At the end of 2024, Alphabet had 183,323 full-time employees. Up until 2015, these figures were reported as Google employees. The alphabet was created through a corporate restructuring of Google in October 2015 and became the parent company of Google as well as several of its former subsidiaries, including Calico, X, CapitalG and Sidewalk Labs. Google’s popularity Google is one of the most famous internet companies in the world, and in May 2024, the most visited multi-platform website in the United States, with over 278 million U.S. unique visitors during that month alone. The California-based multinational internet company has been delivering digital products and services since its creation in 1996. Due to the popularity of its search engine, the verb “to google” has entered the everyday language and the Oxford Dictionary. In addition to that, the company has also crafted itself as one of the most desirable employers, largely due to the many perks it offers in its offices worldwide. Some of the most appealing aspects of working for Google according to its employees include readily available foods and drinks, good working conditions, and ample communal spaces for relaxing, as well as many health benefits and generous salaries. Google offices and employees As of February 2022, Google and Alphabet had more than 70 offices in over 200 cities throughout 50 around the globe, including Germany, Czechia, Finland, Canada, Mexico, Turkey, and New Zealand. The company’s headquarters, also known as “the Googleplex,” are located in Mountain View, California, while other office locations in American states include New York, Georgia, Texas, Washington D.C., and Massachusetts. As Alphabet, the company employs a total over 182 thousand full-time staff, in addition to many other temporary and internship positions. Per the most recent diversity report published in July 2021, most Google employees were male and only 34 percent were female – a figure that has barely changed since the company started reporting on the diversity of its employees in 2016. Furthermore, as of 2021, women occupied only 28.1 percent of leadership positions and 24.6 percent of tech positions. Although Google has regularly stated that the company is committed to promoting ethnic diversity among its personnel, some 54.4 percent of its U.S. employees are White and only 3.3 percent of employees are Black.

Context: This dataset contains information about employees in a company, including their educational backgrounds, work history, demographics, and employment-related factors. It has been anonymized to protect privacy while still providing valuable insights into the workforce.

Columns:

1. Education: The educational qualifications of employees, including degree, institution, and field of study.

2. Joining Year: The year each employee joined the company, indicating their length of service.

3. City: The location or city where each employee is based or works.

4. Payment Tier: Categorization of employees into different salary tiers.

5. Age: The age of each employee, providing demographic insights.

6. Gender: Gender identity of employees, promoting diversity analysis.

7. Ever Benched: Indicates if an employee has ever been temporarily without assigned work.

8. Experience in Current Domain: The number of years of experience employees have in their current field.

9. Leave or Not: a target column

Usage: This dataset can be used for various HR and workforce-related analyses, including employee retention, salary structure assessments, diversity and inclusion studies, and leave pattern analyses. Researchers, data analysts, and HR professionals can gain valuable insights from this dataset.

Potential Research Questions: 1. What is the distribution of educational qualifications among employees? 2. How does the length of service (Joining Year) vary across different cities? 3. Is there a correlation between Payment Tier and Experience in Current Domain? 4. What is the gender distribution within the workforce? 5. Are there any patterns in leave-taking behavior among employees?

Acknowledgments: We would like to acknowledge the contributions of our HR department in providing this dataset for research and analysis purposes.

https://www.kaggle.com/datasets/arashnic/fitbit original data from this site. I took only parts of the data to combine to practice some skills I learned. My review needs much work to improve and validate. https://www.kaggle.com/arashnic
Description of data source: FitBit Fitness Tracker Data (CC0: Public Domain, dataset made available through Mobius): This Kaggle data set contains personal fitness tracker from thirty fitbit users.

This dataset contains historical stock market data obtained from Yahoo Finance using the yfinance Python library. The dataset spans a specific time period and includes seven key columns: Date, Open, High, Low, close, Adjusted Close, and Volume.

Use case:

This dataset is valuable for a wide range of analyses and applications in finance, economics, and data science. It can be used to: - Perform technical analysis to identify trading patterns and trends. - Develop and backtest trading strategies. - Conduct research on the relationship between trading volume and price movements. - Analyze the impact of macroeconomic events on individual stocks or the overall market. - Train machine learning models for stock price prediction, volatility forecasting, and risk management.

