About the Google Stock Price Dataset

The Google Stock Price Dataset consists of two CSV (Comma Separated Values) files containing historical stock price data for training and evaluation. Each row in the dataset represents a trading day, and the columns provide various information related to Google's stock for that day.

Columns:

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

Open: The opening price of Google's stock on that trading day.

High: The highest price reached during the trading day.

Low: The lowest price reached during the trading day.

Close: The closing price of Google's stock on that trading day.

Adj Close: The adjusted closing price, accounting for any corporate actions (e.g., stock splits, dividends) that may affect the stock's value.

Volume: The trading volume, representing the number of shares traded on that trading day.

Time Period: The train dataset spans from January 1, 2010, to December 31, 2022, providing twelve years of daily stock price information for model training. The test dataset spans from January 1, 2023, to July 30, 2023, providing seven month of daily stock price data for model evaluation.

Data Source:

The dataset was collected from Yahoo Finance (finance.yahoo.com), a reputable and widely-used financial data platform.

Use Case:

The Google Stock Price Dataset can be utilized for various purposes, such as predicting future stock prices, analyzing historical stock trends, and building machine learning models for financial forecasting.

Potential Applications:

Time Series Analysis: Explore stock price patterns and seasonality. Financial Modeling: Develop predictive models to forecast stock prices. Algorithmic Trading: Create trading strategies based on historical stock data. Risk Management: Assess potential risks and volatilities in the stock market.

Citation:

If you use this dataset in your research or analysis, please provide proper attribution and citation to acknowledge the source.

License: This dataset is provided under the Creative Commons CC0 1.0 Universal (CC0 1.0) Public Domain Dedication, making it freely available for use without any restrictions or attribution requirements.

[ADVISORY] IMPORTANT

Instructions for citation:

If you use this dataset anywhere in your work, kindly cite as the below: L. Gupta, "Google Play Store Apps," Feb 2019. [Online]. Available: https://www.kaggle.com/lava18/google-play-store-apps

Context

While many public datasets (on Kaggle and the like) provide Apple App Store data, there are not many counterpart datasets available for Google Play Store apps anywhere on the web. On digging deeper, I found out that iTunes App Store page deploys a nicely indexed appendix-like structure to allow for simple and easy web scraping. On the other hand, Google Play Store uses sophisticated modern-day techniques (like dynamic page load) using JQuery making scraping more challenging.

Content

Each app (row) has values for catergory, rating, size, and more.

Acknowledgements

This information is scraped from the Google Play Store. This app information would not be available without it.

Inspiration

The Play Store apps data has enormous potential to drive app-making businesses to success. Actionable insights can be drawn for developers to work on and capture the Android market!

Google’s energy consumption has increased over the last few years, reaching 25.9 terawatt hours in 2023, up from 12.8 terawatt hours in 2019. The company has made efforts to make its data centers more efficient through customized high-performance servers, using smart temperature and lighting, advanced cooling techniques, and machine learning. Datacenters and energy Through its operations, Google pursues a more sustainable impact on the environment by creating efficient data centers that use less energy than the average, transitioning towards renewable energy, creating sustainable workplaces, and providing its users with the technological means towards a cleaner future for the future generations. Through its efficient data centers, Google has also managed to divert waste from its operations away from landfills. Reducing Google’s carbon footprint Google’s clean energy efforts is also related to their efforts to reduce their carbon footprint. Since their commitment to using 100 percent renewable energy, the company has met their targets largely through solar and wind energy power purchase agreements and buying renewable power from utilities. Google is one of the largest corporate purchasers of renewable energy in the world.

