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.

About Datasets: - Domain : Finance - Project: Bank loan of customers - Datasets: Finance_1.xlsx & Finance_2.xlsx - Dataset Type: Excel Data - Dataset Size: Each Excel file has 39k+ records

KPI's: 1. Year wise loan amount Stats 2. Grade and sub grade wise revol_bal 3. Total Payment for Verified Status Vs Total Payment for Non Verified Status 4. State wise loan status 5. Month wise loan status 6. Get more insights based on your understanding of the data

Process: 1. Understanding the problem 2. Data Collection 3. Data Cleaning 4. Exploring and analyzing the data 5. Interpreting the results

This data contains Power Query, Power Pivot, Merge data, Clustered Bar Chart, Clustered Column Chart, Line Chart, 3D Pie chart, Dashboard, slicers, timeline, formatting techniques.

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.

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 The 21 recorded sessions took place between February 2013 and December 2016.
Data were collected using Turning Technologies' remote controls (called clickers) and TurningPoint software.

The 4 versions of the quiz used during these 4 years are provided in the 'quizzes' folder for information purpose (in PDF and Powerpoint formats).

Turning Technologies records data in a closed format (.tpzx) that can be exported and converted them into 3 formats provided here (these 3 files contain the same data):

• Excel (.xslx)
• Comma-spearated values (.csv)
• SQLite (.sqlite)

The first one was directly exported from TurningPoint and is provided for Excel users who can't read CSV correctly.
CSV was converted from Excel and is provided for non-Excel users.
Finally, 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 - meaning [possible values]). If students didn't answer the question, the value is '-'.

Session - session number (chronological) [1 to 21] AcademicYear - academic year [12-13, 13-14, 14-15, 15-16, 16-17] Year - calendar year [2013, 2014, 2015, 2016] Month - month (number) [1 to 12] Day - day (number) [1 to 31] Section - section abbreviation [CH, ESC, GM, IF, SIE, SV] Level - students' level [BA2, BA3, MA] Language - course's language [FR or EN] DeviceID - clicker's ID [(unique ID within a session)] Q1 - answers to question 1 [A, B, C, D, E] Q2 - answers to question 2 [A, B, C, D] Q3 - answers to question 3 [A or B] Q4 - answers to question 4 [A or B] Q5 - answers to question 5 [A or B] Q6 - answers to question 6 [A or B] Q7 - answers to question 7 [A or B] Q8 - answers to question 8 [A or B] Q9 - answers to question 9 [A or B] Q8-9 - answers to the question 8-9 (merge) [A or B] Q10 - answers to question 10 [1, 2] Q11 - answers to question 11 [A or B] Q12 - answers to question 12 [A, B]

Section abbreviation meaning * CH: chemistry * ESC: school of criminal justice (Unil) * GM: mechanical engineering * IF: financial engineering * SIE: environmental engineering * SV: life sciences

Level meaning
* BA2: 2nd year of Bachelor * BA3: 3rd year of Bachelor * MA: Master level

Question types For some questions, multiple answers were allowed: Q1, Q2, Q10 & Q12.
Half of the questions have only one correct answer, true or false: Q3, Q5, Q6, Q7, Q8, Q9 & Q8-9.
Finally, for 2 questions only one answer was accepted, but there is not only one correct answer: Q4 & Q11.

INFORMATION ABOUT THE SESSIONS Except otherwise stated below, all sessions were conducted like the original one: Q1 to Q12 (no Q8-9). The original French version of the quiz has been translated into English for a few sessions with Master students. For sessions 14 and 20, Q5 was removed and Q8 & Q9 were merged in Q8-9.
Session 18 was a short one with only 7 sevens questions: Q1, Q2, Q3, Q4, Q6, Q7 & Q9.

CONTACT INFORMATION If you have any question about these data, contact formations.bib@epfl.ch.

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