Sample data for exercises in Further Adventures in Data Cleaning.

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."

The Data Cleaning Tools Market is projected to grow at 16.9% CAGR, reaching $6.78 Billion by 2029. Where is the industry heading next? Get the sample report now!

Access and clean an open source herbarium dataset using Excel or RStudio.

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

Dataset

This dataset was created by Luis Lira

Contents

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.

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.

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.

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.

The global data cleansing tools market is projected to reach USD 4.7 billion by 2033, expanding at a CAGR of 9.6% during the forecast period (2025-2033). The market growth is attributed to factors such as the increasing volume and complexity of data, the need for accurate and reliable data for decision-making, and the growing adoption of cloud-based data cleansing solutions. The market is also witnessing the emergence of new technologies such as artificial intelligence (AI) and machine learning (ML), which are expected to further drive market growth in the coming years. Among the different application segments, large enterprises are expected to hold the largest market share during the forecast period. This is due to the fact that large enterprises have large volumes of data that need to be cleaned and processed, and they have the resources to invest in data cleansing tools. The SaaS segment is expected to grow at the highest CAGR during the forecast period. This is due to the increasing popularity of cloud-based solutions, which offer benefits such as scalability, cost-effectiveness, and ease of deployment. The North America region is expected to hold the largest market share during the forecast period. This is due to the presence of a large number of technology companies and the early adoption of data cleansing tools in the region.

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).

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

As of November 2023, 136 brands had used Disney Advertising's data clean room solution. A data clean room is a digital environment where various parties (such as brands, agencies, retailers, etc.) can combine their first-party data in order to produce audience insights.

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.

146883 Global import shipment records of Cleaning with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

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 Aditi Dash

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