On an annual basis (individual hospital fiscal year), individual hospitals and hospital systems report detailed facility-level data on services capacity, inpatient/outpatient utilization, patients, revenues and expenses by type and payer, balance sheet and income statement.

Due to the large size of the complete dataset, a selected set of data representing a wide range of commonly used data items, has been created that can be easily managed and downloaded. The selected data file includes general hospital information, utilization data by payer, revenue data by payer, expense data by natural expense category, financial ratios, and labor information.

There are two groups of data contained in this dataset: 1) Selected Data - Calendar Year: To make it easier to compare hospitals by year, hospital reports with report periods ending within a given calendar year are grouped together. The Pivot Tables for a specific calendar year are also found here. 2) Selected Data - Fiscal Year: Hospital reports with report periods ending within a given fiscal year (July-June) are grouped together.

With a step-by-step approach, learn to prepare Excel files, data worksheets, and individual data columns for data analysis; practice conditional formatting and creating pivot tables/charts; go over basic principles of Research Data Management as they might apply to an Excel project. Avec une approche étape par étape, apprenez à préparer pour l’analyse des données des fichiers Excel, des feuilles de calcul de données et des colonnes de données individuelles; pratiquez la mise en forme conditionnelle et la création de tableaux croisés dynamiques ou de graphiques; passez en revue les principes de base de la gestion des données de recherche tels qu’ils pourraient s’appliquer à un projet Excel.

Analyzing sales data is essential for any business looking to make informed decisions and optimize its operations. In this project, we will utilize Microsoft Excel and Power Query to conduct a comprehensive analysis of Superstore sales data. Our primary objectives will be to establish meaningful connections between various data sheets, ensure data quality, and calculate critical metrics such as the Cost of Goods Sold (COGS) and discount values. Below are the key steps and elements of this analysis:

1- Data Import and Transformation:

• Gather and import relevant sales data from various sources into Excel.
• Utilize Power Query to clean, transform, and structure the data for analysis.
• Merge and link different data sheets to create a cohesive dataset, ensuring that all data fields are connected logically.

2- Data Quality Assessment:

• Perform data quality checks to identify and address issues like missing values, duplicates, outliers, and data inconsistencies.
• Standardize data formats and ensure that all data is in a consistent, usable state.

3- Calculating COGS:

• Determine the Cost of Goods Sold (COGS) for each product sold by considering factors like purchase price, shipping costs, and any additional expenses.
• Apply appropriate formulas and calculations to determine COGS accurately.

4- Discount Analysis:

• Analyze the discount values offered on products to understand their impact on sales and profitability.
• Calculate the average discount percentage, identify trends, and visualize the data using charts or graphs.

5- Sales Metrics:

• Calculate and analyze various sales metrics, such as total revenue, profit margins, and sales growth.
• Utilize Excel functions to compute these metrics and create visuals for better insights.

6- Visualization:

• Create visualizations, such as charts, graphs, and pivot tables, to present the data in an understandable and actionable format.
• Visual representations can help identify trends, outliers, and patterns in the data.

7- Report Generation:

• Compile the findings and insights into a well-structured report or dashboard, making it easy for stakeholders to understand and make informed decisions.

Throughout this analysis, the goal is to provide a clear and comprehensive understanding of the Superstore's sales performance. By using Excel and Power Query, we can efficiently manage and analyze the data, ensuring that the insights gained contribute to the store's growth and success.

The National Pollutant Release Inventory (NPRI) is Canada's public inventory of pollutant releases (to air, water and land), disposals and transfers for recycling. This database contains the full NPRI dataset from 1993 to the current reporting year. To help you navigate, a Microsoft Word file provides information on the database’s structure and schema. The database is available in Microsoft Access format (accdb). The data are in normalized or “list” format and are optimized for pivot table analyses. The data are also available in a CSV format : https://open.canada.ca/data/en/dataset/40e01423-7728-429c-ac9d-2954385ccdfb. Please consult the following resources to enhance your analysis: Guide on using and Interpreting NPRI Data: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/using-interpreting-data.html Access additional data from the NPRI, including datasets and mapping products: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/tools-resources-data/exploredata.html Supplemental Information This data is also available in non-proprietary CSV format on the Bulk Data page. http://open.canada.ca/data/en/dataset/40e01423-7728-429c-ac9d-2954385ccdfb These files contain data from 1993 to the latest reporting year available. These datasets are in normalized or ‘list’ format and are optimized for pivot table analyses. Supporting Projects: National Pollutant Release Inventory (NPRI)

About this webinar We rarely receive the research data in an appropriate form. Often data is messy. Sometimes it is incomplete. And sometimes there’s too much of it. Frequently, it has errors. This webinar targets beginners and presents a quick demonstration of using the most widespread data wrangling tool, Microsoft Excel, to sort, filter, copy, protect, transform, aggregate, summarise, and visualise research data. Webinar Topics Introduction to Microsoft Excel user interface Interpret data using sorting, filtering, and conditional formatting Summarise data using functions Analyse data using pivot tables Manipulate and visualise data Handy tips to speed up your work Licence Copyright © 2021 Intersect Australia Ltd. All rights reserved.

Abstract This dataset was derived by the Bioregional Assessment Programme. The parent datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset contains a compilation of stratigraphic data for bores and wells in the Galilee subregion as well as summary statistics for Galilee stratigraphic formations and groups. Dataset History Stratigraphic data …Show full descriptionAbstract This dataset was derived by the Bioregional Assessment Programme. The parent datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset contains a compilation of stratigraphic data for bores and wells in the Galilee subregion as well as summary statistics for Galilee stratigraphic formations and groups. Dataset History Stratigraphic data was exported from several source datasets, formation and group names cleaned and transformed into a standard format and loaded into the 'all strat subregion' sheet. In the 'all strat subregion' sheet, the 'Strat source' column identifies the source dataset used to derive the stratigraphic information for each bore. "Jul14_GW" refers to the "STRATIGRAPHY" sheet in the QLD Department of Natural Resources and Mining Groundwater Database Extract 20142808 (GUID: a5c5cbdb-1152-43f7-9533-a123027b7ce1 ) dataset "WCR" refers to well completion reports from the QDEX Well Completion Reports (WCR) - Galilee v01 (GUID: 370a30da-54e3-4b58-a983-9c655b14ba3a ) dataset "Com strat" refers to the 'BH_costrat' table in the Queensland petroleum exploration data - QPED (GUID: cb357721-bf22-45c9-a82e-828807912dd4) dataset "GSQ strat" refers to the 'BH_gsqstrat' table in the Queensland petroleum exploration data - QPED (GUID: cb357721-bf22-45c9-a82e-828807912dd4) dataset. Formation and group pivot tables showing various statistcs summarising the stratigraphic information were created. From these statistics, formation and group thickness values were derived. Strat and group thickness values were derived from source top and bottom values. Dataset Citation Bioregional Assessment Programme (2016) Stratigraphy and statistics for the Galilee subregion. Bioregional Assessment Derived Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/0e611b46-def6-42ee-a518-4c2fd2d07fce. Dataset Ancestors Derived From QDEX well completion reports (WCR) - Galilee v01 Derived From QLD Department of Natural Resources and Mines Groundwater Database Extract 20142808 Derived From Queensland petroleum exploration data - QPED