Fifteen LiDAR point-cloud derived height metrics with corresponding Aboveground biomass for 30 samples.

A. SUMMARY This data set reports key performance metrics for departments and programs in the City and County of San Francisco. B. HOW THE DATASET IS CREATED City departments report updates about their key metrics to the Controller’s Office. The Controller's Office uses an online application to collect and organize this data. Departments update most metrics once or twice each year. Some metrics may not display data for every year. C. UPDATE PROCESS Most metrics update twice each year. Updates with results for the first 6 months of each fiscal year are published in the spring, typically between April and May. Updates with results for each full fiscal year are published in the fall, typically in November. D. HOW TO USE THIS DATASET Each row represents one metric and one fiscal year for a department, with multiple values for each fiscal year. Some metrics do not include values for all fields or fiscal years. Some results for the latest fiscal year are unavailable because of known lags in reporting. Users should review any data notes reported for each row for guidance about interpreting values. All values are reported as numbers without formatting, but the column [Measure Data Type] describes the intended format. For example, a value appearing as “0.50” with [Measure Data Type] reported as “Percent” should be displayed as “50%”.

The report contains thirteen (13) performance metrics for City's workforce development programs. Each metric can be breakdown by three demographic types (gender, race/ethnicity, and age group) and the program target population (e.g., youth and young adults, NYCHA communities) as well. This report is a key output of an integrated data system that collects, integrates, and generates disaggregated data by Mayor's Office for Economic Opportunity (NYC Opportunity). Currently, the report is generated by the integrated database incorporating data from 18 workforce development programs managed by 5 City agencies. There has been no single "workforce development system" in the City of New York. Instead, many discrete public agencies directly manage or fund local partners to deliver a range of different services, sometimes tailored to specific populations. As a result, program data have historically been fragmented as well, making it challenging to develop insights based on a comprehensive picture. To overcome it, NYC Opportunity collects data from 5 City agencies and builds the integrated database, and it begins to build a complete picture of how participants move through the system onto a career pathway. Each row represents a count of unique individuals for a specific performance metric, program target population, a specific demographic group, and a specific period. For example, if the Metric Value is 2000 with Clients Served (Metric Name), NYCHA Communities (Program Target Population), Asian (Subgroup), and 2019 (Period), you can say that "In 2019, 2,000 Asian individuals participated programs targeting NYCHA communities. Please refer to the Workforce Data Portal for further data guidance (https://workforcedata.nyc.gov/en/data-guidance), and interactive visualizations for this report (https://workforcedata.nyc.gov/en/common-metrics).

The 311 website allows residents to submit service requests or check the status of existing requests online. The percentage of 311 website uptime, the amount of time the site was available, and the target uptime for each week are available by mousing over columns. The target availability for this site is 99.5%.

As a Human Resources professional in People Analytics for the past 5 years, I have found that there is a huge gap in free datasets to use for practicing data analytics related to employee metrics outside of turnover or attrition.

This dataset is completely created by myself, utilizing randomized names, ethnicities, genders, ages, positions, departments, and more data that you may encounter in a People Analytics role. Any resemblance to actual persons, living or dead, or actual business distribution or representation is purely coincidental. The data contained in this file is not intended to discriminate on basis of race, color, religion, sex (including pregnancy and gender identity), national origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, military service, or other non-merit factor. All data was randomized based on free data available from US Government websites, and when not available from a .gov website, the data was randomized based on Microsoft Excel's =rand and =randbetween functions.

This dataset can be used to practice people analytics related to diversity, position, department, performance, and more. You may freely use the data for any purpose, including personal usage, school or university projects, videos or courses, or whatever else you would like to use it for.

This dataset contains performance metrics tracked by the Chicago Department of Transportation (CDOT). Thirty-five different performance metrics are tracked in this dataset, which reports the performance target, actual performance, and number of requests completed for a given metric. These metrics are based on data calculated from the city's 311 system. Individual breakout of each performance metric are also available under the "More Views" button.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Gamelytics: Mobile Analytics Challenge 🎮📊

Subtitle

Unlock key insights into player behavior, optimize game metrics, and make data-driven decisions!

