The Google Reviews & Ratings Dataset provides businesses with structured insights into customer sentiment, satisfaction, and trends based on reviews from Google. Unlike broad review datasets, this product is location-specific—businesses provide the locations they want to track, and we retrieve as much historical data as possible, with daily updates moving forward.

This dataset enables businesses to monitor brand reputation, analyze consumer feedback, and enhance decision-making with real-world insights. For deeper analysis, optional AI-driven sentiment analysis and review summaries are available on a weekly, monthly, or yearly basis.

Dataset Highlights

• Location-Specific Reviews – Reviews and ratings for the locations you provide.
• Daily Updates – New reviews and rating changes updated automatically.
• Historical Data Access – Retrieve past reviews where available.
• AI Sentiment Analysis (Optional) – Summarized insights by week, month, or year.
• Competitive Benchmarking – Compare performance across selected locations.

Use Cases

• Franchise & Retail Chains – Monitor brand reputation and performance across locations.
• Hospitality & Restaurants – Track guest sentiment and service trends.
• Healthcare & Medical Facilities – Understand patient feedback for specific locations.
• Real Estate & Property Management – Analyze tenant and customer experiences through reviews.
• Market Research & Consumer Insights – Identify trends and analyze feedback patterns across industries.

Data Updates & Delivery

• Update Frequency: Daily
• Data Format: CSV for easy integration
• Delivery: Secure file transfer (SFTP or cloud storage)

Data Fields Include:

• Business Name
• Location Details
• Star Ratings
• Review Text
• Timestamps
• Reviewer Metadata

Optional Add-Ons:

• AI Sentiment Analysis – Aggregate trends by week, month, or year.
• Custom Location Tracking – Tailor the dataset to fit your specific business needs.

Ideal for

• Marketing Teams – Leverage real-world consumer feedback to optimize brand strategy.
• Business Analysts – Use structured review data to track customer sentiment over time.
• Operations & Customer Experience Teams – Identify service issues and opportunities for improvement.
• Competitive Intelligence – Compare locations and benchmark against industry competitors.

Why Choose This Dataset?

• Accurate & Up-to-Date – Daily updates ensure fresh, reliable data.
• Scalable & Customizable – Track only the locations that matter to you.
• Actionable Insights – AI-driven summaries for quick decision-making.
• Easy Integration – Delivered in a structured format for seamless analysis.

By leveraging Google Reviews & Ratings Data, businesses can gain valuable insights into customer sentiment, enhance reputation management, and stay ahead of the competition.

Context

DataSF seeks to transform the way that the City of San Francisco works -- through the use of data.

https://datasf.org/about/

Content

This dataset contains the following tables: ['311_service_requests', 'bikeshare_stations', 'bikeshare_status', 'bikeshare_trips', 'film_locations', 'sffd_service_calls', 'sfpd_incidents', 'street_trees']

• This data includes all San Francisco 311 service requests from July 2008 to the present, and is updated daily. 311 is a non-emergency number that provides access to non-emergency municipal services.
• This data includes fire unit responses to calls from April 2000 to present and is updated daily. Data contains the call number, incident number, address, unit identifier, call type, and disposition. Relevant time intervals are also included. Because this dataset is based on responses, and most calls involved multiple fire units, there are multiple records for each call number. Addresses are associated with a block number, intersection or call box.
• This data includes incidents from the San Francisco Police Department (SFPD) Crime Incident Reporting system, from January 2003 until the present (2 weeks ago from current date). The dataset is updated daily. Please note: the SFPD has implemented a new system for tracking crime. This dataset is still sourced from the old system, which is in the process of being retired (a multi-year process).
• This data includes a list of San Francisco Department of Public Works maintained street trees including: planting date, species, and location. Data includes 1955 to present.

This dataset is deprecated and not being updated.

Fork this kernel to get started with this dataset.

Acknowledgements

http://datasf.org/

• https://cloud.google.com/bigquery/public-data/sfo-311
• https://cloud.google.com/bigquery/public-data/sffd-service-calls
• https://cloud.google.com/bigquery/public-data/sfpd-reports
• https://cloud.google.com/bigquery/public-data/sfo-trees

Dataset Source: SF OpenData. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://sfgov.org/ - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @meric from Unplash.

Inspiration

Which neighborhoods have the highest proportion of offensive graffiti?

Which complaint is most likely to be made using Twitter and in which neighborhood?

What are the most complained about Muni stops in San Francisco?

What are the top 10 incident types that the San Francisco Fire Department responds to?

How many medical incidents and structure fires are there in each neighborhood?

What’s the average response time for each type of dispatched vehicle?

Which category of police incidents have historically been the most common in San Francisco?

What were the most common police incidents in the category of LARCENY/THEFT in 2016?

Which non-criminal incidents saw the biggest reporting change from 2015 to 2016?

What is the average tree diameter?

What is the highest number of a particular species of tree planted in a single year?

Which San Francisco locations feature the largest number of trees?