Disclaimer:

This dataset is provided for educational and research purposes only. The accuracy and completeness of the data are not guaranteed.

Thumbnail Photo by Tima Miroshnichenko: https://www.pexels.com/photo/stock-market-illustration-on-the-screen-7567223/

Google Gemini is a chatbot developed by Google as a director competitor to OpenAI’s ChatGPT. Using all of the power and capabilities of Google in artificial intelligence and search, it has been...

The Google Maps dataset is ideal for getting extensive information on businesses anywhere in the world. Easily filter by location, business type, and other factors to get the exact data you need. The Google Maps dataset includes all major data points: timestamp, name, category, address, description, open website, phone number, open_hours, open_hours_updated, reviews_count, rating, main_image, reviews, url, lat, lon, place_id, country, and more.

Between 2023 and 2027, the majority of companies surveyed worldwide expect big data to have a more positive than negative impact on the global job market and employment, with ** percent of the companies reporting the technology will create jobs and * percent expecting the technology to displace jobs. Meanwhile, artificial intelligence (AI) is expected to result in more significant labor market disruptions, with ** percent of organizations expecting the technology to displace jobs and ** percent expecting AI to create jobs.

This is a mutli-modal dataset for restaurants from Google Local (Google Maps). Data includes images and reviews posted by users, as well as metadata for each restaurant.

Don't forget to hit the upvote🙏🙏

🔖 Overview

The Google Play App Reviews dataset contains valuable feedback from users who have reviewed apps on the Google Play Store. This dataset includes both user ratings and detailed comments, making it ideal for sentiment analysis, user experience evaluation, and app performance research.

📚 Columns Description

📊 Key Features

• ✅ Rich Feedback: Includes both ratings and textual feedback from users.
• 🌍 Global Reach: Reviews are collected from users worldwide, providing diverse insights.
• 🔒 Anonymized Data: No personally identifiable information is included.
• ⚙️ Ready for Analysis: Cleaned and pre-processed for immediate use in sentiment analysis and app performance evaluation.

🎯 Potential Use Cases

• Sentiment Analysis: Analyze user sentiment based on reviews and ratings.
• Customer Feedback: Measure user satisfaction and discover areas for improvement.
• App Version Comparison: Evaluate how different versions of the app perform based on user feedback.
• Geographic Insights: Analyze regional differences in app usage and reviews.
• Developer Interaction: Assess the effectiveness of developer responses to user reviews.

🚀 Get Started!

Ready to dive into the world of app feedback and sentiment analysis? Explore the dataset, build models to understand user sentiments, and enhance app experiences based on real feedback.

Happy coding! ✨

Context

NYC Open Data is an opportunity to engage New Yorkers in the information that is produced and used by City government. We believe that every New Yorker can benefit from Open Data, and Open Data can benefit from every New Yorker. Source: https://opendata.cityofnewyork.us/overview/

Content

Thanks to NYC Open Data, which makes public data generated by city agencies available for public use, and Citi Bike, we've incorporated over 150 GB of data in 5 open datasets into Google BigQuery Public Datasets, including:

• Over 8 million 311 service requests from 2012-2016

• More than 1 million motor vehicle collisions 2012-present

• Citi Bike stations and 30 million Citi Bike trips 2013-present

• Over 1 billion Yellow and Green Taxi rides from 2009-present

• Over 500,000 sidewalk trees surveyed decennially in 1995, 2005, and 2015

This dataset is deprecated and not being updated.

Fork this kernel to get started with this dataset.

Acknowledgements

https://opendata.cityofnewyork.us/

https://cloud.google.com/blog/big-data/2017/01/new-york-city-public-datasets-now-available-on-google-bigquery

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - https://data.cityofnewyork.us/ - 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.

By accessing datasets and feeds available through NYC Open Data, the user agrees to all of the Terms of Use of NYC.gov as well as the Privacy Policy for NYC.gov. The user also agrees to any additional terms of use defined by the agencies, bureaus, and offices providing data. Public data sets made available on NYC Open Data are provided for informational purposes. The City does not warranty the completeness, accuracy, content, or fitness for any particular purpose or use of any public data set made available on NYC Open Data, nor are any such warranties to be implied or inferred with respect to the public data sets furnished therein.

