024). SQL Data Cleaning Project1 [Dataset]. https://www.kaggle.com/datasets/christopheralverio/sql-data-cleaning-project1
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 12, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    christopher alverio
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    Dataset

    This dataset was created by christopher alverio

    Released under MIT

    Contents

  13. M

    MRO Data Cleansing and Enrichment Service Report

    • marketreportanalytics.com
    doc, pdf, ppt
    Updated Apr 10, 2025
    + more versions
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    Market Report Analytics (2025). MRO Data Cleansing and Enrichment Service Report [Dataset]. https://www.marketreportanalytics.com/reports/mro-data-cleansing-and-enrichment-service-76185
    Explore at:
    pdf, doc, pptAvailable download formats
    Dataset updated
    Apr 10, 2025
    Dataset authored and provided by
    Market Report Analytics
    License

    https://www.marketreportanalytics.com/privacy-policyhttps://www.marketreportanalytics.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The MRO (Maintenance, Repair, and Operations) Data Cleansing and Enrichment Service market is experiencing robust growth, driven by the increasing need for accurate and reliable data across diverse industries. The rising adoption of digitalization and data-driven decision-making in sectors like Oil & Gas, Chemicals, Pharmaceuticals, and Manufacturing is a key catalyst. Companies are recognizing the significant value proposition of clean and enriched MRO data in optimizing maintenance schedules, reducing downtime, improving inventory management, and ultimately lowering operational costs. The market is segmented by application (Chemical, Oil and Gas, Pharmaceutical, Mining, Transportation, Others) and type of service (Data Cleansing, Data Enrichment), reflecting the diverse needs of different industries and the varying levels of data processing required. While precise market sizing data is not provided, considering the strong growth drivers and the established presence of numerous players like Enventure, Grihasoft, and OptimizeMRO, a conservative estimate places the 2025 market size at approximately $500 million, with a Compound Annual Growth Rate (CAGR) of 12% projected through 2033. This growth is further fueled by advancements in artificial intelligence (AI) and machine learning (ML) technologies, which are enabling more efficient and accurate data cleansing and enrichment processes. The competitive landscape is characterized by a mix of established players and emerging companies. Established players leverage their extensive industry experience and existing customer bases to maintain market share, while emerging companies are innovating with new technologies and service offerings. Regional growth varies, with North America and Europe currently dominating the market due to higher levels of digital adoption and established MRO processes. However, Asia-Pacific is expected to experience significant growth in the coming years driven by increasing industrialization and investment in digital transformation initiatives within the region. Challenges for market growth include data security concerns, the integration of new technologies with legacy systems, and the need for skilled professionals capable of managing and interpreting large datasets. Despite these challenges, the long-term outlook for the MRO Data Cleansing and Enrichment Service market remains exceptionally positive, driven by the increasing reliance on data-driven insights for improved efficiency and operational excellence across industries.

  14. Data Cleansing Tools Market Report | Global Forecast From 2025 To 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Jan 7, 2025
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    Dataintelo (2025). Data Cleansing Tools Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-data-cleansing-tools-market
    Explore at:
    pdf, csv, pptxAvailable download formats
    Dataset updated
    Jan 7, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Data Cleansing Tools Market Outlook



    The global data cleansing tools market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach USD 4.2 billion by 2032, growing at a CAGR of 12.1% from 2024 to 2032. One of the primary growth factors driving the market is the increasing need for high-quality data in various business operations and decision-making processes.



    The surge in big data and the subsequent increased reliance on data analytics are significant factors propelling the growth of the data cleansing tools market. Organizations increasingly recognize the value of high-quality data in driving strategic initiatives, customer relationship management, and operational efficiency. The proliferation of data generated across different sectors such as healthcare, finance, retail, and telecommunications necessitates the adoption of tools that can clean, standardize, and enrich data to ensure its reliability and accuracy.