Due to changes in the collection and availability of data on COVID-19, this website will no longer be updated. The webpage will no longer be available as of 11 May 2023. On-going, reliable sources of data for COVID-19 are available via the COVID-19 dashboard and the UKHSA GLA Covid-19 Mobility Report Since March 2020, London has seen many different levels of restrictions - including three separate lockdowns and many other tiers/levels of restrictions, as well as easing of restrictions and even measures to actively encourage people to go to work, their high streets and local restaurants. This reports gathers data from a number of sources, including google, apple, citymapper, purple wifi and opentable to assess the extent to which these levels of restrictions have translated to a reductions in Londoners' movements. The data behind the charts below come from different sources. None of these data represent a direct measure of how well people are adhering to the lockdown rules - nor do they provide an exhaustive data set. Rather, they are measures of different aspects of mobility, which together, offer an overall impression of how people Londoners are moving around the capital. The information is broken down by use of public transport, pedestrian activity, retail and leisure, and homeworking. Public Transport For the transport measures, we have included data from google, Apple, CityMapper and Transport for London. They measure different aspects of public transport usage - depending on the data source. Each of the lines in the chart below represents a percentage of a pre-pandemic baseline. activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Citymapper Citymapper mobility index 2021-09-05 Compares trips planned and trips taken within its app to a baseline of the four weeks from 6 Jan 2020 7.9% 28% 19% Google Google Mobility Report 2022-10-15 Location data shared by users of Android smartphones, compared time and duration of visits to locations to the median values on the same day of the week in the five weeks from 3 Jan 2020 20.4% 40% 27% TfL Bus Transport for London 2022-10-30 Bus journey ‘taps' on the TfL network compared to same day of the week in four weeks starting 13 Jan 2020 - 34% 24% TfL Tube Transport for London 2022-10-30 Tube journey ‘taps' on the TfL network compared to same day of the week in four weeks starting 13 Jan 2020 - 30% 21% Pedestrian activity With the data we currently have it's harder to estimate pedestrian activity and high street busyness. A few indicators can give us information on how people are making trips out of the house: activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Walking Apple Mobility Index 2021-11-09 estimates the frequency of trips made on foot compared to baselie of 13 Jan '20 22% 47% 36% Parks Google Mobility Report 2022-10-15 Frequency of trips to parks. Changes in the weather mean this varies a lot. Compared to baseline of 5 weeks from 3 Jan '20 30% 55% 41% Retail & Rec Google Mobility Report 2022-10-15 Estimates frequency of trips to shops/leisure locations. Compared to baseline of 5 weeks from 3 Jan '20 30% 55% 41% Retail and recreation In this section, we focus on estimated footfall to shops, restaurants, cafes, shopping centres and so on. activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Grocery/pharmacy Google Mobility Report 2022-10-15 Estimates frequency of trips to grovery shops and pharmacies. Compared to baseline of 5 weeks from 3 Jan '20 32% 55.00% 45.000% Retail/rec Google Mobility Report 2022-10-15 Estimates frequency of trips to shops/leisure locations. Compared to baseline of 5 weeks from 3 Jan '20 32% 55.00% 45.000% Restaurants OpenTable State of the Industry 2022-02-19 London restaurant bookings made through OpenTable 0% 0.17% 0.024% Home Working The Google Mobility Report estimates changes in how many people are staying at home and going to places of work compared to normal. It's difficult to translate this into exact percentages of the population, but changes back towards ‘normal' can be seen to start before any lockdown restrictions were lifted. This value gives a seven day rolling (mean) average to avoid it being distorted by weekends and bank holidays. name Source Latest Baseline Min/max value in Lockdown 1 Min/max value in Lockdown 2 Min/max value in Lockdown 3 Residential Google Mobility Report 2022-10-15 Estimates changes in how many people are staying at home for work. Compared to baseline of 5 weeks from 3 Jan '20 131% 119% 125% Workplaces Google Mobility Report 2022-10-15 Estimates changes in how many people are going to places of work. Compared to baseline of 5 weeks from 3 Jan '20 24% 54% 40% Restriction Date end_date Average Citymapper Average homeworking Work from home advised 17 Mar '20 21 Mar '20 57% 118% Schools, pubs closed 21 Mar '20 24 Mar '20 34% 119% UK enters first lockdown 24 Mar '20 10 May '20 10% 130% Some workers encouraged to return to work 10 May '20 01 Jun '20 15% 125% Schools open, small groups outside 01 Jun '20 15 Jun '20 19% 122% Non-essential businesses re-open 15 Jun '20 04 Jul '20 24% 120% Hospitality reopens 04 Jul '20 03 Aug '20 34% 115% Eat out to help out scheme begins 03 Aug '20 08 Sep '20 44% 113% Rule of 6 08 Sep '20 24 Sep '20 53% 111% 10pm Curfew 24 Sep '20 15 Oct '20 51% 112% Tier 2 (High alert) 15 Oct '20 05 Nov '20 49% 113% Second Lockdown 05 Nov '20 02 Dec '20 31% 118% Tier 2 (High alert) 02 Dec '20 19 Dec '20 45% 115% Tier 4 (Stay at home advised) 19 Dec '20 05 Jan '21 22% 124% Third Lockdown 05 Jan '21 08 Mar '21 22% 122% Roadmap 1 08 Mar '21 29 Mar '21 29% 118% Roadmap 2 29 Mar '21 12 Apr '21 36% 117% Roadmap 3 12 Apr '21 17 May '21 51% 113% Roadmap out of lockdown: Step 3 17 May '21 19 Jul '21 65% 109% Roadmap out of lockdown: Step 4 19 Jul '21 07 Nov '22 68% 107%

Ask

Business Task:

Analyze Cyclistic historical bike trip data to identify trends that explain how annual members and casual riders differ. Transform data into actionable insights and create compelling data visualizations that explain why casual riders should purchase an annual membership. Design a new marketing strategy to convert casual riders into annual members. Use digital media to create effective marketing targeted at casual riders, explaining why it would be beneficial to become an annual member.