Author: Víctor Yeste. Universitat Politècnica de Valencia.The object of this study is the design of a cybermetric methodology whose objectives are to measure the success of the content published in online media and the possible prediction of the selected success variables.In this case, due to the need to integrate data from two separate areas, such as web publishing and the analysis of their shares and related topics on Twitter, has opted for programming as you access both the Google Analytics v4 reporting API and Twitter Standard API, always respecting the limits of these.The website analyzed is hellofriki.com. It is an online media whose primary intention is to solve the need for information on some topics that provide daily a vast number of news in the form of news, as well as the possibility of analysis, reports, interviews, and many other information formats. All these contents are under the scope of the sections of cinema, series, video games, literature, and comics.This dataset has contributed to the elaboration of the PhD Thesis:Yeste Moreno, VM. (2021). Diseño de una metodología cibermétrica de cálculo del éxito para la optimización de contenidos web [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/176009Data have been obtained from each last-minute news article published online according to the indicators described in the doctoral thesis. All related data are stored in a database, divided into the following tables:tesis_followers: User ID list of media account followers.tesis_hometimeline: data from tweets posted by the media account sharing breaking news from the web.status_id: Tweet IDcreated_at: date of publicationtext: content of the tweetpath: URL extracted after processing the shortened URL in textpost_shared: Article ID in WordPress that is being sharedretweet_count: number of retweetsfavorite_count: number of favoritestesis_hometimeline_other: data from tweets posted by the media account that do not share breaking news from the web. Other typologies, automatic Facebook shares, custom tweets without link to an article, etc. With the same fields as tesis_hometimeline.tesis_posts: data of articles published by the web and processed for some analysis.stats_id: Analysis IDpost_id: Article ID in WordPresspost_date: article publication date in WordPresspost_title: title of the articlepath: URL of the article in the middle webtags: Tags ID or WordPress tags related to the articleuniquepageviews: unique page viewsentrancerate: input ratioavgtimeonpage: average visit timeexitrate: output ratiopageviewspersession: page views per sessionadsense_adunitsviewed: number of ads viewed by usersadsense_viewableimpressionpercent: ad display ratioadsense_ctr: ad click ratioadsense_ecpm: estimated ad revenue per 1000 page viewstesis_stats: data from a particular analysis, performed at each published breaking news item. Fields with statistical values can be computed from the data in the other tables, but total and average calculations are saved for faster and easier further processing.id: ID of the analysisphase: phase of the thesis in which analysis has been carried out (right now all are 1)time: "0" if at the time of publication, "1" if 14 days laterstart_date: date and time of measurement on the day of publicationend_date: date and time when the measurement is made 14 days latermain_post_id: ID of the published article to be analysedmain_post_theme: Main section of the published article to analyzesuperheroes_theme: "1" if about superheroes, "0" if nottrailer_theme: "1" if trailer, "0" if notname: empty field, possibility to add a custom name manuallynotes: empty field, possibility to add personalized notes manually, as if some tag has been removed manually for being considered too generic, despite the fact that the editor put itnum_articles: number of articles analysednum_articles_with_traffic: number of articles analysed with traffic (which will be taken into account for traffic analysis)num_articles_with_tw_data: number of articles with data from when they were shared on the media’s Twitter accountnum_terms: number of terms analyzeduniquepageviews_total: total page viewsuniquepageviews_mean: average page viewsentrancerate_mean: average input ratioavgtimeonpage_mean: average duration of visitsexitrate_mean: average output ratiopageviewspersession_mean: average page views per sessiontotal: total of ads viewedadsense_adunitsviewed_mean: average of ads viewedadsense_viewableimpressionpercent_mean: average ad display ratioadsense_ctr_mean: average ad click ratioadsense_ecpm_mean: estimated ad revenue per 1000 page viewsTotal: total incomeretweet_count_mean: average incomefavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesterms_ini_num_tweets: total tweets on the terms on the day of publicationterms_ini_retweet_count_total: total retweets on the terms on the day of publicationterms_ini_retweet_count_mean: average retweets on the terms on the day of publicationterms_ini_favorite_count_total: total of favorites on the terms on the day of publicationterms_ini_favorite_count_mean: average of favorites on the terms on the day of publicationterms_ini_followers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the terms on the day of publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms on the day of publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who spoke about the terms on the day of publicationterms_ini_user_age_mean: average age in days of users who have spoken of the terms on the day of publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms on the day of publicationterms_end_num_tweets: total tweets on terms 14 days after publicationterms_ini_retweet_count_total: total retweets on terms 14 days after publicationterms_ini_retweet_count_mean: average retweets on terms 14 days after publicationterms_ini_favorite_count_total: total bookmarks on terms 14 days after publicationterms_ini_favorite_count_mean: average of favorites on terms 14 days after publicationterms_ini_followers_talking_rate: ratio of media Twitter account followers who have recently posted a tweet talking about the terms 14 days after publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms 14 days after publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who have spoken about the terms 14 days after publicationterms_ini_user_age_mean: the average age in days of users who have spoken of the terms 14 days after publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms 14 days after publication.tesis_terms: data of the terms (tags) related to the processed articles.stats_id: Analysis IDtime: "0" if at the time of publication, "1" if 14 days laterterm_id: Term ID (tag) in WordPressname: Name of the termslug: URL of the termnum_tweets: number of tweetsretweet_count_total: total retweetsretweet_count_mean: average retweetsfavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesfollowers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the termuser_num_followers_mean: average followers of users who were talking about the termuser_num_tweets_mean: average number of tweets published by users who were talking about the termuser_age_mean: average age in days of users who were talking about the termurl_inclusion_rate: URL inclusion ratio

