Reference no: EM133753184

Data Science

Assessment Item 1: Report - Statistical Analysis of Business Data
Report - Statistical Analysis of
Business Data (Individual) (1000 Words

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.

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.

Data Acquisition and Data

Part A - Report

Part B - Presentations

Assignment Overview:
In this assignment, you will work in a group of 3 to 5 students to conduct an Exploratory Data Analysis (EDA) on a comprehensive dataset. The dataset can be acquired from internal or external sources, or by merging both. You will utilize appropriate techniques, tools, and programming languages, such as Python, to perform various data procedures including data acquisition, data wrangling, and data mining to extract meaningful insights from the dataset. The final deliverables will include an EDA report and an oral presentation video to showcase your findings and analysis.

Assignment Tasks:

Data Acquisition:

Identify and acquire a comprehensive dataset suitable for the EDA. You can choose from the suggested data sources provided or explore and select different datasets based on your group's common interest.
Ensure the dataset is relevant, sufficiently large, and contains multiple variables for thorough analysis.

Data Wrangling:

Preprocess the acquired dataset to handle missing values, outliers, and inconsistencies.
Perform data cleaning tasks such as removing duplicates, standardizing formats, and transforming variables if necessary.
Explore methods to handle categorical variables and convert them into a suitable format for analysis.

Data Exploration:

Conduct initial data exploration to understand the structure, distributions, and relationships within the dataset.
Utilize descriptive statistics and visualization techniques (e.g., histograms, box plots, scatter plots) to gain insights into individual variables and their interactions.
Identify any patterns, trends, or anomalies present in the data.

Data Mining and Analysis:

Apply appropriate data mining techniques such as clustering, classification, or regression to uncover deeper insights within the dataset.
Utilize machine learning algorithms if applicable to predict or classify certain outcomes based on the available variables.
Perform feature engineering if necessary to enhance the predictive power of the model.

EDA Report:

Compile all findings, analysis, and visualizations into a comprehensive EDA report.
Structure the report to include an introduction, methodology, results, discussion, and conclusion sections.
Provide clear explanations for the steps taken, insights gained, and any challenges encountered during the analysis.
Include visualizations and summary statistics to support your findings.

Oral Presentation:

Prepare a concise oral presentation to present your EDA findings to the class.
Highlight key insights, trends, and interesting observations discovered during the analysis.
Use visual aids such as slides or interactive dashboards to enhance the presentation.

Assessment Item: Data Modelling Project (Group)

Part A - Report (1500 Words) and

Part B - Presentations Overview

Assignment Overview:
In this assignment, you will work in a group of 3 to 5 students. In this group assessment, you will collaborate with your team members to produce a comprehensive final report summarizing the achievements of credit analysis dataset, the process of building data model(s) to fit the dataset and conducting data analysis. You will also address how the results are validated and interpreted, and provide insights and recommendations derived from your analysis. Additionally, ethical and social issues related to the project must be thoroughly addressed. You will utilize appropriate tools and languages, such as Python and Tableau, to complete this task. Your group will be required to submit a report and deliver an oral presentation.

Creating Dataset:
Use the below program to generate credit analysis dataset with 5000 customer information.

Columns(information) in Dataset:
CustomerID: This column represents a unique identifier for each customer. It's typically used to track individual customers within the dataset.

CreditScore: This column represents the credit score of each customer. Credit scores are numerical representations of an individual's creditworthiness, often used by lenders to assess the risk of lending money to a borrower. Higher credit scores indicate lower credit risk.
Age: This column represents the age of each customer. Age can be an important factor in credit analysis as it may correlate with financial stability and responsibility.
Income: This column represents the income of each customer. Income is a key factor in determining creditworthiness, as it affects an individual's ability to repay loans.
LoanAmount: This column represents the amount of the loan that each customer has applied for or obtained. It indicates the sum of money borrowed from a lender.
LoanDurationMonths: This column represents the duration of the loan in months. It indicates the length of time over which the loan is expected to be repaid.
LoanPurpose: This column represents the purpose for which the loan is taken. It could include categories such as personal loans, car loans, home loans, or education loans.
EmploymentStatus: This column represents the employment status of each customer. It indicates whether the customer is employed, unemployed, or selfemployed. Employment status is important in assessing a borrower's ability to repay a loan.
DefaultStatus: This column represents whether the customer has defaulted on a loan. It's a binary column where "True" indicates that the customer has defaulted, and "False" indicates that the customer has not defaulted. Default status is a critical factor in credit analysis as it reflects the risk associated with lending to a particular customer.
Task:

Data Understanding:
Describe the key features of the credit analysis dataset generated using the provided Python code.
What are the dimensions of the dataset? How many records does it contain?
Discuss the significance of each column in the dataset and how it contributes to the credit analysis process.
Are there any missing values or outliers in the dataset? If so, how do you plan to handle them before proceeding with data modeling and analysis?

Data Modeling and Analysis:
Explain the process of building data model(s) to fit the credit analysis dataset. Which techniques or algorithms did you employ for modeling? b. What metrics or criteria did you use to evaluate the performance of your data model(s)?
Provide insights into the patterns or trends observed during data analysis. How do these insights contribute to understanding customer behavior and credit risk?
Discuss any challenges or limitations encountered during the modeling and analysis phase and how you addressed them.

Validation and Interpretation:
Describe the methods used to validate the results obtained from data modeling and analysis.
How do you interpret the outcomes of your analysis in the context of credit risk assessment?
Discuss the reliability and robustness of the insights derived from the analysis.

Insights and Recommendations:
Based on your analysis, what insights can be drawn regarding customer creditworthiness and risk management?
Provide recommendations for improving the credit assessment process or mitigating credit risk based on your findings.
How do these insights and recommendations align with the objectives of the credit analysis project?

Ethical and Social Considerations:
Identify and discuss any ethical or social issues related to the collection, usage, and analysis of the credit analysis dataset.
How did your team address these ethical and social considerations throughout the project?
What measures were implemented to ensure fairness, transparency, and accountability in the analysis and decision-making process?

Oral Presentation:
Prepare a concise oral presentation to present your findings to the class.
Highlight key insights, trends, and interesting observations discovered during the analysis.
Use visual aids such as slides or interactive dashboards to enhance the presentation.