This dataset include two .csv files containing the integrated dataset used by the COVID-19 School Dashboard website to report and maps confirmed school-related cases of COVID-19 in publicly funded elementary and secondary schools in Ontario, Canada, and connects this to data on school social background characteristics. One csv file reports cases from 2020-09-10 to 2021-04-14 (2020 school year) while the other csv file reports cases from 2021-09-13 to 2021-12-22 (2021 school year). Two accompanying .doc files are included to describe the variables in the .csv files.

This .csv file is the underlying data for the Elections Spending Dashboard. You can access the dashboard at https://election-spending-data.shinyapps.io/dashboard/

An interactive dashboard to visualize sales performance, product categories, regional performance, and key business KPIs.

📌 Description This project analyzes online sales data using Power BI, focusing on performance metrics such as Total Sales Amount, Profit, Quantity Sold, and Payment Modes. The dashboard provides detailed visualizations to identify top-performing categories, sub-categories, and locations. It aims to deliver actionable insights for business strategy, marketing decisions, and operational improvements.

The dataset is split across two CSV files:

• Orders.csv – contains customer and order metadata (date, name, location)

• Details.csv – contains order-level details (profit, quantity, payment mode, category)

🧩 Key Features - KPI Cards: Total Amount, Total Profit, Total Quantity, Profit Margin

• Pie Charts: Sales by Category, Sales by Payment Mode

• Donut Chart: Sales by State

• Bar Chart: Sales by Sub-Category

• Map: Quantity sold across Indian States

• Interactive Slicers and Filters

⚒️ Tools & Techniques Power BI Desktop

DAX Calculations

Custom Visual Design for Clean UI/UX

These files document the granting activities of the Institute of Museum and Library Services from 2008-2024. Each csv shows the grants that were given in a particular year and state, as designated in the file name. The data shows the grant number, the program name, description (if available), amount ($), institution name, city, and state.

DescriptionUnderlying data file for graduate industry regional analysis in the dashboard. CoverageGraduate populations of UK domiciled graduates of English Higher Education providers, one, three, five and ten years after graduation (YAG), 2021/22 tax year. File formats and conventionsFormatFiles are Comma Separated Value (CSV)ConventionsEarnings figures are rounded to the nearest £100Employment outcome percentages are rounded to the nearest 0.1%All populations are rounded to the nearest 5 full-person equivalent (FPE) individualsc = data has been suppressed due to small numbers. z = there is no result (N/A)

2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

• Confirmed Cases by Country/Region/Sovereignty
• Confirmed Cases by Province/State/Dependency
• Deaths
• Recovered

Downloadable data:
https://github.com/CSSEGISandData/COVID-19

Additional Information about the Visual Dashboard:
https://systems.jhu.edu/research/public-health/ncov

JCMUA Sewer Maintenance Data Dashboards 2014-2016JCMUA Sewer Maintenance Data Dashboard 2016 (XLSX)Download LinkJCMUA Sewer Maintenance Data Dashboard 2015 (XLSX)Download LinkJCMUA Sewer Maintenance Data Dashboard 2015 (CSV)Download LinkJCMUA Sewer Maintenance Data Dashboard 2014 (XLSX)Download LinkJCMUA Sewer Maintenance Data Dashboard 2014 (CSV)Download Link

Selection of 104 water security related indicators, with visualisation tools to plot up to 4 variables simultaneously. Graphs can be direct linked, and data sets can be downloaded as CSV files

Dataset Overview: Contains sales data from Blinkit, including product details, order quantities, revenue, and timestamps. Useful for demand forecasting, price optimization, trend analysis, and business insights. Helps in understanding customer behavior and seasonal variations in online grocery shopping. Potential Use Cases: - Time Series Analysis: Analyze sales trends over different periods. - Demand Forecasting: Predict future product demand based on historical data. - Price Optimization: Identify the impact of pricing on sales and revenue. - Customer Behavior Analysis: Understand buying patterns and preferences. - Market Trends: Explore how different factors affect grocery sales performance. This dataset can be beneficial for data scientists, business analysts, and researchers looking to explore e-commerce and retail trends. Feel free to use it for analysis, machine learning models, and business intelligence projects. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16299142%2Fa633fb36dc370263696b5d2ec940c74f%2FScreenshot%202025-06-16%20082824.png?generation=1750086765806732&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16299142%2F8843129c88c2f57d66006a3ac9d37dc7%2FScreenshot%202025-06-16%20084001.png?generation=1750086777975125&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16299142%2Ffa4f29a8f4cc763a1cc66c7913c077e8%2FScreenshot%202025-06-16%20084007.png?generation=1750086787100561&alt=media" alt="">

Key indicator data from City of Toronto divisions and agencies are shared with the City Manager's Office and made available through the Toronto's Dashboard application. Toronto’s Dashboard data is used to assess trends and directions of key indicators for Toronto as a whole and for City of Toronto services. This dataset includes the most recent data and data from previous periods available in the Toronto's Dashboard application. CSV files for each individual key indicator can be exported directly through Toronto's Dashboard.