The City is not liable for any deficiencies in the completeness, accuracy, content, or fitness for any particular purpose or use of any public data set, or application utilizing such data set, provided by any third party.

Banner Photo by @bicadmedia from Unplash.

Inspiration

On which New York City streets are you most likely to find a loud party?

Can you find the Virginia Pines in New York City?

Where was the only collision caused by an animal that injured a cyclist?

What’s the Citi Bike record for the Longest Distance in the Shortest Time (on a route with at least 100 rides)?

https://cloud.google.com/blog/big-data/2017/01/images/148467900588042/nyc-dataset-6.png" alt="enter image description here"> https://cloud.google.com/blog/big-data/2017/01/images/148467900588042/nyc-dataset-6.png

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?

📊 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.

Introduction: I have chosen to complete a data analysis project for the second course option, Bellabeats, Inc., using a locally hosted database program, Excel for both my data analysis and visualizations. This choice was made primarily because I live in a remote area and have limited bandwidth and inconsistent internet access. Therefore, completing a capstone project using web-based programs such as R Studio, SQL Workbench, or Google Sheets was not a feasible choice. I was further limited in which option to choose as the datasets for the ride-share project option were larger than my version of Excel would accept. In the scenario provided, I will be acting as a Junior Data Analyst in support of the Bellabeats, Inc. executive team and data analytics team. This combined team has decided to use an existing public dataset in hopes that the findings from that dataset might reveal insights which will assist in Bellabeat's marketing strategies for future growth. My task is to provide data driven insights to business tasks provided by the Bellabeats, Inc.'s executive and data analysis team. In order to accomplish this task, I will complete all parts of the Data Analysis Process (Ask, Prepare, Process, Analyze, Share, Act). In addition, I will break each part of the Data Analysis Process down into three sections to provide clarity and accountability. Those three sections are: Guiding Questions, Key Tasks, and Deliverables. For the sake of space and to avoid repetition, I will record the deliverables for each Key Task directly under the numbered Key Task using an asterisk (*) as an identifier.

Section 1 - Ask: A. Guiding Questions: Who are the key stakeholders and what are their goals for the data analysis project? What is the business task that this data analysis project is attempting to solve?

B. Key Tasks: Identify key stakeholders and their goals for the data analysis project *The key stakeholders for this project are as follows: -Urška Sršen and Sando Mur - co-founders of Bellabeats, Inc. -Bellabeats marketing analytics team. I am a member of this team. Identify the business task. *The business task is: -As provided by co-founder Urška Sršen, the business task for this project is to gain insight into how consumers are using their non-BellaBeats smart devices in order to guide upcoming marketing strategies for the company which will help drive future growth. Specifically, the researcher was tasked with applying insights driven by the data analysis process to 1 BellaBeats product and presenting those insights to BellaBeats stakeholders.

Section 2 - Prepare: A. Guiding Questions: Where is the data stored and organized? Are there any problems with the data? How does the data help answer the business question?

B. Key Tasks: Research and communicate the source of the data, and how it is stored/organized to stakeholders. *The data source used for our case study is FitBit Fitness Tracker Data. This dataset is stored in Kaggle and was made available through user Mobius in an open-source format. Therefore, the data is public and available to be copied, modified, and distributed, all without asking the user for permission. These datasets were generated by respondents to a distributed survey via Amazon Mechanical Turk reportedly (see credibility section directly below) between 03/12/2016 thru 05/12/2016. *Reportedly (see credibility section directly below), thirty eligible Fitbit users consented to the submission of personal tracker data, including output related to steps taken, calories burned, time spent sleeping, heart rate, and distance traveled. This data was broken down into minute, hour, and day level totals. This data is stored in 18 CSV documents. I downloaded all 18 documents into my local laptop and decided to use 2 documents for the purposes of this project as they were files which had merged activity and sleep data from the other documents. All unused documents were permanently deleted from the laptop. The 2 files used were: -sleepDaymerged.csv -dailyActivitymerged.csv Identify and communicate to stakeholders any problems found with the data related to credibility and bias. *As will be more specifically presented in the Process section, the data seems to have credibility issues related to the reported time frame of the data collected. The metadata seems to indicate that the data collected covered roughly 2 months of FitBit tracking. However, upon my initial data processing, I found that only 1 month of data was reported. *As will be more specifically presented in the Process section, the data has credibility issues related to the number of individuals who reported FitBit data. Specifically, the metadata communicates that 30 individual users agreed to report their tracking data. My initial data processing uncovered 33 individual IDs in the dailyActivity_merged dataset. *Due to the small number of participants (...

