Creating different visualizations in dashboard

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Reference no: EM133852744 , Length: word count:1000

Data Science

Assessment Item 1: Report - Statistical Analysis of Business Data

Objective
This assessment item relates to the unit learning outcomes as in the unit descriptor. This assessment is designed to give students experience in analyzing a suitable dataset and creating different visualizations in dashboard and to improve student presentation skills relevant to the Unit of Study subject matter.

Case Study:
You are a data scientist hired by a retail company, "SmartMart," which operates a chain of grocery stores. SmartMart has been in the market for several years and has a significant customer base. However, the company is facing challenges in optimizing its operations and maximizing profits. As a data scientist, your task is to analyze the provided dataset and identify areas where data science techniques can be applied to create business value for SmartMart. Book assignment help service now!

Dataset:
You'll need to use the below python code to generate your own artificial dataset. The dataset provided contains information on SmartMart's sales transactions over the past year. It includes data such as:
Date and time of each transaction
Customer ID
Product ID
Quantity sold
Unit price
Total transaction amount
Store ID

Tasks:
You are tasked to apply appropriate statistical analysis techniques to extract valuable information from the dataset. This may include but is not limited to:
Descriptive statistics
Correlation analysis
Hypothesis testing
Time-series analysis

You need to:
Identify key findings and insights from your analysis that can help SmartMart make data-driven decisions to optimize its operations and increase profitability.
Present your analysis results in a clear and concise manner, including visualizations and explanations where necessary.
Provide recommendations on specific strategies or actions that SmartMart can take based on your analysis.

Report Structure (suggestive)
Executive summary
Introduction
Analysis Process and Methodology
Findings and insights
Recommendations
Code screenshots
Conclusion
Appendix

Reference no: EM133852744

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