Description

Welcome to the Gamelytics: Mobile Analytics Challenge, a real-world-inspired dataset designed for data enthusiasts eager to dive deep into mobile game analytics. This dataset challenges you to analyze player behavior, evaluate A/B test results, and develop metrics for assessing game event performance.

Project Context & Tasks

Task 1: Retention Analysis

🔍 Objective: Calculate the daily retention rate of players, starting from their registration date.
📄 Data Sources:
- reg_data.csv: Contains user registration timestamps (reg_ts) and unique user IDs (uid).
- auth_data.csv: Contains user login timestamps (auth_ts) and unique user IDs (uid).
💡 Challenge: Develop a Python function to calculate retention, allowing you to test its performance on both the complete dataset and smaller samples.

Task 2: A/B Testing for Promotional Offers

🔍 Objective: Identify the best-performing promotional offer set by comparing key revenue metrics.
💰 Context:
- The test group has a 5% higher ARPU than the control group.
- In the control group, 1928 users out of 202,103 are paying customers.
- In the test group, 1805 users out of 202,667 are paying customers.
📊 Data Sources:
- ab_test.csv: Includes user_id, revenue, and testgroup columns.
💡 Challenge: Decide which offer set performs best, and determine the appropriate metrics for a robust evaluation.

Task 3: Event Performance Evaluation in "Plants & Gardens"

🔍 Objective: Develop metrics to assess the success of a time-limited in-game event where players can earn unique rewards.
🍃 Context: Players complete levels to win exclusive items, bonuses, or coins. In a variation, players may be penalized (sent back levels) after failed attempts.
💡 Challenge: Define how metrics should change under the penalty variation and identify KPIs for evaluating event success.

Dataset Information

The provided data is split into three files, each detailing a specific aspect of the application. Here's a breakdown:

1. User Registration Data (reg_data.csv)

• Records: 1,000,000
• Columns:
  • reg_ts: Registration time (Unix time, int64)
  • uid: Unique user ID (int64)
• Memory Usage: 15.3 MB
• Description: This dataset contains user registration timestamps and IDs. It is clean and contains no missing data.

2. User Activity Data (auth_data.csv)

• Records: 9,601,013
• Columns:
  • auth_ts: Login time (Unix time, int64)
  • uid: Unique user ID (int64)
• Memory Usage: 146.5 MB
• Description: This dataset captures user login timestamps and IDs. It is clean and contains no missing data.

3. A/B Testing Data (ab_test.csv)

• Records: 404,770
• Columns:
  • user_id: Unique user ID (int64)
  • revenue: Revenue (int64)
  • testgroup: Test group (object)
• Memory Usage: ~9.3 MB
• Description: This dataset provides insights into A/B test results, including revenue and group allocation for each user. It is clean and ready for analysis.

Inspiration & Benefits

• Real-World Relevance: Inspired by actual challenges in mobile gaming analytics, this dataset lets you solve meaningful problems.
• Diverse Data Types: Work with registration logs, activity timestamps, and experimental results to gain a holistic understanding of mobile game data.
• Skill Building: Perfect for those honing skills in retention analysis, A/B testing, and event-based performance evaluation.
• Community Driven: Built to inspire collaboration and innovation in the data analytics community. 🚀

Whether you’re a beginner or an expert, this dataset offers an engaging challenge to sharpen your analytical skills and drive actionable insights. Happy analyzing! 🎉📈

To make 311 effective for residents, visitors, and business owners, 311 representatives must respond to calls in a timely and accurate manner.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

