Analyze the provided dataset and identify areas

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Reference no: EM133853254

Data Science

Assessment Item: 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. Book assignment help service now!

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.

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.

Deliverables:
You need to submit one report (1000 +/- 10% words) in PDF format, documenting your analysis process, findings, and recommendations containing Python code/scripts used for data analysis, along with comments explaining the code logic and methodology and relevant Visualizations (e.g., plots, charts) supporting your analysis and findings.

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

Dataset:
Use the below python code to generate dataset with 1000 rows and following 7 columns (Customer ID, Product ID, Quantity sold, Unit price, Total transaction amount, Store ID). You can use any python editor to generate the dataset.

Reference no: EM133853254

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