Project Overview
The Final Project is your opportunity to showcase everything you have learned in this Data Analytics course. You will build a complete, end-to-end analytics project that can be added to your professional portfolio. This project should demonstrate your ability to solve real-world business problems using data analytics techniques.
Excel & SQL
Data manipulation, queries, and pivot tables
Visualization
Power BI, Tableau, or Python charts
Statistics
Descriptive stats, hypothesis testing
Business Insights
Recommendations and storytelling
Project Options
Choose ONE of the following project tracks. Each track presents a unique challenge and allows you to specialize in a specific area of data analytics.
Option A: Sales Analytics Dashboard
Build a comprehensive sales analytics solution that tracks performance, identifies trends, and provides actionable insights for sales teams. Analyze revenue, products, regions, and customer segments.
Suggested Datasets
- Superstore Sales Dataset (Kaggle)
- E-Commerce Sales Data
- Retail Transaction Dataset
- AdventureWorks Sales Data
Required Analysis
- Revenue trends and forecasting
- Product performance analysis
- Regional sales comparison
- Customer segmentation insights
Option B: Financial Analytics Report
Create a financial analysis dashboard that provides insights into company performance, budgeting, and financial health. Include profitability analysis, expense tracking, and KPI monitoring.
Suggested Datasets
- Company Financial Statements
- Budget vs Actuals Data
- Stock Market Data (Yahoo Finance)
- Bank Transaction Dataset
Required Analysis
- Profit and loss analysis
- Budget variance reporting
- Cash flow visualization
- Financial ratio calculations
Option C: HR Analytics Platform
Build an HR analytics solution that analyzes workforce data, tracks employee metrics, and identifies patterns in attrition, performance, and engagement.
Suggested Datasets
- IBM HR Analytics Dataset
- Employee Performance Data
- Recruitment Pipeline Data
- Workforce Demographics Dataset
Required Analysis
- Attrition pattern analysis
- Employee demographics breakdown
- Performance distribution study
- Compensation equity analysis
Option D: Custom Analytics Project
Have your own project idea? Build something unique that demonstrates your data analytics skills. Custom projects must be pre-approved by submitting a brief proposal.
Technical Requirements
Regardless of which project option you choose, your project must include ALL of the following components:
Data Collection and Preparation
- Use a dataset with at least 5,000 rows and 10+ columns
- Document the data source clearly (Kaggle, public APIs, etc.)
- Perform thorough data cleaning (handle missing values, duplicates, outliers)
- Include a data dictionary explaining each column
Exploratory Data Analysis (EDA)
- Statistical summary of all key metrics
- At least 8 meaningful visualizations (charts, graphs, heatmaps)
- Trend analysis and pattern identification
- Clear insights documented with business context
- Data quality assessment and profiling
SQL Analysis
- Write at least 10 SQL queries demonstrating various techniques
- Include JOINs, aggregations, subqueries, and window functions
- Document each query with business question it answers
- Export query results for dashboard use
Statistical Analysis
- Calculate key descriptive statistics
- Perform at least one hypothesis test relevant to your data
- Create correlation analysis between key variables
- Document statistical findings with business interpretation
Interactive Dashboard (Power BI or Tableau)
- Create a multi-page dashboard with at least 4 pages
- Include KPI cards, charts, tables, and filters
- Implement interactivity (slicers, drill-through, tooltips)
- Apply consistent formatting and professional design
- Add navigation between dashboard pages
Python Analysis (Optional but Recommended)
- Use pandas for data manipulation and cleaning
- Create visualizations with matplotlib or seaborn
- Perform advanced analysis not easily done in Excel
- Document code in Jupyter notebooks with markdown explanations
Business Recommendations
- Provide at least 5 actionable recommendations
