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

Assessment Item 1: Report - Statistical Analysis of Business Data

Overview

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:

1. Descriptive statistics
2. Correlation analysis
3. Hypothesis testing
4. 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:

1. 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.

Note:

Please submit a single pdf report that includes screenshots of Python code along with corresponding results, as well as screenshots of visualizations

relevant/supporting your analysis. The screenshot of your python code must include your student ID and name clearly visible in the beginning of the script. You can also export the dataset as .csv file and use other software like Ms Excel for the same analysis.

Report Structure (suggestive)

1. Executive summary
   1. Introduction
   2. Analysis Process and Methodology
   3. Findings and insights
   4. Recommendations
   5. Code screenshots
   6. Conclusion
   7. 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.

ICT603 Data Science

Assessment 1 - Dataset generating code

Instruction- You can use this code to create your own file and

analyse the given dataset. You MUST use the given in assessment guideline to create the dataset. Refer to assessment details for more instructions.

Student ID -            < ********>

Name -                    < ******************>

campus -             < *******> Subject code -             < ICT 603>

Assessment no - < Assessment 1> """

import pandas as pd import numpy as np import random

from datetime import datetime, timedelta

# Generate 1000 random dates and times within a specific range start_date = datetime(2023, 1, 1)

end_date = datetime(2023, 12, 31)

date_times = [start_date + timedelta(seconds=random.randint(0, int((end_date - start_date).total_seconds()))) for _ in range(1000)] # Generate random customer IDs

customer_ids = ['C' + str(i).zfill(4) for i in range(1, 1001)]

# Generate random product IDs

product_ids = ['P' + str(i).zfill(3) for i in range(1, 101)] # Generate random quantities sold

quantities_sold = np.random.randint(1, 10, size=1000) # Generate random unit prices

unit_prices = np.random.uniform(1, 100, size=1000) # Calculate total transaction amounts

total_transaction_amounts = quantities_sold * unit_prices # Generate random store IDs

store_ids = ['S' + str(i).zfill(3) for i in range(1, 11)] # Randomly assign store IDs to transactions

store_ids = [random.choice(store_ids) for _ in range(1000)] # Create DataFrame

data = {

'Date & Time': date_times,

'Customer ID': random.choices(customer_ids, k=1000), 'Product ID': random.choices(product_ids, k=1000), 'Quantity Sold': quantities_sold,

'Unit Price': unit_prices,

'Total Transaction Amount': total_transaction_amounts, 'Store ID': store_ids

}

df = pd.DataFrame(data)

# Convert Date & Time column to datetime format df['Date & Time'] = pd.to_datetime(df['Date & Time']) # Sort DataFrame by Date & Time

df = df.sort_values(by='Date & Time') # Reset index

df.reset_index(drop=True, inplace=True) # Print DataFrame

print(df)

df.to_csv("ICT603_A1.csv")

###################################################

Submission Instructions

All submissions are to be submitted through the assignment 1 Drop-boxes that will be set up in the Moodle account for this Unit of Study. Assignments not submitted through these drop boxes will not be considered. Submissions must be made by the due date and time (which will be in the session detailed above) and determined by your Unit coordinator

Note: All work is due by the due date and time. Late submissions will be penalized at 20% of the assessment final grade per day, including weekends.

Marking Criteria/Rubric

You will be assessed on the following marking criteria/Rubric: Total Marks: 30

Assessment Item 2: 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:

1. 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.

Example Data Sources:

1. Kaggle Datasets (https://www.kaggle.com/datasets)
2. UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/index.php)
3. Government Open Data Portals (e.g., data.gov)
4. Academic Research Databases (e.g., PubMed, IEEE Xplore)
5. Social Media APIs (e.g., Twitter, Facebook)

1. 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.

Note: It is mandatory that Data Wrangling operations should be incorporate in the dataset.

1. 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.

1. 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.

1. 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.

1. 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.
  • Please submit two files, the Report and the Oral Presentation, through the link provided in the LMS before the specified deadline.

Note: Collaboration within the group is encouraged, but each group member must contribute substantially to the analysis, report writing, and presentation. Plagiarism or unauthorized use of external sources will result in penalties.

Submission Instructions

All submissions are to be submitted through turn-it-in. Drop-boxes linked to turn-it-in will be set up in the Unit of Study Moodle account. Assignments not submitted through these drop-boxes will not be considered.

Submissions must be made by the due date and time (which will be in the session detailed above) and determined by your Unit coordinator. Submissions made after the due date and time will be penalized at the rate of 20% per day (including weekend days).

The turn-it-in similarity score will be used in determining the level if any of plagiarism. Turn-it-in will check conference websites, Journal articles, the Web and your own class member submissions for plagiarism. You can see your turn-it-in similarity score when you submit your assignment to the appropriate drop-box. If this is a concern you will have a chance to change your assignment and re-submit. However, re-submission is only allowed prior to the submission due date and time. After the due date and time have elapsed you cannot make re-submissions and you will have to live with the similarity score as there will be no chance for changing. Thus, plan early and submit early to take advantage of this feature. You can make multiple submissions, but please remember we only see the last submission, and the date and time you submitted will be taken from that submission.

Your document should be a single word or pdf document containing your report

Note: All work is due by the due date and time. Late submissions will be penalized at 20% of the assessment final grade per day, including weekends.

Marking Criteria/Rubric

You will be assessed on the following marking criteria/Rubric:

Total Marks: 30