if you’re looking for a job in data analytics, you’ll need a portfolio to demonstrate your expertise. Of course, if you’re new to data analytics, you probably don’t have much expertise! Not to worry. The fact you might not have worked on a paid project yet doesn’t mean you can’t whip up a compelling portfolio using some practice datasets.

Fortunately, the Internet is awash with these, most of which are completely free to download (thanks to the open data initiative). In this post, we’ll highlight a few first-rate repositories where you can find data on everything from business to finance, planetary science and crime.

Prefer to watch this information over reading it? Check out this video on dataset resources, presented by our very own in-house data scientist, Tom!

It seems we turn to Google for everything these days, and data is no exception. Launched in 2018, Google Dataset Search is like Google’s standard search engine, but strictly for data.

While it’s not the best tool if you prefer to browse, if you have a particular topic or keyword in mind, it won’t disappoint. Google Dataset Search aggregates data from external sources, providing a clear summary of what’s available, a description of the data, who it’s provided by, and when it was last updated. It’s an excellent place to start.

"This dataset forms a vital component of my Google Data Analytics Capstone Project, representing a culmination of the skills and knowledge I've acquired throughout the program. As a responsible data analyst, I've meticulously cited the sources and references in the accompanying documentation, ensuring the highest data integrity and transparency standards.

I welcome and encourage any constructive feedback and reviews to enhance the quality and depth of my analysis. Collaborative efforts are at the heart of data analytics, and your insights and suggestions can play a pivotal role in refining the outcomes of this project.

Exploring this dataset has been an illuminating journey, and I'm excited to share my findings and insights with the Kaggle community. Stay tuned for a comprehensive analysis that deepens the data, uncovers patterns, and provides valuable insights. Together, we can harness the power of data to drive meaningful change and innovation.

Thank you for joining me on this data-driven adventure!"

Don't forget to upvote, comment, and follow if you are using this dataset. If you have any questions about the dataset I uploaded, feel free to leave them in the comments. Thank you! :)

Jangan lupa untuk upvote, comment, follow jika anda menggunakan dataset ini, dan jika ada pertanyaan mengenai dataset yang saya upload, silahkan tinggalkan di comment. Terima kasih :)

Column Descriptions (English) 1. reviewId: A unique ID for each user review. 2. userName: The name of the user who submitted the review. 3. userImage: The URL of the user's profile picture. 4. content: The text content of the review provided by the user. 5. score: The review score given by the user, typically on a scale of 1-5. 6. thumbsUpCount: The number of likes (thumbs up) received by the review. 7. reviewCreatedVersion: The app version used by the user when creating the review (not always available). 8. at: The date and time when the review was submitted. 9. replyContent: The developer's response to the review (no data available in this column). 10. repliedAt: The date and time when the developer's response was submitted (no data available in this column). 11. appVersion: The app version used by the user when submitting the review (not always available).

Deskripsi Kolom (Bahasa Indonesia) 1. reviewId: ID unik untuk setiap ulasan yang diberikan pengguna. 2. userName: Nama pengguna yang memberikan ulasan. 3. userImage: URL gambar profil pengguna yang memberikan ulasan. 4. content: Isi teks ulasan yang diberikan oleh pengguna. 5. score: Skor ulasan yang diberikan pengguna, biasanya dalam skala 1-5. 6. thumbsUpCount: Jumlah suka (thumbs up) yang diterima oleh ulasan tersebut. 7. reviewCreatedVersion: Versi aplikasi yang digunakan pengguna saat membuat ulasan (tidak selalu tersedia). 8. at: Tanggal dan waktu saat ulasan dibuat. 9. replyContent: Isi balasan dari pengembang aplikasi terhadap ulasan (tidak ada data dalam kolom ini). 10. repliedAt: Tanggal dan waktu saat balasan dari pengembang diberikan (tidak ada data dalam kolom ini). 11. appVersion: Versi aplikasi yang digunakan pengguna saat memberikan ulasan (tidak selalu tersedia).

