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~dashboard showcase~ Super Sample Superstore. This corporate style viz is a different take on the classic Tableau Superstore data set. Quickly spot increases or decreases in sales, profit ratio, and shipping time, and switch to the Prescriptive tab to pinpoint the root cause.
Refresh. Practice Your Data Analysis Skills as a Superstore Data Analyst.
This is a sample superstore dataset, a kind of simulation where you perform extensive data analysis to deliver insights on how the company can increase its profits while minimizing the losses. Perform ‘Exploratory Data Analysis’ on dataset ‘SampleSuperstore’.
Mar 30, 2022 · You will learn how to run EDA and perform it on the superstore dataset by using Python and then, visualising and analysing the data.
This project aims to use business intelligence tool to analyse the performance of a global superstore. The outcome of this analysis will provide insights that inform strategic decisions for the continued success and refinement of the Superstore.
About This Dataset: The Sample Superstore Sales dataset provides sales data for a fictional retail company, including information on products, orders and customers. This dataset includes the following variables: • Order ID - A unique identifier for each order.
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Super Sample Superstore. This corporate style viz is a different take on the classic Tableau Superstore data set. Quickly spot increases or decreases in sales, profit ratio, and shipping time, and switch to the Prescriptive tab to pinpoint the root cause. Created by Ryan Sleeper, this workbook aims to train users on design, data visualization ...
The table contains data on a sample superstore dataset with 9994 rows and 14 columns, including information on ship mode, segment, country, city, state, postal code, region, category, sub-category, sales, quantity, discount, and profit, which can be used to analyze and gain insights into various aspects of the superstore's operations and ...
Jun 21, 2022 · New learners who work almost exclusively with the Sample Superstore data set will soon face an unavoidable reality. When working with real-world data, a significant amount of time developing data visualizations requires considerable attention to preparing data.