Key stakeholders to be considered are Cyclistic customers, Lily Moreno, the Cyclistic marketing analytics team, as well as the Cyclistic executive team. Cyclistic customers include casual riders and members, some with disabilities that use assistive options. Only 30% of riders use Cyclistic to commute to work, while most riders use the bike-share service for leisure. Lily Moreno is the director of marketing. The marketing analytics team helps guide the marketing strategy. The executive team decides whether to approve the recommended marketing program.

Prepare

A description of all data sources used:

Cyclistic bike-share historical trip data is public. It is located on the Divvy website. The .CSV files are sorted by year and month, dating back to 2013. The data is not in real-time, but it is current because it is published every month. Each file has comprehensive data on individual rider ID’s, bike type, time & date of trip, station location information, and whether each rider is a casual rider or a member.

The Divvy website includes the following system data:

Each trip is anonymized and includes: • Trip start day and time • Trip end day and time • Trip start station • Trip end station • Rider type (Member, Single Ride, and Day Pass) The data has been filtered to remove trips that are taken by staff as they service and inspect the system; and any trips that were below 60 seconds in length (potentially false starts or users trying to re-dock a bike to ensure it was secure).

The Data License Agreement explains that Motivate International Inc. (“Motivate”) operates the City of Chicago’s (“City”) Divvy bike-share service. The City of Chicago is the owner of all Divvy data and makes it accessible to the public. Lyft is the operator of Divvy in Chicago. Lyft has a privacy policy that explains their commitment to respecting our personal information.

The Divvy Data License Agreement explains the following:

• License. Motivate hereby grants to you a non-exclusive, royalty-free, limited, perpetual license to access, reproduce, analyze, copy, modify, distribute in your product or service and use the Data for any lawful purpose (“License”).

• No Warranty. THE DATA IS PROVIDED “AS IS,” AS AVAILABLE (AT MOTIVATE’S SOLE DISCRETION) AND AT YOUR SOLE RISK. TO THE MAXIMUM EXTENT PROVIDED BY LAW MOTIVATE DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING THE IMPLIED WARRANTIES OF MERCHANTABILITY FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. MOTIVATE FURTHER DISCLAIMS ANY WARRANTY THAT THE DATA WILL MEET YOUR NEEDS OR WILL BE OR CONTINUE TO BE AVAILABLE, COMPLETE, ACCURATE, TIMELY, SECURE, OR ERROR FREE.

In contrast to all Divvy system data being reliable, the “No Warranty” terms and conditions make it so that there is no guarantee if the data will be “AVAILABLE, COMPLETE, ACCURATE, TIMELY, SECURE, OR ERROR FREE.” The credibility of the data could potentially be negatively affected if they are not held responsible.

Sampling bias could take place because Chicago is significantly affected by weather. There is also an influx of tourists at certain times of the year. Weather and tourism’s effect on data can be accounted for because these influences are constant.

Divvy bike-share consistently providing accurate data is necessary to create and follow through with an effective marketing strategy. All the data is original and owned by the City of Chicago making it a credible source. Lyft is also a credible source because they have the technology to accurately collect data. Although the Data License Agreement states that it has “No Warranty,” the source of the data and the way it is managed makes it credible. Divvy bike-share data is cited using the following:

• Divvy (https://www.divvybikes.com) • Divvy Historical Data (https://divvy-tripdata.s3.amazonaws.com/index.html) • Divvy System Data (https://www.divvybikes.com/system-data) • Divvy Data License Agreement (https://www.divvybikes.com/data-license-agreement) • Lyft’s Privacy Policy (https://www.lyft.com/privacy)

The sources of the data confirm data credibility. The data is detailed and thorough making it effective and efficient for marketing purposes.

Process

Documentation of any cleaning or manipulation of data:

1. Format Cells --> Alignment --> Shrink to Fit top row

2. Data --> Remove Duplicates

3. Create and calculate new ...

📉 GOOGL Daily Stock Prices (2004–2025)

📌 Overview

This dataset offers complete historical daily stock prices for Alphabet Inc. (GOOGL). Spanning from GOOGL’s IPO in 2004 through to the present, it provides a clean and consistent view of stock performance over time.

Whether you’re building predictive models, testing trading strategies, or visualizing long-term price movements, this dataset is ready to use with just a few lines of code.

🔗 Use Together With My Other GOOGL Datasets!

This dataset is part of a larger ecosystem of Google/Alphabet-related datasets I created. You can use them together for powerful, multi-dimensional analysis:

👉 GOOGL Financial Dataset: Quarterly Reports + Daily Prices
Includes quarterly income statements, balance sheets, cash flow statements, and another source of daily prices for cross-verification or model ensembling.

👉 GOOGL Daily News — 2000 to 2025
Provides daily news headlines and summaries related to Alphabet Inc., perfect for sentiment analysis, event-based forecasting, and correlating news with stock prices.

Combine all three datasets to:

• Analyze how news sentiment affects stock performance
• Correlate earnings announcements with market reaction
• Build predictive models that integrate both fundamentals and external signals

📁 Columns

1. open – Opening stock price of the day
2. high – Highest price reached that day
3. low – Lowest price during the day
4. close – Closing price of the trading day
5. volume – Volume of shares traded
date (index) – Trading date

In 2024, Google ranked as the most valuable media and entertainment brand worldwide, with a brand value of 683 billion U.S. dollars. Facebook ranked second, valued at around 167 billion dollars. Part of the Tencent Group, WeChat and v.qq.com (Tencent Video) had a brand value of 56 billion and 17.5 billion dollars, respectively.