    Furthermore, the rising adoption of Machine Learning (ML) and Artificial Intelligence (AI) technologies has underscored the importance of clean data. These technologies rely heavily on large datasets to provide accurate and reliable insights. Any errors or inconsistencies in data can lead to erroneous outcomes, making data cleansing tools indispensable. Additionally, regulatory and compliance requirements across various industries necessitate the maintenance of clean and accurate data, further driving the market for data cleansing tools.



    The growing trend of digital transformation across industries is another critical growth factor. As businesses increasingly transition from traditional methods to digital platforms, the volume of data generated has skyrocketed. However, this data often comes from disparate sources and in various formats, leading to inconsistencies and errors. Data cleansing tools are essential in such scenarios to integrate data from multiple sources and ensure its quality, thus enabling organizations to derive actionable insights and maintain a competitive edge.



    In the context of ensuring data reliability and accuracy, Data Quality Software and Solutions play a pivotal role. These solutions are designed to address the challenges associated with managing large volumes of data from diverse sources. By implementing robust data quality frameworks, organizations can enhance their data governance strategies, ensuring that data is not only clean but also consistent and compliant with industry standards. This is particularly crucial in sectors where data-driven decision-making is integral to business success, such as finance and healthcare. The integration of advanced data quality solutions helps businesses mitigate risks associated with poor data quality, thereby enhancing operational efficiency and strategic planning.



    Regionally, North America is expected to hold the largest market share due to the early adoption of advanced technologies, robust IT infrastructure, and the presence of key market players. Europe is also anticipated to witness substantial growth due to stringent data protection regulations and the increasing adoption of data-driven decision-making processes. Meanwhile, the Asia Pacific region is projected to experience the highest growth rate, driven by the rapid digitalization of emerging economies, the expansion of the IT and telecommunications sector, and increasing investments in data management solutions.



    Component Analysis



    The data cleansing tools market is segmented into software and services based on components. The software segment is anticipated to dominate the market due to its extensive use in automating the data cleansing process. The software solutions are designed to identify, rectify, and remove errors in data sets, ensuring data accuracy and consistency. They offer various functionalities such as data profiling, validation, enrichment, and standardization, which are critical in maintaining high data quality. The high demand for these functionalities across various industries is driving the growth of the software segment.



    On the other hand, the services segment, which includes professional services and managed services, is also expected to witness significant growth. Professional services such as consulting, implementation, and training are crucial for organizations to effectively deploy and utilize data cleansing tools. As businesses increasingly realize the importance of clean data, the demand for expert

  15. Z

    Data Cleaning, Translation & Split of the Dataset for the Automatic...

    • data.niaid.nih.gov
    Updated Aug 8, 2022
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    Köhler, Juliane (2022). Data Cleaning, Translation & Split of the Dataset for the Automatic Classification of Documents for the Classification System for the Berliner Handreichungen zur Bibliotheks- und Informationswissenschaft [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_6957841
    Explore at:
    Dataset updated
    Aug 8, 2022
    Dataset authored and provided by
    Köhler, Juliane
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Cleaned_Dataset.csv – The combined CSV files of all scraped documents from DABI, e-LiS, o-bib and Springer.

    Data_Cleaning.ipynb – The Jupyter Notebook with python code for the analysis and cleaning of the original dataset.

    ger_train.csv – The German training set as CSV file.

    ger_validation.csv – The German validation set as CSV file.

    en_test.csv – The English test set as CSV file.

    en_train.csv – The English training set as CSV file.

    en_validation.csv – The English validation set as CSV file.

    splitting.py – The python code for splitting a dataset into train, test and validation set.

    DataSetTrans_de.csv – The final German dataset as a CSV file.

    DataSetTrans_en.csv – The final English dataset as a CSV file.

    translation.py – The python code for translating the cleaned dataset.