Ethereum is a crypto currency which leverages blockchain technology to store transactions in a distributed ledger. A blockchain is an ever-growing "tree" of blocks, where each block contains a number of transactions. To learn more, read the "Ethereum in BigQuery: a Public Dataset for smart contract analytics" blog post by Google Developer Advocate Allen Day. This dataset is part of a larger effort to make cryptocurrency data available in BigQuery through the Google Cloud Public Datasets program.

How much time do people spend on social media? As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

This is the data of a social media platform of an organization. You have been hired by the organization & given their social media data to analyze, visualize and prepare a report on it.

You are required to prepare a neat notebook on it using Jupyter Notebook/Jupyter Lab or Google Colab. Then, zip everything including the notebook file (.ipynb file) and the dataset. Finally, upload through the google forms link stated below. The notebook should be neat, containing codes with details regarding your code, visualizations, and description of your purpose of doing each task.

You are suggested but not limited to go through the general steps like -> Data Cleaning, Data preparation, Exploratory Data Analysis(EDA), Correlations finding, Feature extraction, and more. (There is no limit to your skills and ideas)

After doing what needs to be done, you are to give your organization insights and facts. For example, are they reaching more audiences on weekends? Is posting content on the weekdays turn out to be more effective? Is posting many contents on the same day make more sense? Or, should they post content regularly and keep day-to-day consistency? Did you find any trend patterns in the data? What are your advice after completing the analysis? Mention them clearly at the end of the Notebook. (These are just a few examples, your findings may be entirely different and that is totally acceptable. )

Note that, we will value clear documentation which states clear insights from analysis of data & visualizations, more than anything else. It will not matter how complex methods are you applying if it eventually does not find anything useful.

The data for this study uses real-time images taken by the camera as well as those available in the public domain. A total of 2.4k images were collected. Efforts were put in by the authors to make sure that the dataset was generalized and consisted of multifarious images taken at different times of the day and lighting conditions to make way for more robust detection. Images were also selected to have a wide variety of vehicle models in different real-time scenarios. All given classes unless mentioned otherwise are a mixture of a collection of scraped images from the web using a custom python script, taken from the Google V6 image repository or a collection of camera clicked pictures to bring diversity to the database: • Car • Auto Rickshaw • Pedestrian* • Potholes • Bikes • Traffic Light As the dataset is too large to publish, the results and the validation set have been published here. The complete dataset can be found at https://drive.google.com/drive/folders/1NgR54s_E-UjsUoqvRZrtR_BawMkVkzwo?usp=sharing.

As global communities responded to COVID-19, we heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps would be helpful as they made critical decisions to combat COVID-19. These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

Zilliqa is a blockchain platform designed around the concept of sharding. Sharding means dividing the network into several smaller component networks that are able to process transactions in parallel. Cryptocurrency markets are becoming more accessible and analysis is increasing by the day. Gone will be the days when investors jump into crypto to become overnight millionaires and it is the reason some ICOs are doing well while others have been losing value since January. Zilliqa platform is among the few projects that seem to be gaining favor from different facets in the financial services sector. Zilliqa aims at maximizing scalability within blockchain tech. The platform has been developed using sharding tech in order to interlink more networks. This dataset is part of a larger effort to make cryptocurrency data available in BigQuery through the Google Cloud Public Datasets program. The program is hosting several cryptocurrency datasets, with plans to both expand offerings to include additional cryptocurrencies and reduce the latency of updates. You can find these datasets by searching "cryptocurrency" in GCP Marketplace. For analytics interoperability, we designed a unified schema that allows all Bitcoin-like datasets to share queries. Interested in learning more about how the data from these blockchains were brought into BigQuery? Looking for more ways to analyze the data? Check out the Google Cloud Big Data blog post and try the sample queries below to get started. 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

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.

