Context

This file contains 5 years of daily time series data for several measures of traffic on a statistical forecasting teaching notes website whose alias is statforecasting.com. The variables have complex seasonality that is keyed to the day of the week and to the academic calendar. The patterns you you see here are similar in principle to what you would see in other daily data with day-of-week and time-of-year effects. Some good exercises are to develop a 1-day-ahead forecasting model, a 7-day ahead forecasting model, and an entire-next-week forecasting model (i.e., next 7 days) for unique visitors.

Content

The variables are daily counts of page loads, unique visitors, first-time visitors, and returning visitors to an academic teaching notes website. There are 2167 rows of data spanning the date range from September 14, 2014, to August 19, 2020. A visit is defined as a stream of hits on one or more pages on the site on a given day by the same user, as identified by IP address. Multiple individuals with a shared IP address (e.g., in a computer lab) are considered as a single user, so real users may be undercounted to some extent. A visit is classified as "unique" if a hit from the same IP address has not come within the last 6 hours. Returning visitors are identified by cookies if those are accepted. All others are classified as first-time visitors, so the count of unique visitors is the sum of the counts of returning and first-time visitors by definition. The data was collected through a traffic monitoring service known as StatCounter.

Inspiration

This file and a number of other sample datasets can also be found on the website of RegressIt, a free Excel add-in for linear and logistic regression which I originally developed for use in the course whose website generated the traffic data given here. If you use Excel to some extent as well as Python or R, you might want to try it out on this dataset.

This data about nola.gov provides a window into how people are interacting with the the City of New Orleans online. The data comes from a unified Google Analytics account for New Orleans. We do not track individuals and we anonymize the IP addresses of all visitors.

Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

Per the Federal Digital Government Strategy, the Department of Homeland Security Metrics Plan, and the Open FEMA Initiative, FEMA is providing the following web performance metrics with regards to FEMA.gov.rnrnInformation in this dataset includes total visits, avg visit duration, pageviews, unique visitors, avg pages/visit, avg time/page, bounce ratevisits by source, visits by Social Media Platform, and metrics on new vs returning visitors.rnrnExternal Affairs strives to make all communications accessible. If you have any challenges accessing this information, please contact FEMAWebTeam@fema.dhs.gov.

These data represent the number of visitors to tourist sites by location

🇧🇪 벨기에

The dataset provides 12 months (August 2016 to August 2017) of obfuscated Google Analytics 360 data from the Google Merchandise Store , a real ecommerce store that sells Google-branded merchandise, in BigQuery. It’s a great way analyze business data and learn the benefits of using BigQuery to analyze Analytics 360 data Learn more about the data The data includes The data is typical of what an ecommerce website would see and includes the following information:Traffic source data: information about where website visitors originate, including data about organic traffic, paid search traffic, and display trafficContent data: information about the behavior of users on the site, such as URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions on the Google Merchandise Store website.Limitations: All users have view access to the dataset. This means you can query the dataset and generate reports but you cannot complete administrative tasks. Data for some fields is obfuscated such as fullVisitorId, or removed such as clientId, adWordsClickInfo and geoNetwork. “Not available in demo dataset” will be returned for STRING values and “null” will be returned for INTEGER values when querying the fields containing no data.This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery

In November 2024, Google.com was the leading website in Colombia by unique visits, with around 52.9 million single accesses to the URL during that month. YouTube.com came in second with approximately 30.9 million unique monthly visits. Facebook ranked third with 24.2 million unique monthly visits.

Summary time series data of the International Visitor Survey, the National Visitor Survey and the State Tourism Satellite Account, as published by Tourism Research Australia (TRA). These data sources estimate total visitor expenditure in South Australia, direct tourism jobs and regional tourism expenditure. Breakdowns of visitor origin are also provided, with time series of visitors from the UK, Germany, USA, China and New Zealand, as well as domestic visitors in South Australia.