- Support each recommendation with data evidence
- Create an executive summary (1-2 pages)
- Discuss limitations and future analysis opportunities
Deliverables
Your final submission must include all of the following files in your Google Drive folder:
Folder Structure
Data-Analytics-Final-Project-[YourName]/
├── README.md # Project overview, setup instructions, key findings
├── data/
│ ├── raw/ # Original, unprocessed data files
│ └── processed/ # Cleaned and transformed data
├── sql/
│ └── queries.sql # All SQL queries with comments
├── excel/
│ └── analysis.xlsx # Excel analysis with pivot tables
├── notebooks/
│ ├── 01_data_exploration.ipynb # EDA notebook (if using Python)
│ └── 02_analysis.ipynb # Advanced analysis notebook
├── powerbi/
│ └── dashboard.pbix # Power BI dashboard file
├── tableau/
│ └── dashboard.twbx # OR Tableau workbook (if using Tableau)
├── docs/
│ ├── executive_summary.pdf # Executive summary document
│ └── data_dictionary.md # Data dictionary
├── screenshots/
│ ├── dashboard_page1.png # Dashboard screenshots
│ ├── dashboard_page2.png
│ └── dashboard_page3.png
└── presentation/
└── final_presentation.pdf # Optional presentation slides
README.md Must Include:
- Your full name and submission date
- Project title and executive summary (problem, approach, results)
- Dataset description and source link
- Tools used (Excel, SQL, Power BI/Tableau, Python)
- Key findings (5-7 bullet points with metrics)
- Business recommendations with supporting data
- Limitations and future analysis opportunities
Do Include
- All analysis files with clear outputs
- Working Power BI/Tableau dashboard
- Professional visualizations
- Well-documented SQL queries
- Executive summary with recommendations
- Dashboard screenshots
Do Not Include
- Very large raw data files (provide download link)
- Temporary or cache files
- Personal or sensitive information
- Broken dashboard connections
- Incomplete or draft work
- Files without clear naming
Submission
Create a Google Drive folder with the exact name shown below and share with view access:
Required Folder Name
Data-Analytics-Final-Project-[YourName]
Enter your Google Drive folder link - we will verify your files automatically
Grading Rubric
Your final project will be graded on the following criteria:
| Criteria | Points | Description |
|---|---|---|
| Data Handling and EDA | 80 | Data loading, cleaning, thorough exploratory analysis with meaningful visualizations |
| SQL Analysis | 60 | Well-written queries demonstrating various SQL techniques |
| Statistical Analysis | 50 | Appropriate statistical methods with correct interpretation |
| Dashboard Design | 120 | Professional Power BI/Tableau dashboard with interactivity and good UX |
| Business Insights | 80 | Actionable recommendations supported by data evidence |
| Documentation | 60 | Comprehensive README, executive summary, clear explanations |
| Presentation Quality | 50 | Professional formatting, consistent design, clear storytelling |
| Total | 500 |
Bonus Points (Up to 50)
- +15 pts: Python analysis with pandas and visualizations
- +15 pts: Published to Power BI Service or Tableau Public
- +10 pts: Exceptional visualizations (publication quality)
- +10 pts: Video walkthrough of dashboard
Grading Scale
Excellent
450-50090-100%
Good
400-44980-89%
Satisfactory
350-39970-79%
Needs Work
<350<70%
Ready to Submit?
Make sure you have completed all requirements and reviewed the grading rubric above.
Submit Your Final ProjectPro Tips
Project Planning
- Start with understanding your data first
- Break the project into daily milestones
- Document as you go, not at the end
- Save versions of your dashboard regularly
Quality Over Quantity
- Deep analysis beats more charts
- Explain WHY each insight matters
- Focus on actionable recommendations
- Professional design matters
Time Management
- Days 1-3: Data collection and EDA (25%)
- Days 4-6: SQL and statistical analysis (25%)
- Days 7-10: Dashboard creation (35%)
- Days 11-12: Documentation and review (15%)
Common Pitfalls
- Do not ignore data quality issues
- Avoid cluttered dashboards
- Do not make unsupported claims
- Test your files before submitting