RCS data Asia is essential if you’re considering implementing telemarketing or SMS marketing for your organization. It is an accurate and actual mobile number database. As we know Asia is the world’s biggest continent. Also, the maximum number of people use phones in this area. Here the RCS database can play a crucial factor any day. Again, this new type brought by Google message will grow more very soon. So, consider this RCS data Asia as soon as you can and step forward with your business. RCS data Asia offers you clean and fresh contacts to promote your company all across the continent. All our information comes from various trusted sources and is verified by our team. Therefore, this number list offers fantastic features to reach many consumers. Similar to others, this database can be accessed via long-distance and international calls and messages in order to promote your goods and services through telemarketing and cold-calling campaigns. Asia RCS data will help you on so many occasions. Talk to our data expert if you want to build a targeted phone list. You can find all the most recent, accurate lists of mobile phone numbers here. No matter what type of business you own, these contacts will undoubtedly help you. With the help of this dataset, you may grow your company and manage it more productively. In the end, Asia RCS data contains thousands of updated and genuine contacts. This is a one-time payment and an instant downloadable software which can be an Excel or CSV file type. We will provide everything you need for your product advertising. Moreover, the Asia RCS data has 95% correct data collected by our personnel. So, if you are here then buy the library and promote your business and service all over Asia.

Due to changes in the collection and availability of data on COVID-19, this website will no longer be updated. The webpage will no longer be available as of 11 May 2023. On-going, reliable sources of data for COVID-19 are available via the COVID-19 dashboard and the UKHSA

GLA Covid-19 Mobility Report

Since March 2020, London has seen many different levels of restrictions - including three separate lockdowns and many other tiers/levels of restrictions, as well as easing of restrictions and even measures to actively encourage people to go to work, their high streets and local restaurants. This reports gathers data from a number of sources, including google, apple, citymapper, purple wifi and opentable to assess the extent to which these levels of restrictions have translated to a reductions in Londoners' movements.

The data behind the charts below come from different sources. None of these data represent a direct measure of how well people are adhering to the lockdown rules - nor do they provide an exhaustive data set. Rather, they are measures of different aspects of mobility, which together, offer an overall impression of how people Londoners are moving around the capital. The information is broken down by use of public transport, pedestrian activity, retail and leisure, and homeworking.

Public Transport

For the transport measures, we have included data from google, Apple, CityMapper and Transport for London. They measure different aspects of public transport usage - depending on the data source. Each of the lines in the chart below represents a percentage of a pre-pandemic baseline.

https://cdn.datapress.cloud/london/img/dataset/60e5834b-68aa-48d7-a8c5-7ee4781bde05/2025-06-09T20%3A54%3A15/6b096426c4c582dc9568ed4830b4226d.webp" alt="Embedded Image" />

activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Citymapper Citymapper mobility index 2021-09-05 Compares trips planned and trips taken within its app to a baseline of the four weeks from 6 Jan 2020 7.9% 28% 19% Google Google Mobility Report 2022-10-15 Location data shared by users of Android smartphones, compared time and duration of visits to locations to the median values on the same day of the week in the five weeks from 3 Jan 2020 20.4% 40% 27% TfL Bus Transport for London 2022-10-30 Bus journey ‘taps' on the TfL network compared to same day of the week in four weeks starting 13 Jan 2020 - 34% 24% TfL Tube Transport for London 2022-10-30 Tube journey ‘taps' on the TfL network compared to same day of the week in four weeks starting 13 Jan 2020 - 30% 21% Pedestrian activity

With the data we currently have it's harder to estimate pedestrian activity and high street busyness. A few indicators can give us information on how people are making trips out of the house:

https://cdn.datapress.cloud/london/img/dataset/60e5834b-68aa-48d7-a8c5-7ee4781bde05/2025-06-09T20%3A54%3A15/bcf082c07e4d7ff5202012f0a97abc3a.webp" alt="Embedded Image" />

activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Walking Apple Mobility Index 2021-11-09 estimates the frequency of trips made on foot compared to baselie of 13 Jan '20 22% 47% 36% Parks Google Mobility Report 2022-10-15 Frequency of trips to parks. Changes in the weather mean this varies a lot. Compared to baseline of 5 weeks from 3 Jan '20 30% 55% 41% Retail & Rec Google Mobility Report 2022-10-15 Estimates frequency of trips to shops/leisure locations. Compared to baseline of 5 weeks from 3 Jan '20 30% 55% 41% Retail and recreation

In this section, we focus on estimated footfall to shops, restaurants, cafes, shopping centres and so on.

https://cdn.datapress.cloud/london/img/dataset/60e5834b-68aa-48d7-a8c5-7ee4781bde05/2025-06-09T20%3A54%3A16/b62d60f723eaafe64a989e4afec4c62b.webp" alt="Embedded Image" />

activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Grocery/pharmacy Google Mobility Report 2022-10-15 Estimates frequency of trips to grovery shops and pharmacies. Compared to baseline of 5 weeks from 3 Jan '20 32% 55.00% 45.000% Retail/rec <a href="https://ww

📊 Google LLC (GOOGL) Stock Price Dataset – Last 5 Years

📄 About the Dataset

This dataset contains daily historical stock price data for Google LLC (Ticker: GOOGL) covering the last 5 years. It includes essential financial metrics such as opening price, daily high and low, closing price, adjusted close price, and trading volume.

🧰 Key Features:

• Date: The trading day in YYYY-MM-DD format
• Open: Price at market open
• High: Highest price during the trading session
• Low: Lowest price during the trading session
• Close: Price at market close
• Adj Close: Adjusted closing price accounting for dividends and stock splits
• Volume: Number of shares traded

🧪 How to Use This Dataset (Beginner Tips)

Beginners can use this dataset to: - Visualize stock price trends over time
- Calculate daily returns and assess volatility
- Apply moving averages (e.g., 50-day, 200-day) to identify trends
- Analyze trading volume patterns
- Practice time series forecasting and financial modeling

This dataset is ideal for learning stock market analysis, financial data visualization, and time series modeling.

