Comprehensive Data Analytics & Visualization Project (Python + Power BI/Tableau)

Project Overview:

I am looking for a data analyst / data visualization expert to complete a comprehensive data analysis and storytelling project consisting of three full-scale subprojects, each representing a different real-world business domain.

The freelancer will be responsible for:

Data Cleaning & Preparation

Exploratory Data Analysis (EDA)

Data Visualization (using Python + Power BI/Tableau)

Data Storytelling Report & Presentation Deck

The goal is to demonstrate end-to-end analytical capability — from raw data to insights, visualization, and storytelling.

Project Objectives:

The overall objective is to analyze three distinct datasets (E-commerce, Customer Segmentation, and Financial Data) to uncover actionable insights, trends, and recommendations — each presented in both Python and BI dashboard formats (Power BI/Tableau).

Project Scope (3 Modules):
Module 1: E-commerce Sales Analysis Dashboard

Objective:
Analyze the sales performance of an online store to identify trends, top-performing categories, and regional insights.

Tasks:

Clean and preprocess e-commerce sales data (orders, regions, product categories).

Perform descriptive analysis and identify sales trends over time.

Visualize:

Line chart – sales trend over time

Bar chart – sales by product category or region

Pie chart – product contribution to total sales

Heatmap – regional performance

Deliver a Power BI/Tableau dashboard + Python visualizations.

Highlight insights such as: best-selling products, peak seasons, and customer spending trends.

Dataset Example: Kaggle – E-commerce Sales Data

Module 2: Customer Segmentation & Prediction

Objective:
Segment customers based on purchase behavior and predict future purchase tendencies using machine learning.

Tasks:

Clean and prepare customer data (demographics, purchase history).

Engineer features: total spend, average purchase value, frequency, etc.

Apply K-Means or clustering techniques for segmentation.

Build a predictive model (e.g., regression or decision tree) to forecast future purchases.

Evaluate model using accuracy/precision/recall.

Visualize customer clusters, purchase frequency, and predicted behavior.

Present findings in Python (Seaborn/Plotly) + Power BI/Tableau dashboard format.

Dataset Example: Kaggle – Online Retail Customer Segmentation Data

Module 3: Financial Data Analysis & Forecasting

Objective:
Analyze and forecast financial performance or stock prices using time series data.

Tasks:

Use stock market or financial performance dataset from Yahoo Finance or Kaggle.

Clean and transform data (handle missing values, adjust date formats).

Perform time series trend analysis and calculate moving averages.

Apply forecasting models like ARIMA or Holt-Winters to predict future prices.

Visualize:

Line charts – historical vs forecasted stock prices

Bar charts – market index comparison

Scatter plots – risk vs return

Create an interactive Power BI/Tableau dashboard summarizing financial health and future projections.

Dataset Example: Yahoo Finance (via yfinance Python library) or Kaggle stock datasets.

General Tasks Across All Modules:

Dataset Selection & Importing: Identify or download datasets from Kaggle, Yahoo Finance, or UCI ML Repository.

Data Cleaning & Preparation: Handle missing values, duplicates, and irrelevant columns; convert data types; aggregate or derive new columns.

Exploratory Data Analysis (EDA): Perform descriptive statistics, correlations, and trend identification.

Data Visualization: Use both Python (Matplotlib, Seaborn, Plotly) and Power BI/Tableau to present results visually.

Data Storytelling Report: Develop a structured narrative:

Problem Statement

Methodology

Insights & Findings

Recommendations

Conclusion

Tools & Technologies:

Programming: Python (Pandas, NumPy, Matplotlib, Seaborn, Plotly, Scikit-learn)

BI Tools: Power BI or Tableau (for dashboards)

Optional: SQL (for data extraction or transformation)

Expected Deliverables:
For Each Module (3 total):

Cleaned dataset file (.csv or .xlsx)

Python code (.py or .ipynb) with all analysis steps clearly documented

Power BI or Tableau dashboard file (.pbix / .twbx)

Visualizations (minimum 5 per module: line, bar, pie, scatter/heatmap, boxplot etc.)

Summary report (PDF/Word) containing:

Problem statement & objective

Cleaning and analysis steps

Insights and findings

Recommendations

Presentation deck (PowerPoint or PDF) with visual storytelling (10–12 slides)

Additional Notes:

The freelancer must explain key steps (data transformations, model choices, and visualization logic).

All code must be well-commented and reproducible.

Dashboard visuals should be clean, professional, and interactive (filters, slicers, etc.).

The final report and presentation should be suitable for submission as an academic or corporate project.