Context

Business Task: Review the data on how consumers are using non-Bellabeat smart devices to point out any trends. With the insights, analyze how those trends could be applied to one of Bellabeat’s products. Use the top usage trends for a marketing strategy to drive growth for Bellabeat.

Content

The data shows the smart device is used to track minute-level output for physical activity, heart rate, and sleep monitoring. It includes information about daily activity, steps, and heart rate that can be used to explore users’ habits.

Acknowledgements

Resources - Kaggle Fitbit Fitness Tracker Data by Mobius Furberg, Robert; Brinton, Julia; Keating, Michael ; Ortiz, Alexa https://zenodo.org/record/53894#.YMoUpnVKiP9 https://bellabeat.com/ https://www.omnicalculator.com/sports/met-minutes-per-week

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Every day, millions of searches reveal what’s capturing people’s attention. This dataset tracks daily trending Google searches in the United States, collected automatically via the Google Trends API since 19 September 2025.

A lightweight pipeline handles the workflow: - A scheduled script pulls the latest trending topics once a day. - The data is stored in Google BigQuery, where it undergoes only basic cleaning (removing duplicates, normalizing dates). Beyond that, the dataset is intentionally left “as-is” to reflect real-world data pipelines and give Kagglers room to tackle the imperfections themselves. - Fresh records are published directly to Kaggle, ensuring the dataset is always current.

This resource is designed for anyone who wants to explore what’s “buzzing” in the US: - Content creators can spot ideas that audiences are actively searching for. - Data analysts can visualize patterns in search interest over time. - Students and learners can practice building queries, dashboards, and predictive models with live, evolving data. - Researchers can study how cultural moments and events show up in online behavior.

With daily updates and historical records preserved in BigQuery, this dataset balances realism and usability: clean enough to work with, but still raw enough to present a genuine challenge.

Context

The dataset is Cyclistic’s historical trip data that will be used to analyze and identify trends. The dataset includes 12 months of Cyclistic trip data. (Note: Cyclistic is a fictional company and the dataset was provided to you someone to answer the business questions. The data has been made available by Motivate International Inc.)

Content

The dataset includes 12 months of Cyclistic trip data from August, 2020 through July 2021. Each row of data is a unique ride with defined start and end data including date, time, station name and ID, lat/long coordinates, the type of bike and whether or not the user was a casual rider or an annual member.

Acknowledgements

The project is part of the Google Data Analytics Certification through Coursera with the dataset provided by Motivate International Inc.

Inspiration

The purpose of reviewing this dataset is to find differences in how casual riders and annual members use of the system differs. Using this information the marketing department can come up with a campaign to convert casual riders into annual members. Cost is not involved in the analysis as leadership had already determined that annual members are more profitable than casual riders.

Cyclistic Trip Data

This dataset contains rows of cyclistic trip data, collected from Index of bucket "divvy-tripdata". The dataset includes 10 columns, each representing a different attribute or feature of the data.

The data has been preprocessed to remove any missing values, duplicates, or other inconsistencies. It is ready for use in a wide range of data analysis and machine learning tasks.

The dataset includes the following columns:

• rideable_type : Type of bicycle used for rides
• member_casual : Type of members performing the drive
• ride_length : Bicycle ride length in minutes
• date : Realized date of cycling ride
• day_of_week : Days of the week when the bike ride takes place
• month : Months of riding
• day : Days of the month in which the ride was carried out
• year : Year in which the ride took place
• hour : Hours when riding is carried out
• season : Seasons in which the ride takes place

This dataset contains data from the capstone project section of the google data analytics course on the coursera platform. This dataset has been cleaned and processed, ready for the user to analyze. We hope that it will help everyone who takes this course and tries to make the preparation process related to the data in the last stage.