This Python-based capstone project simulates a real-world corporate data transformation challenge involving HR and project management analytics. The focus was on cleaning, transforming, and integrating three key datasets — Employee, Project, and Seniority Level — using Python, Pandas, and NumPy, while embedding business logic and automation strategies into the workflow.
Data Analyst | Python Developer
Capstone Project
Python | Pandas | NumPy | Jupyter Notebook | CSV Handling | Data Cleaning | Business Logic | Data Aggregation
- Clean and preprocess interrelated datasets.
- Automate business logic such as bonus calculation, promotion/demotion logic, and designation updates.
- Simulate HR and operational workflows using Python and Pandas fundamentals.
- Deliver clean, aggregated datasets ready for executive-level reporting.
- Created three core DataFrames:
Employee,Project, andSeniorityLevel. - Exported as
.csvfiles to simulate modular, real-world workflows and persistent storage.
- Handled missing
Project Costvalues using a custom for-loop-based running average imputation (not in-built functions), reflecting real-world iterative logic. - Split combined
Namecolumn intoFirst NameandLast Name, then dropped the original to allow granular identity analysis.
- Performed multi-table joins using ID as the primary key to form a unified
FinalDataFrame. - This joined dataset mirrored relational database consolidation for analytics.
- Added a
Bonuscolumn calculated as 5% of theProject Costfor completed projects (status = Finished).
- Demoted employees (designation -1) whose project status was
Failed. - Deleted records where designation level exceeded
4(non-compliant with company policy).
- Applied conditional promotion: employees over the age of 29 were promoted (designation +1), reflecting seniority-driven growth.
- Added
"Mr."or"Mrs."prefix toFirst Namebased on gender and dropped theGendercolumn — enhancing professional identity representation.
- Created a new DataFrame:
TotalProjCost, summarizing total project cost handled by each employee usinggroupbyoperations. - Used for executive reporting and cost attribution per resource.
- Selected all employees whose
Citynames contain the letter"o"— demonstrating text pattern filtering and customer segmentation logic.
- ✅ Data Cleaning & Preprocessing
- 🔁 For-Loop Calculations for Missing Values
- 🧱 DataFrame Merging (Multi-Table Joins)
- 🧠 Conditional Logic (if/else, multiple conditions)
- 📊 GroupBy & Aggregation
- 🔤 String Manipulation
- 🔄 Feature Engineering
- 🗂️ CSV Read/Write & Data Export
- 👨💼 HR & Operational Business Simulation
- 📓 Jupyter Notebook (.ipynb) with step-by-step code and in-line documentation
- 🗃️ Cleaned CSV exports for each DataFrame
- 📊
TotalProjCostDataFrame with grouped analytics - 🧾 Fully documented transformation and logic pipeline
This capstone provided a practical simulation of real-world data operations where data preprocessing, logic-based transformations, and reporting automation are key. It sharpened my ability to handle business requirements through clean, readable, and effective Python code. The logic structures mimic actual HR operations, bonus structures, and project performance metrics—offering transferable value to multiple industries.
Python for Data Analysis | Pandas | NumPy | Data Cleaning | Business Logic | Project Analytics | HR Analytics | Real-World Data Simulation | Conditional Logic | Feature Engineering | Aggregation | Jupyter Notebook | Employee Performance | CSV Operations | Designation Management | Bonus Logic | Python Capstone
Focused on code clarity, business relevance, and transformation accuracy.