Context:

From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people.

So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community.

Johns Hopkins University has made an excellent dashboard using the affected cases data. Data is extracted from the google sheets associated and made available here.

Edited:

Now data is available as csv files in the Johns Hopkins Github repository. Please refer to the github repository for the Terms of Use details. Uploading it here for using it in Kaggle kernels and getting insights from the broader DS community.

Content

2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC

This dataset has daily level information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this is a time series data and so the number of cases on any given day is the cumulative number.

The data is available from 22 Jan, 2020.

Here’s a polished version suitable for a professional Kaggle dataset description:

Dataset Description

This dataset contains time-series and case-level records of the COVID-19 pandemic. The primary file is covid_19_data.csv, with supporting files for earlier records and individual-level line list data.

Files and Columns

1. covid_19_data.csv (Main File)

This is the primary dataset and contains aggregated COVID-19 statistics by location and date.

• Sno – Serial number of the record
• ObservationDate – Date of the observation (MM/DD/YYYY)
• Province/State – Province or state of the observation (may be missing for some entries)
• Country/Region – Country of the observation
• Last Update – Timestamp (UTC) when the record was last updated (not standardized, requires cleaning before use)
• Confirmed – Cumulative number of confirmed cases on that date
• Deaths – Cumulative number of deaths on that date
• Recovered – Cumulative number of recoveries on that date

2. 2019_ncov_data.csv (Legacy File)

This file contains earlier COVID-19 records. It is no longer updated and is provided only for historical reference. For current analysis, please use covid_19_data.csv.

3. COVID_open_line_list_data.csv

This file provides individual-level case information, obtained from an open data source. It includes patient demographics, travel history, and case outcomes.

4. COVID19_line_list_data.csv

Another individual-level case dataset, also obtained from public sources, with detailed patient-level information useful for micro-level epidemiological analysis.

✅ Use covid_19_data.csv for up-to-date aggregated global trends.

✅ Use the line list datasets for detailed, individual-level case analysis.

Country level datasets:

If you are interested in knowing country level data, please refer to the following Kaggle datasets:

India - https://www.kaggle.com/sudalairajkumar/covid19-in-india

South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset

Italy - https://www.kaggle.com/sudalairajkumar/covid19-in-italy

Brazil - https://www.kaggle.com/unanimad/corona-virus-brazil

USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa

Switzerland - https://www.kaggle.com/daenuprobst/covid19-cases-switzerland

Indonesia - https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases

Acknowledgements :

Johns Hopkins University for making the data available for educational and academic research purposes

MoBS lab - https://www.mobs-lab.org/2019ncov.html

World Health Organization (WHO): https://www.who.int/

DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.

BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/

National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml

China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm

Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html

Macau Government: https://www.ssm.gov.mo/portal/

Taiwan CDC: https://sites.google....

Dataset Description: This dataset is a curated exploration guide that encompasses must visit destinations across India. It serves as an extensive resource for travelers and enthusiasts alike, offering detailed insights into each location's unique characteristics. From historical landmarks to religious shrines and natural wonders, this dataset is a window to India's diverse and rich cultural heritage.

Column Descriptions:

Zone: Geographical region of the place within India, categorizing it into zones like Northern, Southern, etc. State: The state in which the place is located, providing a regional context. City: The city or town where the destination is situated. Name: The name of the tourist spot or landmark. Type: Classification of the place, such as Temple, War Memorial, or Natural Park. Establishment Year: The year in which the place was established or discovered. Time Needed to Visit (hrs): Estimated duration in hours to thoroughly visit the place. Google Review Rating: The average Google review rating for the place, indicative of its popularity and visitor satisfaction. Entrance Fee in INR: The cost of admission in Indian Rupees. Airport within 50km Radius: Indicates if there is an airport within 50 kilometers of the place for accessibility. Weekly Off: The day of the week when the place is closed to visitors. Significance: The importance or role of the place, such as Historical, Religious, or Environmental. DSLR Allowed: Indicates whether visitors are permitted to use DSLR cameras at the location. Number of Google Reviews (in lakhs): The total number of Google reviews in lakhs (hundreds of thousands) that the place has received. Best Time to Visit: Suggested time of the day for visiting the place to have the best experience.

Enhance your data visualization, perform exploratory data analysis (EDA), and apply classification algorithms using this diverse dataset to uncover captivating insights into India's top destinations.

Image attribue : Photo by Flo Maderebner: https://www.pexels.com/photo/couple-sitting-on-rock-beside-lake-238622/

As of April 2024, it was found that men between the ages of 25 and 34 years made up Facebook largest audience, accounting for 18.4 percent of global users. Additionally, Facebook's second largest audience base could be found with men aged 18 to 24 years.

              Facebook connects the world

              Founded in 2004 and going public in 2012, Facebook is one of the biggest internet companies in the world with influence that goes beyond social media. It is widely considered as one of the Big Four tech companies, along with Google, Apple, and Amazon (all together known under the acronym GAFA). Facebook is the most popular social network worldwide and the company also owns three other billion-user properties: mobile messaging apps WhatsApp and Facebook Messenger,
              as well as photo-sharing app Instagram. Facebook usersThe vast majority of Facebook users connect to the social network via mobile devices. This is unsurprising, as Facebook has many users in mobile-first online markets. Currently, India ranks first in terms of Facebook audience size with 378 million users. The United States, Brazil, and Indonesia also all have more than 100 million Facebook users each.

