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Exploratory Data Analysis (EDA) of Super Store Sales with Python
Data scientists help companies interpret and manage data and solve complex problems using expertise in a variety of data. They generally have a foundation in computer science, modeling, statistics, analytics, and math - coupled with a strong business sense. It’s this merging of intelligence and practical knowledge that makes the data scientist so valuable to a company.
Exploratory data analysis (EDA) is used by data scientists to analyze and investigate data sets and summarize their main characteristics, often employing data visualization methods. It helps determine how best to manipulate data sources to get the answers you need, making it easier for data scientists to discover patterns, spot anomalies, test a hypothesis, or check assumptions.
You can learn Exploratory Data Analysis with sample superstore dataset. This is a kind of a simulation where you perform extensive data analysis to deliver insights on how the company can increase its profits while minimizing the losses. Our task is to analyze the sales data and identify weak areas and opportunities for Super Store to boost business growth. By analyzing superstore sales data, we will make recommendations to business questions.
Which Category is Best Selling and Most Profitable?
What are the Best Selling and Most Profitable Sub-Category?
Which is the Top Selling Sub-Category?
Which Customer Segment is Most Profitable?
Which is the Preferred Ship Mode?
Which Region is the Most Profitable?
Which City has the Highest Number of Sales?
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