A portfolio of data analysis projects across multiple tools and technologies, covering real-world use cases in logistics, operations, supply chain, and business intelligence.
data_analysis_projects/
│
├── excel/
│ ├── 01_inventory_control/
│ ├── 02_kpi_dashboard/
│ ├── 03_inventory_parameters/
│ ├── 04_supplier_evaluation/
│ └── 05_budget_tracking/
│
├── python/
│ ├── 01_sales_eda/
│ ├── 02_inventory_optimization/
│ ├── 03_web_scraping/
│ ├── 04_etl_pipeline/
│ └── 05_predictive_maintenance/
│
├── tableau/
│ ├── 01_logistics_dashboard/
│ ├── 02_sales_performance/
│ ├── 03_customer_segmentation/
│ ├── 04_supply_chain_map/
│ └── 05_financial_overview/
│
├── power_bi/
│ ├── 01_warehouse_kpis/
│ ├── 02_procurement_analysis/
│ ├── 03_project_tracking/
│ ├── 04_hr_headcount/
│ └── 05_operational_costs/
│
├── sql/
│ ├── 01_inventory_queries/
│ ├── 02_sales_reporting/
│ ├── 03_customer_analysis/
│ ├── 04_supply_chain_queries/
│ └── 05_data_cleaning/
│
└── machine_learning/
├── 01_demand_forecasting/
├── 02_anomaly_detection/
├── 03_classification_model/
├── 04_clustering_customers/
└── 05_regression_analysis/
| Category |
Tools |
| Spreadsheets |
Microsoft Excel (Advanced), Power Query |
| Programming |
Python (pandas, numpy, matplotlib, seaborn, scikit-learn, requests, BeautifulSoup, sqlalchemy) |
| BI & Visualization |
Tableau, Power BI |
| Databases |
SQL (MySQL / PostgreSQL), SQLite |
| Machine Learning |
scikit-learn, statsmodels |
| Version Control |
Git, GitHub |
| # |
Project |
Description |
Key Skills |
| 01 |
Inventory Control |
ABC/XYZ analysis on Amazon e-commerce sales data. Classifies 7,116 SKUs by value and demand variability across 12 months |
Pivot Tables, SUMIF, COUNTIF, Conditional Formatting, VLOOKUP |
| 02 |
KPI Dashboard |
Operational sales KPI dashboard using Walmart weekly sales data across 45 stores and 3 years |
Dashboard Design, Slicers, Dynamic Charts, Named Ranges |
| 03 |
Inventory Parameters |
Safety stock, reorder point, EOQ and value at risk calculation for 290 SKUs using 25 months of real sales history |
NORM.S.INV, VLOOKUP, SQRT, CEILING, Pivot Tables |
| 04 |
Supplier Evaluation |
Multi-criteria decision model (MCDM) to evaluate and rank 35 suppliers across 12 weighted criteria |
SUMPRODUCT, Min-Max Normalization, INDEX/MATCH, RANK |
| 05 |
Budget Tracking |
Budget vs actual variance analysis across 6 cost categories over 12 months with executive dashboard |
SUMIF, Pivot Tables, Conditional Formatting, Clustered Bar Chart |
| # |
Project |
Description |
Key Libraries |
| 01 |
Sales EDA |
Exploratory data analysis on Amazon India sales dataset to identify consumption patterns, discount effectiveness, and customer satisfaction |
pandas, matplotlib, seaborn, re |
| 02 |
Inventory Optimization |
Calculate optimal inventory parameters (EOQ, Safety Stock, Reorder Point) for 303 SKUs using 2.5 years of sales data with ABC classification and risk assessment |
pandas, numpy, scipy, matplotlib, seaborn |
| 03 |
Web Scraping |
Extract, clean and analyze book data from public e-commerce website. Demonstrates HTTP requests, HTML parsing with BeautifulSoup, pagination handling, and data export |
requests, BeautifulSoup, pandas, matplotlib, seaborn |
| 04 |
ETL Pipeline |
End-to-end ETL pipeline on a 9-file relational e-commerce dataset (Olist, 100K orders). Covers multi-source extraction, datetime correction, null handling, feature engineering, category translation, SQLite loading via SQLAlchemy, and business SQL queries |
pandas, numpy, sqlalchemy |
| 05 |
Predictive Maintenance |
Failure prediction using classification model |