About this Dataset

This dataset offers a comprehensive, up-to-date look at the historical stock performance of Alphabet Inc. (GOOGL), the parent company of Google.

About the Company

Alphabet Inc. is an American multinational technology conglomerate headquartered in Mountain View, California. It was created in 2015 as the parent company of Google and several other companies previously owned by Google. The company is best known for its core search engine, Google, as well as products and services like Android, YouTube, and Gmail. As a key component of the S&P 500, Alphabet's stock performance is a significant indicator of the global technology sector and the internet economy.

Key Features

Daily OHLCV Data: The dataset contains essential Open, High, Low, Close, and Volume metrics for each trading day.

Comprehensive History: Includes data from Google's early trading history to the present, offering a long-term perspective.

Regular Updates: The dataset is designed for regular, automated updates to ensure data freshness for time-sensitive projects.

Data Dictionary

Date: The date of the trading session in YYYY-MM-DD format.

ticker: The standard ticker symbol for Alphabet Inc. on the NASDAQ exchange: 'GOOGL' (Class A).

name: The full name of the company: 'Alphabet Inc.'.

Open: The stock price in USD at the start of the trading session.

High: The highest price reached during the trading day in USD.

Low: The lowest price recorded during the trading day in USD.

Close: The final stock price at market close in USD.

Volume: The total number of shares traded on that day.

Data Collection

The data for this dataset is collected using the yfinance Python library, which pulls information directly from the Yahoo Finance API.

Potential Use Cases

Financial Analysis: Analyze historical price trends, volatility, and trading volume of Google stock.

Machine Learning: Develop and test models for stock price prediction and time series forecasting.

Educational Projects: A perfect real-world dataset for students and data enthusiasts to practice data cleaning, visualization, and modeling.

Overview

Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries.

Content

Over 9000 stations' data are typically available.

The daily elements included in the dataset (as available from each station) are: Mean temperature (.1 Fahrenheit) Mean dew point (.1 Fahrenheit) Mean sea level pressure (.1 mb) Mean station pressure (.1 mb) Mean visibility (.1 miles) Mean wind speed (.1 knots) Maximum sustained wind speed (.1 knots) Maximum wind gust (.1 knots) Maximum temperature (.1 Fahrenheit) Minimum temperature (.1 Fahrenheit) Precipitation amount (.01 inches) Snow depth (.1 inches)

Indicator for occurrence of: Fog, Rain or Drizzle, Snow or Ice Pellets, Hail, Thunder, Tornado/Funnel

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.github_repos.[TABLENAME]. Fork this kernel to get started to learn how to safely manage analyzing large BigQuery datasets.

Acknowledgements

This public dataset was created by the National Oceanic and Atmospheric Administration (NOAA) and includes global data obtained from the USAF Climatology Center. This dataset covers GSOD data between 1929 and present, collected from over 9000 stations. Dataset Source: NOAA

Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source — http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Photo by Allan Nygren on Unsplash

Summary This dataset contains detailed information from every game listed on the NFL's official website, https://www.nfl.com/. It aims to provide a complete record of scores along with play-by-play data across all available seasons. This dataset was created with the hope of being a valuable resource for sports analysts and data scientists interested in American football statistics. The dataset was last updated on 02/10/2025.

Data Collection The data was collected using a custom web scraper, which is openly available for review and further development. You can access the scraper code and documentation at the following GitHub repository: https://github.com/KeoniM/NFL_Scraper.git

Dataset Features For Scores - Season: The NFL season the game belongs to. - Week: Specific week of the NFL season. - GameStatus: Current state or final status of the game. - Day: Day of the week the game was played. - Date: Exact date (month and day) of the game. - AwayTeam: Name of the visiting team. - AwayRecord: Season record of the away team at the time of the game. - AwayScore: Total points scored by the away team. - AwayWin: Boolean indicator if the away team won the game. - HomeTeam: Name of the home team. - HomeRecord: Season record of the home team at the time of the game. - HomeScore: Total points scored by the home team. - HomeWin: Boolean indicator if the home team won the game. - AwaySeeding: Playoff seeding of the away team, if applicable. - HomeSeeding: Playoff seeding of the home team, if applicable. - PostSeason: Boolean indicating whether the game is a postseason match.

Dataset Features For Plays - Season: The NFL season the play belongs to. - Week: Specific week of the NFL season. - Day: Day of the week the play was attempted. - Date: Exact date (month and day) of the play was attempted. - AwayTeam: Name of the visiting team. - HomeTeam: Name of the home team. - Quarter: The quarter of the game the play was attempted. - DriveNumber: The drive number of the quarter the play was attempted. - TeamWithPossession: Team with possession that attempted the play. - IsScoringDrive: Did the drive result in a score. - PlayNumberInDrive: Play number during the drive that the play was attempted. - IsScoringPlay: Did the play result in a score. - PlayOutcome: Short summary of the attempted play. - PlayDescription: In depth summary of the attempted play. - PlayStart: Starting point on the field of the attempted play.

Follow My Data Cleaning Journey If you're interested in following my process of refining and cleaning this dataset, check out my Google Colab notebook on GitHub, where I share ongoing updates and insights: https://github.com/KeoniM/NFL_Data_Cleaning.git. The notebook includes data wrangling techniques, code snippets, and continuous improvements, making this dataset even more valuable for analysis.

Usage Notes This dataset is intended for academic and research purposes. Users are encouraged to attribute data to the source https://www.nfl.com/ when employing this dataset in their projects or publications.

