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 SMARTDEST DATASET WP3 v1.0 includes data at sub-city level for 7 cities: Amsterdam, Barcelona, Edinburgh, Lisbon, Ljubljana, Turin, and Venice. It is made up of information extracted from public sources at the local level (mostly, city council open data portals) or volunteered geographic information, that is, geospatial content generated by non-professionals using mapping systems available on the Internet (e.g., Geofabrik). Details on data sources and variables are included in a ‘metadata’ spreadsheet in the excel file. The same excel file contains 5 additional spreadsheets. The first one, labelled #1, was used to perform the analysis on the determinants of the geographical spread of tourism supply in SMARTDEST case study’s cities (in the main document D3.3, section 4.1), The second one (labelled #2) offers information that would allow to replicate the analysis on tourism-led population decline reported in section 4.3. As for spreadsheets named #3-AMS, #4-BCN, and #5-EDI, they refer to data sources and variables used to run follow-up analyses discussed in section 5.1, with the objective of digging into the causes of depopulation in Amsterdam, Barcelona, and Edinburgh, respectively. The column ‘row’ can be used to merge the excel file with the shapefile ‘db_task3.3_SmartDest’. Data are available at the buurt level in Amsterdam (an administrative unit roughly corresponding to a neighbourhood), census tract level in Barcelona and Ljubljana, for data zones in Edinburgh, statistical zones in Turin, and località in Venice.

Replication materials for the manuscript "Skepticism in Science and Punitive Attitudes", published in the Journal of Criminal Justice.Note that the GSS repeated cross sections for 1972 to 2018 are too large to upload here, but they can be accessed from https://gss.norc.org/content/dam/gss/get-the-data/documents/spss/GSS_spss.zipIncluded here are:(A link to the repeated cross-sections data)Each of the 3 wave panels (2006-2010; 2008-2012; 2010-2014)Replication R script for the repeated cross sections cleaning and analysisReplication R script for the panel data cleaning and analysisAn excel spreadsheet with Uniform Crime Report data to merge to the cross sections.

Excel spreadsheet containing, in separate sheets, underlying numerical data used to generate the indicated figure panels.