The capstone was completed in Power Bi. Due to restrictions on sharing, I've made a powerpoint of the report that demonstrates the data in use and the insight gained from the research.

dailyActivity_merged contains a summary of daily activity such as total distance, intensities (i.e., very active, sedentary), and total minutes in intensities.

There is a discrepancy between the total distance and the sum of VeryActiveDistance, ModeratelyActiveDistance, LightActiveDistance and SedentaryActiveDistance. With an average of 5.489702122 miles in tracker distance, this can be off on average up to .077053 miles or 370 feet.

1.6% (15/940) of tracker distances listed do not match total distance. I will need clarification between total distance and tracker distance. For my report, I will be using total distance.

Aggregated daily data does not contain null values. No assumptions need to be made based on this.

98/940 records are <= 500 feet. 77/98 have a total of 0 steps and the remaining data is 0. A filter has been added to void records where total steps are <= 500.

I removed 5/12/2016 due to lack of sufficient user data.

dailyActivity_merged contains the same calories as dailyCalories_merged when using activity date and ID as a primary key.

dailyActivity_merged does not contain the same calories as hourlyCalories_merged when summing the calories per day in the hourly table.

PseudoData contains mock data I created for users. Pseudo names were created for the ID's to make data relatable for the audience. Teams were generated in the event the analysis discussed this possibility.

heartrate_seconds_merged contains heart rate value every 15 seconds over time.

I removed 5/12/2016 due to lack of sufficient user data. Events were averaged to the nearest hour. The windows function lag() was used to find time between events to determine usage. The visuals will show lag, or time when the device is not used, if it's greater than the total charge time, 2 hours.

hourlyCalories_merged contains calories per hour per ID. The Date and Time were separated into two columns.

Context

An education company named X Education sells online courses to industry professionals. On any given day, many professionals who are interested in the courses land on their website and browse for courses.

The company markets its courses on several websites and search engines like Google. Once these people land on the website, they might browse the courses or fill up a form for the course or watch some videos. When these people fill up a form providing their email address or phone number, they are classified to be a lead. Moreover, the company also gets leads through past referrals. Once these leads are acquired, employees from the sales team start making calls, writing emails, etc. Through this process, some of the leads get converted while most do not. The typical lead conversion rate at X education is around 30%.

Now, although X Education gets a lot of leads, its lead conversion rate is very poor. For example, if, say, they acquire 100 leads in a day, only about 30 of them are converted. To make this process more efficient, the company wishes to identify the most potential leads, also known as ‘Hot Leads’. If they successfully identify this set of leads, the lead conversion rate should go up as the sales team will now be focusing more on communicating with the potential leads rather than making calls to everyone.

There are a lot of leads generated in the initial stage (top) but only a few of them come out as paying customers from the bottom. In the middle stage, you need to nurture the potential leads well (i.e. educating the leads about the product, constantly communicating, etc. ) in order to get a higher lead conversion.

X Education wants to select the most promising leads, i.e. the leads that are most likely to convert into paying customers. The company requires you to build a model wherein you need to assign a lead score to each of the leads such that the customers with higher lead score h have a higher conversion chance and the customers with lower lead score have a lower conversion chance. The CEO, in particular, has given a ballpark of the target lead conversion rate to be around 80%.