CSV Performance Dashboard Time to Step Down Report: Children and Youth in Foster Care

Sessions, page views, users, devices and referrals for the DataWorks platform using information from Google Analytics. The interactive dashboard provides data by month from September 2018 for sessions, page views, users, devices and referrals to DataWorks. Previous years are covered by static dashboards. Excel and csv files give data on the use of datasets published on our Data Works platform. Please note, each file uploaded on the site is listed and this may include files now deleted or changed. Our Open Data is also published on Data.Gov.UK - Calderdale

LouVelo is a docked bikeshare program owned by Louisville Metro Government and operated by Cyclehop since May of 2017. The System includes Approximately 250 bikes, 25 Docked Stations in Louisville, and an additional 3 stations owned and operated by the City of Jeffersonville in Partnership with Cyclehop. These data will be updated on a monthly basis to show monthly trends in ridership along with general patterns of use with pick up and drop off location data. These data are updated and maintained for use in the Louisville Metro Open Data Portal LouVelo Dashboard to show ridership for the entirety of the program. Some stations have been relocated since the programs founding. For up to date information on dock locations please view the system map on the LouVelo website. This dashboard is maintained by Louisville Metro Public Works.For any questions please contact:James GrahamMobility CoordinatorLouisville Metro Public WorksDivision of Transportation444 S. 5th, St, Suite 400Louisville, KY 40202(502) 574-6473james.graham@louisvilleky.govFor more information about the LouVelo bikeshare program please visit their website.

Key indicator data from City of Toronto divisions and agencies are shared with the City Manager's Office and made available through the Toronto's Dashboard application. Toronto’s Dashboard data is used to assess trends and directions of key indicators for Toronto as a whole and for City of Toronto services. This dataset includes the most recent data and data from previous periods available in the Toronto's Dashboard application. CSV files for each individual key indicator can be exported directly through Toronto's Dashboard.

The Parking Penalty Charge Notice (PCN) Dashboard contains details of all on-street PCNs issued in the London Borough of Barnet since 1 January 2015. Analysis of issued PCNs can be carried out by filtering the following fields: Day of the week Year Issue Date of PCN Contravention code Street name Issue time Hours Users can select the criteria by which to filter their search within each section. In some cases, selecting an option in one field may limit the options remaining available in other fields. To see the raw data behind each line in the table on the left, double-click in the field containing the relevant figure. We have also provided the raw data used to build the dashboard as a machine-readable csv file, as well as individual raw datasets for each calendar year.

These dashboards provide details of all traffic Penalty Charge Notices issued to vehicles in the London Borough of Barnet since 1 January 2015. Analysis of issued PCNs can be carried out by filtering the following fields; Day of the week Date Year Issue Date of PCN Contravention code Street name Issue time Hours Users can select the criteria by which to filter their search within each section. In some cases, selecting an option in one field may limit the options remaining available in other fields. Once the search is complete, the user should click the ‘Click here to clear filters’ button. This resets the dashboard for a new search. We have also provided the raw data used to build the dashboards as machine-readable csv files.

The Bus Lane Penalty Charge Notice (PCN) Dashboard contains details of all bus lane PCNs issued in the London Borough of Barnet since 29 January 2015. Analysis of issued PCNs can be carried out by filtering the following fields; Day of the week Year Issue Date of PCN Contravention code Street name Issue time Hours Users can select the criteria by which to filter their search within each section. In some cases, selecting an option in one field may limit the options remaining available in other fields. To see the raw data behind each line in the table on the left, double-click in the field containing the relevant figure. We have also provided the raw data used to build the dashboard as a machine-readable csv file. Please note: This data includes Warning Notices - PCNs issued by CEOs which do not have an associated monetary value

This document explain how data were generated and how to interpret them.