Context

Assume you are a data analyst in an EdTech company. The company’s customer success team works with an objective to help customers get the maximum value from their product by doing deeper dives into the customer's needs, wants and expectations from the product and helping them reach their goals.

The customer success team is aiming to achieve sustainable growth by focusing on retaining the existing users.

Therefore, your team wants to analyze the activity of your existing users and understand their performance, behaviours, and patterns to gain meaningful insights, that help your customer success team take data-informed decisions.

Expected Outcome

1. Brainstorm and identify the right metrics and frame proper questions for analysis. Your analysis should help your customer success team to understand.
   • How is the current retention of the users
   • How are they engaging with the content
   • How efficiently are their discussions being resolved
2. In case you identify any outliers in the data set, make a note of them and exclude them from your analysis.
3. Build the best suitable dashboard presenting your insights.

Your recommendations must be backed by meaningful insights and professional visualizations which will help your customer success team design road maps, strategies, and action items to achieve the goal.

Tools to use:

1. Google Data Studio (preferred), Tableau, Power Bi or any other visualization tool
2. You can use BigQuery SQL if you wish, not mandatory

Overview of the Dataset

The dataset contains the basic details of the enrolled users, their learning resource completion percentages, activities on the platform and the structure of learning resources available on the platform

1.**users_basic_details**: Contains basic details of the enrolled users.

2.**day_wise_user_activity**: Contains the details of the day-wise learning activity of the users. - A user shall have one entry for a lesson in a day.

3.**learning_resource_details**: Contains the details of learning resources offered to the enrolled users - Content is stored in a hierarchical structure: Track → Course →Topic → Lesson. A lesson can be a video, practice, exam, etc. - Example: Tech Foundations → Developer Foundations → Topic 1 → lesson 1

4.**feedback_details**: Contains the feedback details/rating given by the user to a particular lesson. - Feedback rating is given on a scale of 1 to 5, 5 being the highest. - A user can give feedback to the same lesson multiple times.

5.**discussion_details**: Contains the details of the discussions created by the user for a particular lesson.

6.**discussion_comment_details**: Contains the details of the comments posted for the discussions created by the user. - Comments may be posted by mentors or users themselves. - The role of mentors is to guide and help the users by resolving the doubts and issues faced by them related to their learning activity. - A discussion can have multiple comments.

Tables Description

users_basic_details:

• user_id: unique id of the user [string]
• gender: gender of the enrolled user [string]
• current_city: city of residence of the user [string]
• batch_start_datetime: start datetime of the batch, for which the user is enrolled [datetime]
• referral_source: referral channel of the user [string]
• highest_qualification: highest qualification (education details) of the enrolled user [string]

day_wise_user_activity:

• activity_datetime: date and time of learning of the user [datetime]
• user_id: unique id of the user [string]
• lesson_id: unique id of the lesson [string]
• lesson_type: type of the lesson. It can be "SESSION", "PRACTICE", "EXAM" or "PROJECT" [string]
• day_completion_percentage: percent of the lesson completed by the user on a particular day (out of 100%) [float]
  • The completion percentage is calculated by the formula = learnt duration of a lesson on a day/total duration * 100

• overall_completion_percentage: overall completion percentage of the lesson till date by the user (out of 100%) [float]

  • Example: If a user, who started a lesson on Jan 1, ’22 completes the lesson by learning it in parts (10%, 35%, 37%, 18% each day) on 4 different days, Then
    • Jan 1, ‘22 - day_completion_percentage - 10%, overall_completion_percentage - 10%
    • Jan 3, ‘22 - day_completion_percentage - 35%, overall_completion_percentage - 45%
    • Jan 4, ‘22 - day_completion_percentage - 37%, overall_completion_percentage - 82%
    • Jan 6, ‘22 - day_completion_percentage - 18%, overall_completion_percentage - 100%

learning_resource_details:

• track_id: unique id of the track [string]
• track_title: name of the track [string]
• course_id: unique id of the course [string]
• **`...

• Update Frequency: Weekly

Data from this dataset can be downloaded/accessed through this dataset page and Kaggle's API.

Context

Climate Normals are three-decade averages of climatological variables including temperature and precipitation. This product is produced once every 10 years. The 1981–2010 U.S. Climate Normals dataset is the latest release of NCEI’s Climate Normals. This dataset contains daily and monthly Normals of temperature, precipitation, snowfall, heating and cooling degree days, frost/freeze dates, and growing degree days calculated from observations at approximately 9,800 stations operated by NOAA’s National Weather Service.

All data utilized in the computation of the 1981-2010 Climate Normals were taken from the ISD Lite (a subset of derived Integrated Surface Data), the Global Historical Climatology Network-Daily dataset, and standardized monthly temperature data (COOP). These source datasets (including intermediate datasets used in the computation of products) are also archived at the NOAA NCDC.

Content

The comprehensive U.S. Climate Normals dataset includes various derived products including daily air temperature normals (including maximum and minimum temperature normal, heating and cooling degree day normal, and others), precipitation normals (including snowfall and snow depth, percentiles, frequencies and other), and hourly normals (all normal derived from hourly data including temperature, dew point, heat index, wind chill, wind, cloudiness, heating and cooling degree hours, pressure normals). Users can access the data either by product or by station. Included in the dataset is extensive documentation to describe station metadata, filename descriptions, and methodology of producing the data.

Acknowledgements

Dataset Source: NOAA. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source — http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Cover photo by oldskool photography on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

🇺🇸 Alphabet Inc. (GOOGL) Comprehensive Financial Dataset

📌 Overview

Welcome to the GOOGL Financial Dataset! This dataset provides clear and easy-to-use quarterly financial statements (income statement, balance sheet, and cash flow) along with daily historical stock prices.

