A synthetic Bangladesh-focused HRBP and business-recovery analytics portfolio.
Workforce strategy, manufacturing recovery and executive insight across Excel, Power BI, Python, SQL, SQLite and Kaggle.
Overview · Dataset Use · Architecture · Repository · Quick Start · Ethics
📌 Pinned documents: Code of Conduct · Dataset Usage Guide
Sabia Group HRBP Smartwatch Recovery 2026 is a synthetic HR Business Partner and analytics project created by Musa. It simulates a smartwatch manufacturer connecting workforce decisions with production quality, productivity and financial recovery.
Business improvement through systems, not system building alone.
| Starting workforce | Year-end workforce | Final first-pass yield | Final defect rate |
| Area | Coverage |
|---|---|
| Context | Bangladesh-focused synthetic practice data |
| HR scope | Workforce, recruitment, training, performance, ER and HR technology |
| Business scope | Production, quality, productivity, costs and profit |
| Timeline | Q1–Q4 2026 transformation journey |
| Core tools | Excel, Power BI, Python, SQL and SQLite |
| Publishing | GitHub and Kaggle |
Use this project to practise:
- 👥 workforce planning, headcount and critical-skill analysis;
- 🎯 recruitment funnel, time-to-fill and cost-per-hire calculations;
- 🎓 training completion, certification and skill-improvement analysis;
- 🏭 production, FPY, defect, rework and productivity reporting;
- 💰 revenue, operating cost, profit and workforce-cost calculations;
- 🧹 raw-data cleaning, validation and ETL;
- 📊 Excel, Power BI, Python, SQL and SQLite portfolio projects.
The complete calculation formulas, workflows, use cases and platform instructions are available here:
📘 Open the Complete Dataset Usage Guide
%%{init: {"flowchart": {"nodeSpacing": 18, "rankSpacing": 24, "curve": "linear"}}}%%
flowchart LR
A["Synthetic HR, production<br/>and financial files"] --> B["Cleaning and validation<br/>Excel · Python · SQL"] --> C["Clean analytical<br/>tables and audits"]
C --> D["Excel master model"]
C --> E["SQLite database"]
D --> F["Power BI dashboards"]
E --> F
E --> G["SQL analysis"]
E --> H["Python EDA"] --> I["Kaggle publishing"]
flowchart TB
ROOT["sabia-hrbp/"]
ROOT --> AUTO[".github/workflows<br/>Validation"]
ROOT --> MASTER["00_Master/<br/>Excel master model"]
ROOT --> PHASES["Q1–Q4 transformation"]
PHASES --> Q1["01_Q1_Plan_and_Reset/"]
PHASES --> Q2["02_Q2_Controlled_Pilot/"]
PHASES --> Q3["03_Q3_Scale_and_Stabilize/"]
PHASES --> Q4["04_Q4_Full_Rollout/"]
ROOT --> DATA["Data layers"]
DATA --> RAW["05_Raw_Data/"]
DATA --> CLEAN["06_Clean_Data/"]
ROOT --> TOOLS["Analytics tools"]
TOOLS --> PY["07_Python/"]
TOOLS --> PBI["08_PowerBI/"]
TOOLS --> LOOKER["09_Looker_Studio/"]
TOOLS --> KAGGLE["10_Kaggle/"]
TOOLS --> SQL["13_Database_SQL/"]
ROOT --> DOCS["Documentation"]
DOCS --> GUIDE["DATASET_USAGE_GUIDE.md"]
DOCS --> PROJECTDOCS["11_Documentation/"]
DOCS --> WIKI["wiki/"]
View directory guide
| Path | Purpose |
|---|---|
00_Master/ |
Excel master analytics workbook |
01_Q1_Plan_and_Reset/ |
Feasibility and workforce reset |
02_Q2_Controlled_Pilot/ |
Pilot design and evaluation |
03_Q3_Scale_and_Stabilize/ |
Scale-up and stabilization |
04_Q4_Full_Rollout/ |
Enterprise rollout |
05_Raw_Data/ |
Messy files for cleaning practice |
06_Clean_Data/ |
Analysis-ready datasets |
07_Python/ |
Cleaning, validation and EDA |
08_PowerBI/ |
Model, DAX and dashboard guidance |
09_Looker_Studio/ |
BI connector guidance |
10_Kaggle/ |
Dataset and notebook publishing assets |
11_Documentation/ |
Business case, methodology and ethics |
13_Database_SQL/ |
SQLite database, views and SQL library |
wiki/ |
GitHub Wiki-compatible documentation |
| Table | Primary purpose |
|---|---|
| Employee Master | Workforce profile, status, cost and skills |
| Attendance Monthly | Absence, overtime, lateness and safety |
| Recruitment Funnel | Hiring conversion, cost and time-to-fill |
| Training Records | Learning, assessment, certification and cost |
| Production Monthly | Output, FPY, defects and productivity |
| Financial Impact Monthly | Revenue, cost, profit and recovery |
| Pilot Results | Baseline, target, pilot and control comparison |
| Quarterly Scorecard | Executive Q1–Q4 KPI tracking |
| HR Pillar Scores | HR operating-model improvement |
| Data Dictionary | Definitions, grain and metadata |
git clone https://github.com/samusa099/sabia-hrbp.git
cd sabia-hrbp
python -m pip install -r 07_Python/requirements.txt
python 07_Python/clean_and_validate.py
python 07_Python/eda_hrbp_recovery.pypython 13_Database_SQL/00_build_database.pySELECT *
FROM vw_bi_quarterly_business_summary
ORDER BY Quarter;| Quarter | Focus |
|---|---|
| Q1 | Feasibility, workforce diagnosis and reset planning |
| Q2 | 25-person controlled pilot on Line A |
| Q3 | Critical-skill hiring, scaling and stabilization |
| Q4 | Group-wide rollout, benefits review and governance |
All people, entities, events, production results and financial values are fictional and synthetically generated.
- No real employee or confidential company data is included.
- The project must not be used to make real employment decisions.
- Results do not establish causal relationships.
- Real use requires legal, labour-law, privacy and ethical review.
HRBP | HR & Data Analytics Practitioner | Bangladesh
Workforce Strategy · People Analytics · Business Recovery · Excel · Power BI · Python · SQL
Code of Conduct · Dataset Usage Guide · SQL & Database Guide · Power BI Guide · Live Kaggle Dataset
Practice data. Real analytical thinking. Business-focused HRBP portfolio.