LICENSE: CC0 But if you want to combine data with other datasets, feel free to use them as if they were published under CC0 license.
Data were published in February 2017. At that time, Zenodo only provided CC BY, CC BY-SA, CC BY-NC, CC BY-ND and CC BY-NC-ND. No CC0 option was available.

HOW DATA WERE COLLECTED Data provided in this dataset were collected in Fall 2016 as part of the Bachelor course 'Formation des usagers en bibliothèques' taught at the University of applied sciences Geneva (Information Sciences Department).
Data were generated by the interaction of the students with DAD, the Dynamic assessment dashboard, software to be released in 2017 (https://github.com/grolimur/DAD). Only data related to individual activities are published in this set. They were then cleaned and anonymised.

The activities.csv file is provided for information purpose.

Data are provided in 2 formats:

• Comma-spearated values (submissions.csv)
• SQLite (submissions.sqlite)

CSV is not correctly read by Excel, it's recommended to convert it into an .xslx file before using it.
SQLite is provided in order to apply different sorting and filters to the data. It can be read using SQLite manager for Firefox (https://addons.mozilla.org/en-US/firefox/addon/sqlite-manager/).

CODEBOOK Here is the name, the meaning and the possible values of the columns (name - description [possible values]).

ID - database row ID [(database unique row ID)] ActivityID - unique activity ID [1, 2, 3, 4, 10, 14, 16, 21, 22, 23, 41] StudentID - unique anonymous student ID [#50 to #72] date - recorded by the database [timestamp] year - year extracted from date [2016, 2017] month - month extracted from date [01, 11, 12] day - day extracted from date [1 to 31] weekday - weekday calculated from date [1, 2, 3, 4, 5, 6, 7] hour - hour extracted from date - 00 to 23] minute - minute extracted from date [00 to 59] second - second extracted from date [00 to 59] gr - group activity or not [0] validated - was the submission validated or not [-1 or 1] week - week number [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, H1, H2]

ActivityID meaning Activities are described in activities.csv. This file conatins only individual activities description.
The 2 activities iwth the bonus particularity are not present in the data because students didn't need to apply for them. A bonus was automatically added when activity 1 AND 2 AND 3 AND 4 (bonus = activity 5) as well as when activities 21 AND 22 AND 23 (activity 24) were completed.

Date format The date is formatted like YYYY-MM-DD hh:mm:ss.

Weekday meaning 1 stands for Monday, 2 for Tuesday and so on until 7 for Sunday.

Group meaning 0 stands for 'individual activity' and 1 would stand for group activity'.
Only entries with gr="0" appears in this dataset.

Validated meaning -1 stands for 'rejected' and 1 for 'validated'.

Week meaning The course lasts 10 weeks. 1 stands for the 1st week and so on until 10 that stands for the 10th week.
H1 and H2 stand for the Christmas holiday weeks that took place between week 8 and 9.
No submission were sent in weeks 1, 2 and 10.

The following datasets are based on the adult (age 21 and over) beneficiary population and consist of aggregate MHS data derived from Medi-Cal claims, encounter, and eligibility systems. These datasets were developed in accordance with California Welfare and Institutions Code (WIC) § 14707.5 (added as part of Assembly Bill 470 on 10/7/17). Please contact BHData@dhcs.ca.gov for any questions or to request previous years’ versions of these datasets. Note: The Performance Dashboard AB 470 Report Application Excel tool development has been discontinued. Please see the Behavioral Health reporting data hub at https://behavioralhealth-data.dhcs.ca.gov/ for access to dashboards utilizing these datasets and other behavioral health data.

Discount mart wants a dashboard where he can track how well Discount Mart is doing for this year (in terms of Sales, Profit and Quantity Sold). They would also like to know how well categories are performing as well as different regions.The mart owner assumes that most customers buy 2 or more products per basket/order but would like this confirmed by the data. Utkarsh also noted that Profit is 30% of the selling price.The mart has given us csv files from which we have to create a Dashboard for his quick review so that he could take further decisions for the benefit of the company. We created below interactive dashboard as per their requirement. Now, the mart marketing team could switch and view different shipping stats for different months and years. The mart can get more profits when they sell in New York and California but this will not add up for further growth since they have a strong sales there, instead the team should focus on the states such as Texas, Washington where they have mediocre sales which can be increased. The special focus should be on the states which have very low sales like Kansas, Mississippi specially focusing on Furniture products. If selling furniture products is not viable for business, the team should rethink and reinvest in the strategies further.