Reference no: EM133767618

Data Science

Report - Statistical Analysis of Business Data

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.

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

Assessment Item: Data Acquisition and Data Mining (Group) Part A - Report and
Part B- Oral Presentation
Overview

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.

Submission Guidelines:

The EDA report of 1000 words must be submitted digitally, either in PDF or Word document format. The report should include an appendix at the end containing screenshots of the Python code along with its corresponding output
The oral presentation can be delivered using presentation software (e.g., PowerPoint, Google Slides).
Ensure proper citation and referencing for any external sources or datasets used.