As a data engineer double majored with economics, I'll personally analyze and provide constructive feedback on all your work using this dataset. Let's dive in and explore Google's financial journey together!

🗃 Files Included

• googl_daily_prices.csv: Historical daily stock prices.
• googl_income_statement.csv: Quarterly income statements.
• googl_balance_sheet.csv: Quarterly balance sheets.
• googl_cash_flow_statement.csv: Quarterly cash flow statements.

📘 About This Dataset

This dataset offers a unique blend of long-term market performance and detailed financial metrics:

• Time Series of Daily Prices: Track the historical performance of GOOGL stock from its early days up until now.
• Quarterly Financial Statements: Dive into the income statements, balance sheets, and cash flow statements that reflect the company’s financial evolution.
• Integrated Insights: Ideal for comprehensive financial analyses, forecasting, model building, and exploring the dynamic interplay between market performance and underlying business operations.

Whether you're building predictive models, performing deep-dive financial analysis, or exploring the evolution of one of the world's most innovative tech giants, this dataset is your go-to resource for clean, well-organized, and rich financial data.

💡 Tips for Using the Dataset

• Visualize Stock Trends: Plot daily prices to quickly understand stock movements.
• Financial Analysis: Compare income, balance sheet, and cash flow data to spot financial trends and health.
• Predictive Modeling: Use this dataset to build forecasting models and predict future performance.
• Combine Data: Merge price data with financial statements to analyze relationships and uncover deeper insights.

🔗 Works Great with My GOOGL News Dataset!

For a more comprehensive financial analysis, pair this dataset with my other Kaggle dataset:
👉 Google (Alphabet Inc.) Daily News — 2000 to 2025

That dataset includes:

• Daily news articles from Finnhub
• Headlines, summaries, sources, and timestamps
• Covering GOOGL from 2000 to 2025

Combining both datasets unlocks powerful analysis such as:

• Correlating news sentiment with stock price movements
• Studying the impact of earnings reports and product launches
• Developing event-driven forecasting models

Together, they give you everything you need for news + financial signal modeling.

📝 Column Descriptions

📈 googl_daily_prices.csv

• date: Trading date.
• 1. open: Opening stock price on the trading day.
• 2. high: Highest stock price on the trading day.
• 3. low: Lowest stock price on the trading day.
• 4. close: Closing stock price on the trading day.
• 5. volume: Number of shares traded on the day.

📊 googl_income_statement.csv

• fiscalDateEnding: Date marking the end of fiscal quarter.
• reportedCurrency: Currency used in reporting (USD).
• grossProfit: Revenue minus the cost of goods sold.
• totalRevenue: Total income generated from operations.
• costOfRevenue: Direct costs attributable to the production of goods.
• costofGoodsAndServicesSold: Costs directly associated with goods sold.
• operatingIncome: Earnings after operating expenses deducted.
• sellingGeneralAndAdministrative: Administrative and general sales costs.
• researchAndDevelopment: Expenses related to research and innovation.
• operatingExpenses: Total operational costs.
• investmentIncomeNet: Net income from investments.
• netInterestIncome: Income earned from interest after deducting interest paid.
• interestIncome: Income generated from interest-bearing investments.
• interestExpense: Expenses from interest payments.
• nonInterestIncome: Income from non-interest-bearing activities.
• otherNonOperatingIncome: Additional income outside regular operations.
• depreciation: Reduction in value of assets over time.
• depreciationAndAmortization: Combined depreciation and amortization costs.
• incomeBeforeTax: Income before taxation.
• incomeTaxExpense: Taxes paid on earnings.
• interestAndDebtExpense: Interest paid on debts.
• netIncomeFromContinuingOperations: Profit from ongoing operations.
• comprehensiveIncomeNetOfTax: Income after comprehensive expenses.
• ebit: Earnings before interest and taxes.
• ebitda: Earnings before interest, taxes, depreciation, and...

The data set and tools can be found at the GitHub link here:https://github.com/groutbrennan/cleaning-data-with-r/tree/master/capstone_data/working_data

This dataset contains: - Data - R markdown - R analysis and cleaning scripts - Final gpplot scatterplot viz image

This dataset was created as part of the Google data analysis course presented by Coursera comparing how people use their smart devices to track their daily health.

After reviewing the initial data, my hypothesis was people who walk more sleep longer.

However after cleaning, transforming, and analyzing the data, I found people who took more steps during the day actually slept less total minutes than people who took lesser steps. After this conclusion I found there was a correlation between more steps taken during the day and less minutes need to sleep at night. However, I don't have proof that this is the causation. Further research will need to be done to confirm that this is the case.

this graph was created in R and Canva :

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F89427211b425b885997029de576bc555%2Fgraph1.gif?generation=1739130158617622&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F7963143f93af16d50bfa667550fbffbd%2Fgraph2.gif?generation=1739130165946944&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F07cb6ebb5596b36e8ede943ca9b7f9b8%2Fgraph3.gif?generation=1739130173200555&alt=media" alt="">

Recent years have witnessed a rapid growth in the use of mobile devices, enabling people to access the Internet in various contexts. More than 77% of Americans now own a smartphone, with an increasing trend in terms of the time people spend on their phones. More recently, with the release of intelligent assistants such as Google Assistant, Apple Siri, and Microsoft Cortana, people are experiencing mobile search through a single voice-based interface. These systems introduce several research challenges. Given that people spend most of their times in apps and, as a consequence, most of their search interactions would be with apps (rather than a browser), one limitation is that users are unable to use a intelligent assistants to search within many apps.