DPH note about change from 7-day to 14-day metrics: As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, the school learning model indicator metrics will be calculated using a 14-day average rather than a 7-day average. The new school learning model indicators dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/CT-School-Learning-Model-Indicators-by-County-14-d/e4bh-ax24 As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well. With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county). This dataset includes the leading and secondary metrics identified by the Connecticut Department of Health (DPH) and the Department of Education (CSDE) to support local district decision-making on the level of in-person, hybrid (blended), and remote learning model for Pre K-12 education. Data represent daily averages for each week by date of specimen collection (cases and positivity), date of hospital admission, or date of ED visit. Hospitalization data come from the Connecticut Hospital Association and are based on hospital location, not county of patient residence. COVID-19-like illness includes fever and cough or shortness of breath or difficulty breathing or the presence of coronavirus diagnosis code and excludes patients with influenza-like illness. All data are preliminary. These data are updated weekly; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020. These metrics were adapted from recommendations by the Harvard Global Institute and supplemented by existing DPH measures. For national data on COVID-19, see COVID View, the national weekly surveillance summary of U.S. COVID-19 activity, at https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html Notes: 9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

Traffic analytics, rankings, and competitive metrics for examples.com as of September 2025

1) Data Introduction • The Power BI Sample Data is a financial sample dataset provided for Power BI practice and data visualization exercises that includes a variety of financial metrics and transaction information, including sales, profits, and expenses.

2) Data Utilization (1) Power BI Sample Data has characteristics that: • This dataset consists of numerical and categorical variables such as transaction date, region, product category, sales, profit, and cost, optimized for aggregation, analysis, and visualization. (2) Power BI Sample Data can be used to: • Revenue and Revenue Analysis: Analyze sales and profit data by region, product, and period to understand business performance and trends. • Power BI Dashboard Practice: Utilize a variety of financial metrics and transaction data to design and practice dashboards, reports, visualization charts, and more directly at Power BI.

This dataset contains information about posts made on Famous Cosmetic Brand's Facebook page from 1st of January to 31th of December of 2014. Each row represents a single post and includes the following attributes:

1. Page total likes: The total number of likes for the page at the time of the post. Example: 139441
2. Type: The type of post (e.g., photo, status, video, link). Example: Photo
3. Category: A categorical variable representing the content category of the post (the specific meaning of the categories is not defined in the provided data). Example: 2
4. Post Month: The month the post was published (likely represented numerically, e.g., 12 for December). Example: 12
5. Post Weekday: The day of the week the post was published (likely represented numerically, e.g., 1 for Monday). Example: 4
6. Post Hour: The hour of the day the post was published (likely in 24-hour format). Example: 3
7. Paid: A binary variable indicating whether the post was a paid advertisement (1 for yes, 0 for no). Example: 0
8. Lifetime Post Total Reach: The total number of unique people who saw the post during its lifetime. Example: 2752
9. Lifetime Post Total Impressions: The total number of times the post was displayed, regardless of whether it was clicked or seen. Example: 5091
10. Lifetime Engaged Users: The number of unique people who engaged with the post (e.g., liked, commented, shared, clicked). Example: 178
11. Lifetime Post Consumers: The number of unique people who clicked anywhere in the post. Example: 109
12. Lifetime Post Consumptions: The total number of clicks anywhere in the post. Example: 159
13. Lifetime Post Impressions by people who have liked your Page: The number of times the post was shown to people who liked the page. Example: 3078
14. Lifetime Post reach by people who like your Page: The number of people who like the page that saw the post. Example: 1640
15. Lifetime People who have liked your Page and engaged with your post: The number of people who liked the page that engaged with the post. Example: 119
16. comment: Number of comments on the post. Example: 4
17. like: Number of likes on the post. Example: 79
18. share: Number of shares of the post. Example: 17
19. Total Interactions: Total number of interactions with the post (likely sum of comments, likes, and shares). Example: 100

Citation: (Moro et al., 2016) S. Moro, P. Rita and B. Vala. Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach. Journal of Business Research, Elsevier, In press. Available at: http://dx.doi.org/10.1016/j.jbusres.2016.02.010

The City's Internet site allows residents to access City services online, learn more about the City of Chicago, and find other pertinent information. The percentage of the City’s Internet website uptime, the amount of time the site was available, and the target uptime for each week are available by mousing over columns. The target availability for this site is 99.5%.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Description

This dataset offers a comprehensive view of marketing and product performance metrics, designed to assist researchers, data analysts, and marketers in evaluating campaign efficiency, product sales, and customer engagement. It contains 10,000 rows of synthetically generated data, simulating real-world marketing scenarios to provide a rich basis for analysis and experimentation.