Content

Variables Description * Prospect ID - A unique ID with which the customer is identified. * Lead Number - A lead number assigned to each lead procured. * Lead Origin - The origin identifier with which the customer was identified to be a lead. Includes API, Landing Page Submission, etc. * Lead Source - The source of the lead. Includes Google, Organic Search, Olark Chat, etc. * Do Not Email -An indicator variable selected by the customer wherein they select whether of not they want to be emailed about the course or not. * Do Not Call - An indicator variable selected by the customer wherein they select whether of not they want to be called about the course or not. * Converted - The target variable. Indicates whether a lead has been successfully converted or not. * TotalVisits - The total number of visits made by the customer on the website. * Total Time Spent on Website - The total time spent by the customer on the website. * Page Views Per Visit - Average number of pages on the website viewed during the visits. * Last Activity - Last activity performed by the customer. Includes Email Opened, Olark Chat Conversation, etc. * Country - The country of the customer. * Specialization - The industry domain in which the customer worked before. Includes the level 'Select Specialization' which means the customer had not selected this option while filling the form. * How did you hear about X Education - The source from which the customer heard about X Education. * What is your current occupation - Indicates whether the customer is a student, umemployed or employed. * What matters most to you in choosing this course An option selected by the customer - indicating what is their main motto behind doing this course. * Search - Indicating whether the customer had seen the ad in any of the listed items. * Magazine
* Newspaper Article * X Education Forums
* Newspaper * Digital Advertisement * Through Recommendations - Indicates whether the customer came in through recommendations. * Receive More Updates About Our Courses - Indicates whether the customer chose to receive more updates about the courses. * Tags - Tags assigned to customers indicating the current status of the lead. * Lead Quality - Indicates the quality of lead based on the data and intuition the employee who has been assigned to the lead. * Update me on Supply Chain Content - Indicates whether the customer wants updates on the Supply Chain Content. * Get updates on DM Content - Indicates whether the customer wants updates on the DM Content. * Lead Profile - A lead level assigned to each customer based on their profile. * City - The city of the customer. * Asymmetric Activity Index - An index and score assigned to each customer based on their activity and their profile * Asymmetric Profile Index * Asymmetric Activity Score * Asymmetric Profile Score
* I agree to pay the amount through cheque - Indicates whether the customer has agreed to pay the amount through cheque or not. * a free copy of Mastering The Interview - Indicates whether the customer wants a free copy of 'Mastering the Interview' or not. * Last Notable Activity - The last notable activity performed by the student.

Acknowledgements

UpGrad Case Study

Inspiration

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

Context

Stack Overflow is the largest online community for programmers to learn, share their knowledge, and advance their careers.

Content

Updated on a quarterly basis, this BigQuery dataset includes an archive of Stack Overflow content, including posts, votes, tags, and badges. This dataset is updated to mirror the Stack Overflow content on the Internet Archive, and is also available through the Stack Exchange Data Explorer.

Fork this kernel to get started with this dataset.

Acknowledgements

Dataset Source: https://archive.org/download/stackexchange

https://bigquery.cloud.google.com/dataset/bigquery-public-data:stackoverflow

https://cloud.google.com/bigquery/public-data/stackoverflow

Banner Photo by Caspar Rubin from Unplash.

Inspiration

What is the percentage of questions that have been answered over the years?

What is the reputation and badge count of users across different tenures on StackOverflow?

What are 10 of the “easier” gold badges to earn?

Which day of the week has most questions answered within an hour?

Abstract The Medical Information Mart for Intensive Care (MIMIC)-IV database is comprised of deidentified electronic health records for patients admitted to the Beth Israel Deaconess Medical Center. Access to MIMIC-IV is limited to credentialed users. Here, we have provided an openly-available demo of MIMIC-IV containing a subset of 100 patients. The dataset includes similar content to MIMIC-IV, but excludes free-text clinical notes. The demo may be useful for running workshops and for assessing whether the MIMIC-IV is appropriate for a study before making an access request.

Background The increasing adoption of digital electronic health records has led to the existence of large datasets that could be used to carry out important research across many areas of medicine. Research progress has been limited, however, due to limitations in the way that the datasets are curated and made available for research. The MIMIC datasets allow credentialed researchers around the world unprecedented access to real world clinical data, helping to reduce the barriers to conducting important medical research. The public availability of the data allows studies to be reproduced and collaboratively improved in ways that would not otherwise be possible.

Methods First, the set of individuals to include in the demo was chosen. Each person in MIMIC-IV is assigned a unique subject_id. As the subject_id is randomly generated, ordering by subject_id results in a random subset of individuals. We only considered individuals with an anchor_year_group value of 2011 - 2013 or 2014 - 2016 to ensure overlap with MIMIC-CXR v2.0.0. The first 100 subject_id who satisfied the anchor_year_group criteria were selected for the demo dataset.

All tables from MIMIC-IV were included in the demo dataset. Tables containing patient information, such as emar or labevents, were filtered using the list of selected subject_id. Tables which do not contain patient level information were included in their entirety (e.g. d_items or d_labitems). Note that all tables which do not contain patient level information are prefixed with the characters 'd_'.