  16. d

    Navigating Stats Can Data & Scrubbing Data Clean with Excel Workshop

    • search.dataone.org
    • borealisdata.ca
    Updated Jul 31, 2024
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    Costanzo, Lucia; Jadon, Vivek (2024). Navigating Stats Can Data & Scrubbing Data Clean with Excel Workshop [Dataset]. http://doi.org/10.5683/SP3/FF6AI9
    Explore at:
    Dataset updated
    Jul 31, 2024
    Dataset provided by
    Borealis
    Authors
    Costanzo, Lucia; Jadon, Vivek
    Description

    Ahoy, data enthusiasts! Join us for a hands-on workshop where you will hoist your sails and navigate through the Statistics Canada website, uncovering hidden treasures in the form of data tables. With the wind at your back, you’ll master the art of downloading these invaluable Stats Can datasets while braving the occasional squall of data cleaning challenges using Excel with your trusty captains Vivek and Lucia at the helm.

  17. Cleaning Data from Case Study @

    • kaggle.com
    Updated Feb 5, 2024
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    Carlos Ramon1905 (2024). Cleaning Data from Case Study @ [Dataset]. https://www.kaggle.com/datasets/carlosramon1905/cleaning-data-from-case-study
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 5, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Carlos Ramon1905
    Description

    Dataset

    This dataset was created by Carlos Ramon1905

    Contents

  18. d

    Mobile Location Data | Asia | +300M Unique Devices | +100M Daily Users |...

    • datarade.ai
    .json, .csv, .xls
    Updated Mar 21, 2025
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    Quadrant (2025). Mobile Location Data | Asia | +300M Unique Devices | +100M Daily Users | +200B Events / Month [Dataset]. https://datarade.ai/data-products/mobile-location-data-asia-300m-unique-devices-100m-da-quadrant
    Explore at:
    .json, .csv, .xlsAvailable download formats
    Dataset updated
    Mar 21, 2025
    Dataset authored and provided by
    Quadrant
    Area covered
    Iran (Islamic Republic of), Israel, Palestine, Korea (Democratic People's Republic of), Oman, Armenia, Georgia, Kyrgyzstan, Philippines, Bahrain, Asia
    Description

    Quadrant provides Insightful, accurate, and reliable mobile location data.

    Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

    These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

    We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

    We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

    Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

    Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

  19. Restaurant Sales-Dirty Data for Cleaning Training

    • kaggle.com
    Updated Jan 25, 2025
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    Ahmed Mohamed (2025). Restaurant Sales-Dirty Data for Cleaning Training [Dataset]. https://www.kaggle.com/datasets/ahmedmohamed2003/restaurant-sales-dirty-data-for-cleaning-training
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 25, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Ahmed Mohamed
    License

    Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
    License information was derived automatically

    Description

    Restaurant Sales Dataset with Dirt Documentation

    Overview

    The Restaurant Sales Dataset with Dirt contains data for 17,534 transactions. The data introduces realistic inconsistencies ("dirt") to simulate real-world scenarios where data may have missing or incomplete information. The dataset includes sales details across multiple categories, such as starters, main dishes, desserts, drinks, and side dishes.

    Dataset Use Cases

    This dataset is suitable for: - Practicing data cleaning tasks, such as handling missing values and deducing missing information. - Conducting exploratory data analysis (EDA) to study restaurant sales patterns. - Feature engineering to create new variables for machine learning tasks.

    Columns Description

    Column NameDescriptionExample Values
    Order IDA unique identifier for each order.ORD_123456
    Customer IDA unique identifier for each customer.CUST_001
    CategoryThe category of the purchased item.Main Dishes, Drinks
    ItemThe name of the purchased item. May contain missing values due to data dirt.Grilled Chicken, None
    PriceThe static price of the item. May contain missing values.15.0, None
    QuantityThe quantity of the purchased item. May contain missing values.1, None
    Order TotalThe total price for the order (Price * Quantity). May contain missing values.45.0, None
    Order DateThe date when the order was placed. Always present.2022-01-15
    Payment MethodThe payment method used for the transaction. May contain missing values due to data dirt.Cash, None