The dataset includes information on: - Campaign Performance: Budgets, clicks, conversions, ROI, and revenue generated. - Product Details: Units sold, discount levels, and bundle pricing. - Customer Insights: Subscription tiers, lengths, satisfaction ratings, and purchasing behavior. - Promotional Context: Flash sales, discount levels, and common keyword themes.

Key Features

• Campaign Insights: Understand how budget allocation impacts clicks, conversions, and revenue.
• Product Metrics: Analyze pricing strategies, bundles, and unit sales.
• Customer Engagement: Investigate satisfaction levels, subscription behavior, and response to discounts.
• Synthetic Data: All data points are artificially generated to protect privacy while maintaining statistical integrity.

File Details

• File Name: marketing_and_product_performance.csv
• Rows: 10,000
• Columns: 17

Applications

This dataset is ideal for: - Marketing Analysis: Optimize campaigns and understand product success metrics. - Predictive Modeling: Train models for conversion rate prediction, revenue forecasting, and customer segmentation. - Educational Use: Practice data exploration, cleaning, and analysis in a realistic marketing context.

License

This dataset is shared for educational and research purposes. Please attribute the source when using it in any project or publication.

The Iris flower data set or Fisher’s Iris data set is a multivariate data set used and made famous by the British statistician and biologist Ronald Fisher. The dataset was introduced in his 1936 paper "The Use of Multiple Measurements in Taxonomic Problems" (Fisher 1936) as an example of linear discriminant analysis.

This dataset has the following Features:

Petal.Length: Length of the petal

Petal.Width: Width of the petal

Sepal.Length: Length of the sepal

Sepal.Width: Width of the sepal

It has a total of 3 Groups: setosa, versicolor, and virginica.

This data set is used in the production of the Measure A Progress Tracker Dashboard. It contains both metric totals (where demo_cat is total_pers) and demographic breakdowns (according to demographic categories in demo_cat). Each row represents a calculated metric result (value) for a given reporting period (reporting_period), demographic category (demo_cat), demographic group (group), and type (count or within-group percentage). Data is refreshed in accordance with updates to the Measure A Progress Tracker Dashboard. Data Dictionarymetric_label Descriptive name of the Board-Approved Measure A metric. Corresponds with chart titles in the Measure A Progress Tracker dashboardreporting_period The fiscal reporting year for a metric. Fiscal years begin July 1st and end June 30th of the following yeardemo_cat The demographic category for demographic breakdowns (e.g., age, race, gender, veteran status). The value total_pers represents person-level totals group The specific demographic subgroup within the category in demo_cat. For example, if demo_cat is age, group represents groups defined by age range. type Indicates the type of measure for a given metric row. Both counts (“cnt”) and within-group percents (i.e., rates) (“percent”) are available. Percent measures are calculated using the applicable context metrics (i.e., denominators) provided in the data set except in the case of percent measures for the metric Service Participants Who Exited to Permanent Housing and Remained Housed (goal 3), where percent measures are calculated using the Service Participants Who Exited to Permanent Housing (goal) metric and represent group-specific retention ratesvalue The numeric value of the metric

--- DATASET OVERVIEW --- This dataset delivers critical market intelligence including occupancy rates, average daily rates, revenue per available rental, booking pace, and seasonal demand patterns across different geographic areas. With daily updates, AI-driven forward projections, and four years of historical data, it offers property managers, investors, and market analysts the essential benchmarks needed to understand market performance, identify emerging trends, and develop data-driven strategies in the rapidly evolving vacation rental sector.