Deidentification was performed following the same approach as the MIMIC-IV database. Protected health information (PHI) as listed in the HIPAA Safe Harbor provision was removed. Patient identifiers were replaced using a random cipher, resulting in deidentified integer identifiers for patients, hospitalizations, and ICU stays. Stringent rules were applied to structured columns based on the data type. Dates were shifted consistently using a random integer removing seasonality, day of the week, and year information. Text fields were filtered by manually curated allow and block lists, as well as context-specific regular expressions. For example, columns containing dose values were filtered to only contain numeric values. If necessary, a free-text deidentification algorithm was applied to remove PHI from free-text. Results of this algorithm were manually reviewed and verified to remove identified PHI.

Data Description MIMIC-IV is a relational database consisting of 26 tables. For a detailed description of the database structure, see the MIMIC-IV Clinical Database page [1] or the MIMIC-IV online documentation [2]. The demo shares an identical schema and structure to the equivalent version of MIMIC-IV.

Data files are distributed in comma separated value (CSV) format following the RFC 4180 standard [3]. The dataset is also made available on Google BigQuery. Instructions to accessing the dataset on BigQuery are provided on the online MIMIC-IV documentation, under the cloud page [2].

An additional file is included: demo_subject_id.csv. This is a list of the subject_id used to filter MIMIC-IV to the demo subset.

Usage Notes The MIMIC-IV demo provides researchers with the opportunity to better understand MIMIC-IV data.

CSV files can be opened natively using any text editor or spreadsheet program. However, as some tables are large it may be preferable to navigate the data via a relational database. We suggest either working with the data in Google BigQuery (see the "Files" section for access details) or creating an SQLite database using the CSV files. SQLite is a lightweight database format which stores all constituent tables in a single file, and SQLite databases interoperate well with a number software tools.

Code is made available for use with MIMIC-IV on the MIMIC-IV code repository [4]. Code provided includes derivation of clinical concepts, tutorials, and reproducible analyses.

Release Notes Release notes for the demo follow the release notes for the MIMIC-IV database.

Ethics This project was approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (Cambridge, MA). Requirement for individual patient consent was waived because the pr...

NOTES

This dataset comes from Divvy Bikes for learning purpose only, to know more about license, go to license section below. The company is "a fictional company" and does not relate to any commercial companies.

- About Company

In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime.

Until now, Cyclistic’s marketing strategy relied on building general awareness and appealing to broad consumer segments. One approach that helped make these things possible was the flexibility of its pricing plans: single-ride passes, full-day passes, and annual memberships. Customers who purchase single-ride or full-day passes are referred to as casual riders. Customers who purchase annual memberships are Cyclistic members.

- Scenario

You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations.

- Your Task

How do annual members and casual riders use Cyclistic bikes differently?

- Dataset information

1. License

See Data License Agreement for more information

2. Column Descriptors

ride_id: trip id

rideable_type: type of bike (classic, docked and electrical)

started_at: Trip start day and time

ended_at: Trip end day and time

start_station_name, start_station_id: Trip start station with its id

end_station_name, end_station_id: Trip end station

start_lat, start_lng: Latitude and Longitude of trip start station

end_lat, end_lng: Latitude and Longitude of trip end station

member_casual: Rider type (casual and member, more information see About Company)

The objective of this challenge is to predict PM2.5 particulate matter concentration in the air every day for each city. PM2.5 refers to atmospheric particulate matter that have a diameter of less than 2.5 micrometers and is one of the most harmful air pollutants. PM2.5 is a common measure of air quality that normally requires ground-based sensors to measure. The data covers the last three months, spanning hundreds of cities across the globe.

The data comes from three main sources:

Ground-based air quality sensors. These measure the target variable (PM2.5 particle concentration). In addition to the target column (which is the daily mean concentration) there are also columns for minimum and maximum readings on that day, the variance of the readings and the total number (count) of sensor readings used to compute the target value. This data is only provided for the train set - you must predict the target variable for the test set. The Global Forecast System (GFS) for weather data. Humidity, temperature and wind speed, which can be used as inputs for your model. The Sentinel 5P satellite. This satellite monitors various pollutants in the atmosphere. For each pollutant, we queried the offline Level 3 (L3) datasets available in Google Earth Engine (you can read more about the individual products here: https://developers.google.com/earth-engine/datasets/catalog/sentinel-5p). For a given pollutant, for example NO2, we provide all data from the Sentinel 5P dataset for that pollutant. This includes the key measurements like NO2_column_number_density (a measure of NO2 concentration) as well as metadata like the satellite altitude. We recommend that you focus on the key measurements, either the column_number_density or the tropospheric_X_column_number_density (which measures density closer to Earth’s surface). Unfortunately, this data is not 100% complete. Some locations have no sensor readings for a particular day, and so those rows have been excluded. There are also gaps in the input data, particularly the satellite data for CH4.