For further details on these datasets please visit the TRA website: https://www.tra.gov.au/research

ACTIVITY ENGAGED BY TOURISTS : VISITING HISTORICAL SITE (%) SOURCE : DEPARTING VISITOR SURVEY, TOURISM MALAYSIA

Kenya National Bureau of Statistics Statistical Abstract 1998-2009 Visitors To Museums Snake Park And Sites (Numbers)

Number of Visitors: Desert Castles: Residents data was reported at 0.000 Person in Jun 2018. This stayed constant from the previous number of 0.000 Person for May 2018. Number of Visitors: Desert Castles: Residents data is updated monthly, averaging 0.000 Person from Jan 2011 (Median) to Jun 2018, with 81 observations. The data reached an all-time high of 110.000 Person in Jun 2016 and a record low of 0.000 Person in Jun 2018. Number of Visitors: Desert Castles: Residents data remains active status in CEIC and is reported by Ministry of Tourism and Antiquities. The data is categorized under Global Database’s Jordan – Table JO.Q009: Number of Visitors: by Tourist Sites.

Monthly statistics for pages viewed by visitors to the Queensland Government website—People with disability franchise. Source: Google Analytics

Number of Visitors: Shubak Castle: Residents data was reported at 0.000 Person in Dec 2017. This records a decrease from the previous number of 20.000 Person for Nov 2017. Number of Visitors: Shubak Castle: Residents data is updated monthly, averaging 113.000 Person from Jan 2006 (Median) to Dec 2017, with 144 observations. The data reached an all-time high of 1,827.000 Person in Apr 2007 and a record low of 0.000 Person in Dec 2017. Number of Visitors: Shubak Castle: Residents data remains active status in CEIC and is reported by Ministry of Tourism and Antiquities. The data is categorized under Global Database’s Jordan – Table JO.Q009: Number of Visitors: by Tourist Sites.

This data represents the number of visitors to archaeological sites in Jordan

Foreword

This users dataset is a preview of a much bigger dataset, with lots of related data (product listings of sellers, comments on listed products, etc...).

My Telegram bot will answer your queries and allow you to contact me.

Context

There are a lot of unknowns when running an E-commerce store, even when you have analytics to guide your decisions.

Users are an important factor in an e-commerce business. This is especially true in a C2C-oriented store, since they are both the suppliers (by uploading their products) AND the customers (by purchasing other user's articles).

This dataset aims to serve as a benchmark for an e-commerce fashion store. Using this dataset, you may want to try and understand what you can expect of your users and determine in advance how your grows may be.

• For instance, if you see that most of your users are not very active, you may look into this dataset to compare your store's performance.

If you think this kind of dataset may be useful or if you liked it, don't forget to show your support or appreciation with an upvote/comment. You may even include how you think this dataset might be of use to you. This way, I will be more aware of specific needs and be able to adapt my datasets to suits more your needs.

This dataset is part of a preview of a much larger dataset. Please contact me for more.

Content

The data was scraped from a successful online C2C fashion store with over 10M registered users. The store was first launched in Europe around 2009 then expanded worldwide.

Visitors vs Users: Visitors do not appear in this dataset. Only registered users are included. "Visitors" cannot purchase an article but can view the catalog.

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

Questions you might want to answer using this dataset:

• Are e-commerce users interested in social network feature ?
• Are my users active enough (compared to those of this dataset) ?
• How likely are people from other countries to sign up in a C2C website ?
• How many users are likely to drop off after years of using my service ?

Example works:

• Report(s) made using SQL queries can be found on the data.world page of the dataset.
• Notebooks may be found on the Kaggle page of the dataset.

License

CC-BY-NC-SA 4.0

For other licensing options, contact me.

Visitors and views of the City of Brussels website per page (from 2023, determined by IP address)

