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Data Management Project: "Data Insights Dashboard"
Project Description
The “Data Insights Dashboard” is an immersive data management and analytics project designed to evaluate your data handling, visualization, and interpretation skills using the provided “Sample E-commerce Sales Dataset.” In just 10 hours, you will create a dynamic data dashboard from scratch. This dashboard will serve as a powerful tool to visualize and analyze the sales dataset, making it a valuable asset for your data-driven career.
Project Objectives
Data Integration: Ensure seamless integration of the provided “Sample E-commerce Sales Dataset” into your project.
Interactive Visualization: Develop interactive data visualizations using tools like Python (Matplotlib, Seaborn, Plotly), R (ggplot2), or any data visualization library of your choice.
Data Exploration: Perform in-depth data exploration, including summary statistics, distribution analysis, and data profiling, specifically focused on the provided sales dataset.
Dashboard Design: Create an intuitive dashboard layout with multiple visualizations that effectively convey data insights.
Filtering and Interaction: Implement interactive filters and user-friendly controls for exploring the sales data.
Data Storytelling: Develop a narrative that guides users through the sales data, highlighting key findings and trends.
Responsive Design: Ensure that your dashboard is responsive and accessible on various devices, including desktops, tablets, and mobile phones.
Data Privacy: Prioritize data privacy and security considerations in your project.
Project Requirements
Utilize data analysis and visualization tools of your choice (e.g., Python, R, Tableau, Power BI).
Document your data sources, data cleaning process, and analysis steps.
Make use of version control (e.g., Git) to track your project’s progress.
Host your data dashboard on a platform for easy access.
GitHub Repository: Please ensure that you post your project on the GitHub repository at the following link: Data Management Repository.
Evaluation Criteria
Your project will be evaluated based on the following criteria:
Data Integration: How effectively you integrated the provided sales dataset into your project.
Interactive Visualization: The quality and interactivity of data visualizations.
Data Exploration: The depth and accuracy of your data exploration, including summary statistics and data profiling.
Dashboard Design: The usability, layout, and aesthetics of the dashboard.
Filtering and Interaction: The implementation of interactive features for data exploration.
Data Storytelling: Your ability to communicate meaningful insights and trends in the sales data.
Responsive Design: How well your dashboard adapts to different screen sizes and devices.
Data Privacy: The measures taken to protect data privacy and security.
Submission
You will have 10 hours to complete this project, starting from the moment you access the project materials. Once completed, submit the link to your live data dashboard hosted on GitHub and any relevant project files/resources, zipped or compressed into a single file, through the submission form provided.
Data Source
You can access the “Sample E-commerce Sales Dataset” at the following link: sales.csv
Here are some example columns and the type of data they might contain:
Order ID: A unique identifier for each sales order.
Order Date: The date when the order was placed.
Product Name: The name of the product sold.
Product Category: The category to which the product belongs (e.g., Electronics, Clothing, Home & Garden).
Quantity: The quantity of the product sold in each order.
Price per Unit: The price of one unit of the product.
Total Price: The total price of the order (Quantity x Price per Unit).
Customer Name: The name of the customer who made the purchase.
Customer Email: The email address of the customer.
Shipping Address: The address where the order was shipped.
Remember, this project is not just about meeting the requirements but also about showcasing your passion for data management and analytics and your ability to derive valuable insights from the provided sales dataset. Good luck, and enjoy the journey of building your Data Insights Dashboard!