The data is sourced from major OTA platforms and processed through advanced aggregation methodologies that ensure statistical validity while preserving crucial market signals. Our proprietary algorithms enhance the raw data with sophisticated trend analysis and forward-looking projections, enabling users to anticipate future market conditions with increased confidence.

--- KEY DATA ELEMENTS --- Our dataset includes the following core performance metrics for each property: - Property Groups: Group by property type, bedroom counts, key amenities groups - Geographic Identifiers: Multiple geographic levels (vacation area, vacation region, county, etc) - Temporal Dimensions: Daily, weekly, monthly, and quarterly performance metrics - Occupancy Metrics: Market-wide occupancy rates and booking pace indicators - Pricing Metrics: Average daily rates (ADR), revenue per available rental night (RevPAR), and price trends - Booking Pattern Indicators: Average lead time, length of stay, and booking frequency - Seasonality Metrics: Seasonal demand patterns and year-over-year comparisons - Demand Forecasts: Forward-looking projections for occupancy and pricing trends - Historical Pacing: Snapshots into how stay date ranges developed for tracking pacing trends - Forward Looking Trends: Area KPIs 180-365 days into the future

--- USE CASES --- Market Performance Benchmarking: Property managers and owners can benchmark their individual property or portfolio performance against market-wide metrics. By comparing property-specific occupancy rates, ADR, and RevPAR against market averages for similar property types, managers can assess relative performance and identify areas for improvement. These benchmarks provide crucial context for performance evaluation and goal setting.

Investment Decision Support: Real estate investors and portfolio managers can use market-level performance data to identify attractive investment opportunities across different geographic areas. The comprehensive market metrics reveal high-performing areas, emerging markets, and potential investment risks based on actual performance data rather than anecdotal evidence. This information supports data-driven acquisition strategies and portfolio diversification decisions.

Demand Forecasting and Planning: Revenue managers and property operators can leverage the historical performance patterns and forward-looking projections to anticipate demand fluctuations and plan accordingly. The seasonal patterns, booking pace indicators, and AI-enhanced forecasts enable proactive rate adjustments, marketing timing, and operational planning to maximize revenue opportunities during high-demand periods.

Market Entry Analysis: Companies considering entering new vacation rental markets can utilize this dataset to understand market dynamics, competitive intensity, and performance expectations before committing resources. The comprehensive market metrics reduce market entry risk by providing clear visibility into potential revenue opportunities, seasonal patterns, and overall market health.

Performance Attribution Analysis: Market analysts can use this dataset to understand the drivers behind performance variations across different markets and time periods. By analyzing how market composition, seasonality, and external factors influence overall performance, analysts can identify the underlying causes of performance trends and develop more accurate forecasting models.

Economic Impact Assessment: Economic development organizations and tourism authorities can leverage this dataset to quantify the economic contribution of the vacation rental sector. The market-wide revenue metrics, occupancy patterns, and supply growth indicators provide valuable inputs for economic impact studies and policy development related to the short-term rental industry.

--- ADDITIONAL DATASET INFORMATION --- Delivery Details: • Delivery Frequency: daily | weekly | monthly | quarterly | annually • Delivery Method: scheduled file loads • File Formats: csv | parquet • Large File Format: partitioned parquet • Delivery Channels: Google Cloud | Amazon S3 | Azure Blob • Data Refreshes: daily

Dataset Options: • Coverage: Global (most countries) • Historic Data: Available (2021 for most areas) • Future Looking Data: Available (Current date + 180-365 days) • Point-in-Time: Available (with weekly as of dates) • Aggregation and Filtering Options: • Area/Market ...

Example data frame of class-level metrics.

Example of modeled customer behavioral data showing user sessions, engagement metrics, and conversion data across multiple platforms and devices