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."

Sample data for exercises in Further Adventures in Data Cleaning.

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

This clean dataset is a refined version of our company datasets, consisting of 35M+ data records.

It’s an excellent data solution for companies with limited data engineering capabilities and those who want to reduce their time to value. You get filtered, cleaned, unified, and standardized B2B data. After cleaning, this data is also enriched by leveraging a carefully instructed large language model (LLM).

AI-powered data enrichment offers more accurate information in key data fields, such as company descriptions. It also produces over 20 additional data points that are very valuable to B2B businesses. Enhancing and highlighting the most important information in web data contributes to quicker time to value, making data processing much faster and easier.

For your convenience, you can choose from multiple data formats (Parquet, JSON, JSONL, or CSV) and select suitable delivery frequency (quarterly, monthly, or weekly).

Coresignal is a leading public business data provider in the web data sphere with an extensive focus on firmographic data and public employee profiles. More than 3B data records in different categories enable companies to build data-driven products and generate actionable insights. Coresignal is exceptional in terms of data freshness, with 890M+ records updated monthly for unprecedented accuracy and relevance.

During a 2023 survey carried out among marketing leaders predominantly in consumer packaged goods and retail from North America, the most common driver for clean room strategies were in-depth analytics (named by 56 percent of respondents), ability to measure campaign results (54 percent), and ease of data integration (52 percent). In a different survey, 29 percent of responding U.S. marketers said they would focus more on data clean rooms in 2023 than they had in 2022.

The market for data center cleaning services is expected to grow from USD XXX million in 2025 to USD XXX million by 2033, at a CAGR of XX% during the forecast period 2025-2033. The growth of the market is attributed to the increasing number of data centers and the need to maintain these facilities in a clean environment. Data centers are critical to the functioning of the modern economy, as they house the servers that store and process vast amounts of data. Maintaining these facilities in a clean environment is essential to prevent the accumulation of dust and other contaminants, which can lead to equipment failures and downtime. The market for data center cleaning services is segmented by type, application, and region. By type, the market is segmented into equipment cleaning, ceiling cleaning, floor cleaning, and others. Equipment cleaning is the largest segment of the market, accounting for over XX% of the total market revenue in 2025. By application, the market is segmented into the internet industry, finance and insurance, manufacturing industry, government departments, and others. The internet industry is the largest segment of the market, accounting for over XX% of the total market revenue in 2025. By region, the market is segmented into North America, South America, Europe, the Middle East & Africa, and Asia Pacific. North America is the largest segment of the market, accounting for over XX% of the total market revenue in 2025.

This data collection focuses on the solar PV and wind industries in China, Germany, India, Japan, and the United States (U.S.). It provides a historical cross-country set of indicators that shows trends in industry development in terms of size, installed capacity, and jobs created (where available) between 2000 and 2010.Data from World Resources Institute. Follow datasource.kapsarc.org for timely data to advance energy economics research.

This dataset is about book subjects and is filtered where the books is Data cleaning and exploration with machine learning : clean data with machine learning algorithms and techniques, featuring 10 columns including authors, average publication date, book publishers, book subject, and books. The preview is ordered by number of books (descending).

Dataset

This dataset was created by Hanzada Fayez

Released under MIT

Contents

Clean Transportation Program Data 2022. The Clean Transportation Program (also known as Alternative and Renewable Fuel and Vehicle Technology Program) invests up to $100 million annually in a broad portfolio of transportation and fuel transportation projects throughout the state. The Energy Commission leverages public and private investments to support adoption of cleaner transportation powered by alternative and renewable fuels. The program plays an important role in achieving California’s ambitious goals on climate change, petroleum reduction, and adoption of zero-emission vehicles, as well as efforts to reach air quality standards. The program also supports the state’s sustainable, long-term economic development.Data within this application was last updated August 2024.For more information on the Clean Transportation Program, visit:https://www.energy.ca.gov/programs-and-topics/programs/clean-transportation-program

Raw and clean data for Jyutping project, submitted to International Journal of Epidemiology.All data are openly available at the time of scrapping. I only retained Chinese Name and Hong Kong Government Romanised English Names. This project aims to describe the problem of non-standardised romanisation and it's impact on data linkage. The included data allows researchers to replicate my process of extracting Jyutping and Pinyin from Chinese Characters. Quite a few of manual screening and reviewing was required, so the code itself was not fully automated. The codes are stored on my personal GitHub, https://github.com/Jo-Lam/Jyutping_project/tree/main.Please cite this data resource: doi:10.5522/04/26504347

Dataset

This dataset was created by Aditi Dash

Contents

This data used for research that concern about Immunization and health of children in West Java Province, Indonesia. The data collection process was carried out from August 1 to August 31, 2021, with the target sample including parents who have children under the age of 5 years.

Dataset

This dataset was created by yanqiangmiffy

Contents

Dataset containing information on location and usage of heavy-duty vehicles operated in New York State by large private entities and by government agencies and municipalities

Data Wrangling Market size was valued at USD 1.63 Billion in 2024 and is projected to reach USD 3.2 Billion by 2031, growing at a CAGR of 8.80 % during the forecast period 2024-2031.

Global Data Wrangling Market Drivers

Growing Volume and Variety of Data: As digitalization has progressed, organizations have produced an exponential increase in both volume and variety of data. Data from a variety of sources, including social media, IoT devices, sensors, and workplace apps, is included in this, both structured and unstructured. Data wrangling tools are an essential part of contemporary data management methods because they allow firms to manage this heterogeneous data landscape effectively.

Growing Adoption of Advanced Analytics: To extract useful insights from data, companies in a variety of sectors are utilizing advanced analytics tools like artificial intelligence and machine learning. Nevertheless, access to clean, well-researched data is essential to the accomplishment of many analytics projects. The need for data wrangling solutions is fueled by the necessity of ensuring that data is accurate, consistent, and clean for usage in advanced analytics models.

Self-service data preparation solutions are becoming more and more necessary as data volumes rise. These technologies enable business users to prepare and analyze data on their own without requiring significant IT assistance. Platforms for data wrangling provide non-technical users with easy-to-use interfaces and functionalities that make it simple for them to clean, manipulate, and combine data. Data wrangling solutions are being used more quickly because of this self-service approach’s ability to increase agility and facilitate quicker decision-making within enterprises.

Emphasis on Data Governance and Compliance: With the rise of regulated sectors including healthcare, finance, and government, data governance and compliance have emerged as critical organizational concerns. Data wrangling technologies offer features for auditability, metadata management, and data quality control, which help with adhering to data governance regulations. The adoption of data wrangling solutions is fueled by these features, which assist enterprises in ensuring data integrity, privacy, and regulatory compliance.

Big Data Technologies’ Emergence: Companies can now store and handle enormous amounts of data more affordably because to the emergence of big data technologies like Hadoop, Spark, and NoSQL databases. However, efficient data preparation methods are needed to extract value from massive data. Organizations may accelerate their big data analytics initiatives by preprocessing and cleansing large amounts of data at scale with the help of data wrangling solutions that seamlessly interact with big data platforms.

Put an emphasis on cost-cutting and operational efficiency: Organizations are under pressure to maximize operational efficiency and cut expenses in the cutthroat business environment of today. Organizations can increase productivity and reduce resource requirements by implementing data wrangling solutions, which automate manual data preparation processes and streamline workflows. Furthermore, the danger of errors and expensive aftereffects is reduced when data quality problems are found and fixed early in the data pipeline.

CLICK HERE to view metadata. For questions or technical assistance please email maps@phila.gov.

6395 Global import shipment records of Clean,room,panel with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

251642 Global import shipment records of Cleaning Cloth with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.