    Key Characteristics

    1. Data Dirtiness:

      • Missing values in key columns (Item, Price, Quantity, Order Total, Payment Method) simulate real-world challenges.
      • At least one of the following conditions is ensured for each record to identify an item:
        • Item is present.
        • Price is present.
        • Both Quantity and Order Total are present.
      • If Price or Quantity is missing, the other is used to deduce the missing value (e.g., Order Total / Quantity).
    2. Menu Categories and Items:

      • Items are divided into five categories:
        • Starters: E.g., Chicken Melt, French Fries.
        • Main Dishes: E.g., Grilled Chicken, Steak.
        • Desserts: E.g., Chocolate Cake, Ice Cream.
        • Drinks: E.g., Coca Cola, Water.
        • Side Dishes: E.g., Mashed Potatoes, Garlic Bread.

    3 Time Range: - Orders span from January 1, 2022, to December 31, 2023.

    Cleaning Suggestions

    1. Handle Missing Values:

      • Fill missing Order Total or Quantity using the formula: Order Total = Price * Quantity.
      • Deduce missing Price from Order Total / Quantity if both are available.
    2. Validate Data Consistency:

      • Ensure that calculated values (Order Total = Price * Quantity) match.
    3. Analyze Missing Patterns:

      • Study the distribution of missing values across categories and payment methods.

    Menu Map with Prices and Categories

    CategoryItemPrice
    StartersChicken Melt8.0
    StartersFrench Fries4.0
    StartersCheese Fries5.0
    StartersSweet Potato Fries5.0
    StartersBeef Chili7.0
    StartersNachos Grande10.0
    Main DishesGrilled Chicken15.0
    Main DishesSteak20.0
    Main DishesPasta Alfredo12.0
    Main DishesSalmon18.0
    Main DishesVegetarian Platter14.0
    DessertsChocolate Cake6.0
    DessertsIce Cream5.0
    DessertsFruit Salad4.0
    DessertsCheesecake7.0
    DessertsBrownie6.0
    DrinksCoca Cola2.5
    DrinksOrange Juice3.0
    Drinks ...
  20. d

    Enviro-Champs Formshare Data Cleaning Tool

    • search.dataone.org
    Updated Sep 24, 2024
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    Udhav Maharaj (2024). Enviro-Champs Formshare Data Cleaning Tool [Dataset]. http://doi.org/10.7910/DVN/EA5MOI
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    Dataset updated
    Sep 24, 2024
    Dataset provided by
    Harvard Dataverse
    Authors
    Udhav Maharaj
    Time period covered
    Jan 1, 2023 - Jan 1, 2024
    Description

    A data cleaning tool customised for cleaning and sorting the data generated during the Enviro-Champs pilot study as they are downloaded from Formshare, the platform capturing data sent from a customised ODK Collect form collection app. The dataset inclues the latest data from the pilot study as at 14 May 2024.

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kenanyafi (2024). A Journey through Data Cleaning [Dataset]. https://www.kaggle.com/datasets/kenanyafi/a-journey-through-data-cleaning
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A Journey through Data Cleaning

Streamlining Data for Enhanced Analysis and Decision-Making

Explore at:
zip(0 bytes)Available download formats
Dataset updated
Mar 22, 2024
Authors
kenanyafi
Description

Embark on a transformative journey with our Data Cleaning Project, where we meticulously refine and polish raw data into valuable insights. Our project focuses on streamlining data sets, removing inconsistencies, and ensuring accuracy to unlock its full potential.

Through advanced techniques and rigorous processes, we standardize formats, address missing values, and eliminate duplicates, creating a clean and reliable foundation for analysis. By enhancing data quality, we empower organizations to make informed decisions, drive innovation, and achieve strategic objectives with confidence.

Join us as we embark on this essential phase of data preparation, paving the way for more accurate and actionable insights that fuel success."

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