Variable Definitions: Read about the datasets at the following pages:

Weather Data: https://developers.google.com/earth-engine/datasets/catalog/NOAA_GFS0P25 Sentinel 5P data: https://developers.google.com/earth-engine/datasets/catalog/sentinel-5p - all columns begin with the dataset name (eg L3_NO2 corresponds to https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2) - look at the corresponding dataset on GEE for detailed descriptions of the image bands - band names should match the second half of the column titles.

We have created Singapore Maritime Dataset, using Canon 70D cameras around Singapore waters. All the videos are acquired in high definition (1080X1920 pixels). We divide the dataset into parts, on-shore videos and on-board videos, which are acquired by a camera placed on-shore on a fixed platform and a camera placed on-board a moving vessel, respectively. The videos are acquired at various locations and routes and thus do not necessarily capture the same scene. The third part is Near Infrared (NIR) videos which are also captured using another Canon 70D camera with the hot mirror removed and Mid-Opt BP800 Near-IR Bandpass filter.

Acknowledgement- Dataset has been captured by Dilip K. Prasad and annotated by student volunteers. Dataset has been captured on various environmental conditions like before sunrise (40 min before sunrise), sunrise, mid-day, afternoon, evening, after sunset (2hrs after sunset), Haze and Rain from July 2015 to May 2016.

Optical lens used for all the 3 sub-dataset - Canon EF 70-300mm f/4-5.6 IS USM

Following papers to be cited for using this dataset

D. K. Prasad, D. Rajan, L. Rachmawati, E. Rajabaly, and C. Quek, "Video Processing from Electro-optical Sensors for Object Detection and Tracking in Maritime Environment: A Survey," IEEE Transactions on Intelligent Transportation Systems (IEEE), 18 (8), 1993 - 2016, 2017. (preprint PDF)

Other papers on this dataset from our group

D. K. Prasad, H. Dong, D. Rajan, and C. Quek, "Are object detection assessment criteria ready for maritime computer vision?," IEEE Transactions on Intelligent Transportation Systems, 2019.

D. K. Prasad, C.K. Prasath, D. Rajan, L. Rachmawati, E. Rajabally, and C. Quek, "Object detection in maritime environment: Performance evaluation of background subtraction methods," IEEE Transactions on Intelligent Transportation Systems, 22 (5), 1787-1802, 2019.

D. K. Prasad, D. Rajan, L. Rachmawati, E. Rajabally, and C. Quek, “MuSCoWERT: multi-scale consistence of weighted edge Radon transform for horizon detection in maritime images,” Journal of Optical Society America A, vol. 33, issue 12, pp. 2491-2500, 2016.

D. K. Prasad, C.K. Prasath, D. Rajan, L. Rachmawati, E. Rajabally, and C. Quek, “Maritime situational awareness using adaptive multisensory management under hazy conditions,” 5th International Maritime-Port Technology and Development Conference (MTEC 2017), Singapore, 26-28 April, 2017.

D. K. Prasad, C.K. Prasath, D. Rajan, C. Quek, L. Rachmawati, and E. Rajabally, “Challenges in video based object detection in maritime scenario using computer vision,” 19th International Conference on Connected Vehicles, Zurich, 13-14 January, 2017.

D. K. Prasad, D. Rajan, C. Krishna Prasath, L. Rachmawati, E. Rajabally, and C. Quek, “MSCM-LiFe: Multi-Scale Cross Modal Linear Feature for Horizon Detection in Maritime Images,” IEEE TENCON, Singapore,22-25 Nov, 2016.

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. This data is extracted from the same link and made available in csv format.

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.

The data is available from 22 Jan 2020.

Acknowledgements

Johns Hopkins university has made the data available in google sheets format here. Sincere thanks to them.

Thanks to WHO, CDC, NHC and DXY for making the data available in first place.

Picture courtesy : Johns Hopkins University dashboard

Inspiration

Some insights could be

1. Changes in number of affected cases over time
2. Change in cases over time at country level
3. Latest number of affected cases