Source: Google Analytics

🇬🇧 United Kingdom English Introduction The GiGL Spaces to Visit dataset provides locations and boundaries for open space sites in Greater London that are available to the public as destinations for leisure, activities and community engagement. It includes green corridors that provide opportunities for walking and cycling. The dataset has been created by Greenspace Information for Greater London CIC (GiGL). As London’s Environmental Records Centre, GiGL mobilises, curates and shares data that underpin our knowledge of London’s natural environment. We provide impartial evidence to support informed discussion and decision making in policy and practice. GiGL maps under licence from the Greater London Authority. Description This dataset is a sub-set of the GiGL Open Space dataset, the most comprehensive dataset available of open spaces in London. Sites are selected for inclusion in Spaces to Visit based on their public accessibility and likelihood that people would be interested in visiting. The dataset is a mapped Geographic Information System (GIS) polygon dataset where one polygon (or multi-polygon) represents one space. As well as site boundaries, the dataset includes information about a site’s name, size and type (e.g. park, playing field etc.). GiGL developed the Spaces to Visit dataset to support anyone who is interested in London’s open spaces - including community groups, web and app developers, policy makers and researchers - with an open licence data source. More detailed and extensive data are available under GiGL data use licences for GIGL partners, researchers and students. Information services are also available for ecological consultants, biological recorders and community volunteers – please see www.gigl.org.uk for more information. Please note that access and opening times are subject to change (particularly at the current time) so if you are planning to visit a site check on the local authority or site website that it is open. The dataset is updated on a quarterly basis. If you have questions about this dataset please contact GiGL’s GIS and Data Officer. Data sources The boundaries and information in this dataset, are a combination of data collected during the London Survey Method habitat and open space survey programme (1986 – 2008) and information provided to GiGL from other sources since. These sources include London borough surveys, land use datasets, volunteer surveys, feedback from the public, park friends’ groups, and updates made as part of GiGL’s on-going data validation and verification process. Due to data availability, some areas are more up-to-date than others. We are continually working on updating and improving this dataset. If you have any additional information or corrections for sites included in the Spaces to Visit dataset please contact GiGL’s GIS and Data Officer. NOTE: The dataset contains OS data © Crown copyright and database rights 2025. The site boundaries are based on Ordnance Survey mapping, and the data are published under Ordnance Survey's 'presumption to publish'. When using these data please acknowledge GiGL and Ordnance Survey as the source of the information using the following citation: ‘Dataset created by Greenspace Information for Greater London CIC (GiGL), 2025 – Contains Ordnance Survey and public sector information licensed under the Open Government Licence v3.0 ’

Introduction The GiGL Spaces to Visit dataset provides locations and boundaries for open space sites in Greater London that are available to the public as destinations for leisure, activities and community engagement. It includes green corridors that provide opportunities for walking and cycling. The dataset has been created by Greenspace Information for Greater London CIC (GiGL). As London’s Environmental Records Centre, GiGL mobilises, curates and shares data that underpin our knowledge of London’s natural environment. We provide impartial evidence to support informed discussion and decision making in policy and practice. GiGL maps under licence from the Greater London Authority. Description This dataset is a sub-set of the GiGL Open Space dataset, the most comprehensive dataset available of open spaces in London. Sites are selected for inclusion in Spaces to Visit based on their public accessibility and likelihood that people would be interested in visiting. The dataset is a mapped Geographic Information System (GIS) polygon dataset where one polygon (or multi-polygon) represents one space. As well as site boundaries, the dataset includes information about a site’s name, size and type (e.g. park, playing field etc.). GiGL developed the Spaces to Visit dataset to support anyone who is interested in London’s open spaces - including community groups, web and app developers, policy makers and researchers - with an open licence data source. More detailed and extensive data are available under GiGL data use licences for GIGL partners, researchers and students. Information services are also available for ecological consultants, biological recorders and community volunteers – please see www.gigl.org.uk for more information. Please note that access and opening times are subject to change (particularly at the current time) so if you are planning to visit a site check on the local authority or site website that it is open. The dataset is updated on a quarterly basis. If you have questions about this dataset please contact GiGL’s GIS and Data Officer. Data sources The boundaries and information in this dataset, are a combination of data collected during the London Survey Method habitat and open space survey programme (1986 – 2008) and information provided to GiGL from other sources since. These sources include London borough surveys, land use datasets, volunteer surveys, feedback from the public, park friends’ groups, and updates made as part of GiGL’s on-going data validation and verification process. Due to data availability, some areas are more up-to-date than others. We are continually working on updating and improving this dataset. If you have any additional information or corrections for sites included in the Spaces to Visit dataset please contact GiGL’s GIS and Data Officer. NOTE: The dataset contains OS data © Crown copyright and database rights 2024. The site boundaries are based on Ordnance Survey mapping, and the data are published under Ordnance Survey's 'presumption to publish'. When using these data please acknowledge GiGL and Ordnance Survey as the source of the information using the following citation: ‘Dataset created by Greenspace Information for Greater London CIC (GiGL), 2024 – Contains Ordnance Survey and public sector information licensed under the Open Government Licence v3.0 ’

Monthly statistics for pages viewed by visitors to the Queensland Government website—Seniors franchise. Source: Google Analytics Monthly statistics for pages viewed by visitors to the Queensland Government website—Seniors franchise. Source: Google Analytics