scikit-learn, pandas |
| # |
Project |
Description |
| 01 |
Logistics Dashboard |
End-to-end shipment tracking and on-time delivery KPIs |
| 02 |
Sales Performance |
Regional sales breakdown with trend analysis |
| 03 |
Customer Segmentation |
RFM segmentation visualized by cluster |
| 04 |
Supply Chain Map |
Geographic flow map of supply chain network |
| 05 |
Financial Overview |
Revenue, cost and margin summary for management |
| # |
Project |
Description |
| 01 |
Warehouse KPIs |
Fill rate, turnover, accuracy - warehouse operations report |
| 02 |
Procurement Analysis |
Spend analysis by category, supplier and period |
| 03 |
Project Tracking |
Gantt-style project progress with milestone tracking |
| 04 |
HR Headcount |
Headcount evolution, turnover and department breakdown |
| 05 |
Operational Costs |
Cost center analysis with drill-through by category |
| # |
Project |
Description |
| 01 |
Inventory Queries |
Stock level queries, reorder alerts, dead stock detection |
| 02 |
Sales Reporting |
Revenue aggregation, growth rates, top products |
| 03 |
Customer Analysis |
Purchase frequency, lifetime value, retention rate |
| 04 |
Supply Chain Queries |
Lead time analysis, supplier performance |
| 05 |
Data Cleaning |
Deduplication, null handling, type normalization |
| # |
Project |
Description |
| 01 |
Demand Forecasting |
Time series model to predict future demand |
| 02 |
Anomaly Detection |
Detect outliers in operational or sensor data |
| 03 |
Classification Model |
Binary classification for risk or failure prediction |
| 04 |
Customer Clustering |
Unsupervised segmentation using K-Means |
| 05 |
Regression Analysis |
Predict continuous variable (cost, time, quantity) |
- Python 3.9+
- pip or conda
- Power BI Desktop (for .pbix files)
- Tableau Public or Tableau Desktop (for .twbx files)
git clone https://github.com/Gainciarte/data_analysis_projects.git
cd data_analysis_projects
pip install -r requirements.txt
- Each project folder contains its own
README.md with objective, data source, methodology and results.
- Raw data files are not included in this repository due to file size. Each project README links to the original data source.
- Datasets used are either publicly available or anonymized synthetic data.
- Projects are added progressively - check back for updates.
- Excel 01: Stock Status analysis was excluded as the dataset contains sales data only, not inventory levels. This limitation is documented in the project README.
- Excel 03: renamed from Demand Forecast to Inventory Parameters to better reflect the actual analysis performed.
| Project |
Dataset |
Source |
| Excel 01 - Inventory Control |
Amazon Sale Report (e-commerce sales data) |
Kaggle |
| Excel 02 - KPI Dashboard |
Walmart Sales (45 stores, weekly sales 2010-2012) |
Kaggle |
| Excel 03 - Inventory Parameters |
Dynamic Inventory Analytics - Kaizen Analytics |
Kaggle |
| Excel 04 - Supplier Evaluation |
Suppliers Ranking Grades |
Kaggle |
| Excel 05 - Budget Tracking |
Financial Dataset - Expenses Budget vs Actual |
Kaggle |
| Python 01 - Sales EDA |
Amazon Sales Dataset |
Kaggle |
| Python 02 - Inventory Optimization |
Dynamic Inventory Dataset - Kaizen Analytics |
Kaggle |
| Python 03 - Web Scraping |
Books to Scrape (practice website) |
http://books.toscrape.com/ |
| Python 04 - ETL Pipeline |
Brazilian E-Commerce Public Dataset by Olist |
Kaggle |
Gustavo Inciarte
Mechanical Engineer | Data & Logistics Analyst
LinkedIn · GitHub
This portfolio is part of an ongoing professional development initiative focused on data analysis applied to logistics, supply chain and operations.