VSCode-Python-2026

Python Development Workspace - BairesDev Technical Assessments 2026

🏆 Project Overview

This repository contains solutions to various technical assessments covering algorithms, data structures, SQL database queries, ETL/Data Engineering concepts, and Python data science challenges completed in February 2026.

Assessment Results

Status: ✅ All Challenges Completed
Overall Performance: Perfect Scores Achieved
Date: February 2026

📚 Contents

1. Algorithms & Data Structures (8 Questions)

• ✅ Merge Sort complexity analysis (O(n log n))
• ✅ QuickSort average case analysis (O(n log n))
• ✅ Binary Search for sorted arrays (O(log n))
• ✅ Stack data structure use cases
• ✅ Balanced tree operations (O(log n))
• ✅ Tree properties and relationships
• ✅ Tree traversal methods (Inorder, Preorder, Postorder)
• ✅ Recursive algorithms

2. SQL Database Challenges (4 Problems)

All solutions located in MYSQL-DATA-2026/ directory:

Challenge 1: Employee Salary Aggregations

• Calculate SUM, MIN, MAX of employee salaries
• File: employee_salary_aggregations.sql

Challenge 2: Travel Agency Trip Counter

• Count trips between locations with alphabetical ordering
• File: travel_agency_trip_counter.sql

Challenge 3: Customer Order Analysis

• Calculate total spending per customer with name formatting
• File: customer_order_analysis.sql

Challenge 4: Job Applicant Position Assignment

• Assign Senior/Intermediate/Junior levels based on qualifications
• File: job_applicant_position_assignment.sql

3. ETL & Data Engineering (7 Questions)

• ✅ ETL testing purposes and methodologies
• ✅ Common data sources (databases, flat files, web pages)
• ✅ Transformation operations (joins, filtering, aggregation)
• ✅ Benefits of ETL tools vs. custom scripts
• ✅ Common ETL tools (Talend, Informatica, MuleSoft)
• ✅ Data warehouse vs. data lake architecture
• ✅ Challenges faced by ETL data engineers

Documentation: MYSQL-DATA-2026/Data Engineer Coding Challenge.md

4. Python Data Science Challenge: Outlier Detection 🏆

Challenge: FMCG Sales Data - Detect and Replace Outliers
Score: 🎉 100/100 (Perfect Score!)
Status: ✅ ACCEPTED
Execution Time: 0.618 seconds
Memory Usage: 195.8 MB

Problem Statement

Working with sales data from an FMCG company to detect and replace outliers in the revenue column using a percentile-based approach.

Solution Approach

• Calculate 1st and 99th percentiles as outlier bounds
• Identify values outside these bounds
• Replace outliers with min/max from non-outlier data
• Validate and save cleaned data

Key Features

• ✅ Percentile-based outlier detection (1st/99th)
• ✅ Pandas/NumPy data manipulation
• ✅ Comprehensive execution logging system
• ✅ Step-by-step documentation
• ✅ Performance metrics tracking
• ✅ Automatic audit trail generation

Files

Jupyter Notebook (Interactive):

• Notebook: outlier_detection_sales_revenue.ipynb - Full solution with documentation

Python Scripts (Standalone):

• Full-Featured: outlier_detection.py - Modular design, production-ready
• Simple: outlier_detection_simple.py - Quick execution, minimal code
• With Logging: outlier_detection_with_logging.py - Comprehensive audit trail

Data Files:

• Dataset: dataset/data.csv (40 rows)
• Output: submission.csv
• Logs: execution_log_YYYYMMDD_HHMMSS.txt

See: PYTHON-SCRIPTS-GUIDE.md for detailed usage instructions

🛠️ Technical Stack

Programming Languages:

• Python 3.14.2
• SQL (MySQL dialect)

Python Libraries:

• pandas 2.x - Data manipulation and analysis
• numpy 1.x - Numerical computing
• jupyter - Interactive notebook environment

Development Environment:

• VS Code with Python extension
• Jupyter Notebook integration
• Virtual Environment (.venv)
• Git version control

Database:

• MySQL for SQL challenges

📂 Project Structure

Python-vscode-2026/
│
├── README.md                              (This file)
├── CodeCitations.md                       (Code references)
│
├── Algorithm Challenges/
│   ├── array_algorithms_challenge.py
│   ├── counting_bits.py
│   ├── getMaximumSubarray.py
│   ├── list_operations_challenge.py
│   ├── permutation_subarray_challenge.py
│   ├── recursion_algorithms_challenge.py
│   ├── solvePeaksProblem.py
│   ├── string_algorithms_challenge.py
│   └── string_manipulation_challenge.py
│
├── MYSQL-DATA-2026/                       (SQL & Database Files)
│   ├── Data Engineer Coding Challenge.md
│   ├── employee_salary_aggregations.sql
│   ├── travel_agency_trip_counter.sql
│   ├── customer_order_analysis.sql
│   ├── job_applicant_position_assignment.sql
│   ├── database_queries.py
│   ├── PYMySQLdatabase_queries.py
│   └── sqlite3-database-queries.py
│
├── Data Science Challenge/
│   ├── outlier_detection_sales_revenue.ipynb
│   ├── submission.csv
│   ├── execution_log_*.txt
│   └── dataset/
│       └── data.csv
│
├── Session Outputs/
│   ├── BairesDev-Assement-SESSION OUTPUT-02-05-2026.txt
│   ├── BairesDev2-Assement2-SESSION2 OUTPUT-02-05-2026.txt
│   ├── BairesDev3-Assement3-SESSION3-OUTPUT-02-07-2026.txt
│   ├── Python-Data-Science-Challenge-SESSION-OUTPUT-02-07-2026.txt
│   └── Outlier-Detection-Challenge-SESSION-OUTPUT-02-07-2026.txt
│
└── filliat-Assement-notes-2026/
    └── [Assessment context and notes]

🚀 Getting Started

Prerequisites

# Python 3.14.2 or higher
python --version

# Virtual environment
python -m venv .venv

Installation

1. Clone the repository:

git clone https://github.com/brianfilliat/VSCode-Python-2026.git
cd VSCode-Python-2026

2. Activate virtual environment:

# Windows PowerShell
.\.venv\Scripts\Activate.ps1

# Linux/Mac
source .venv/bin/activate

3. Install required packages:

pip install pandas numpy jupyter

4. Launch Jupyter Notebook or run Python scripts:

# Option 1: Jupyter Notebook (Interactive)
jupyter notebook outlier_detection_sales_revenue.ipynb

# Option 2: Python Script (Quick)
python outlier_detection_simple.py

# Option 3: Python Script (Full-featured)
python outlier_detection.py

# Option 4: Python Script (With Logging)
python outlier_detection_with_logging.py

See PYTHON-SCRIPTS-GUIDE.md for detailed script usage and comparisons.

📊 Assessment Highlights

Perfect Score Achievement: Outlier Detection Challenge

Key Success Factors:

1. Correct Algorithm Implementation

   • Proper percentile calculation using quantile(0.01) and quantile(0.99)
   • Replacement with non-outlier min/max (not percentile bounds)
   • Efficient pandas vectorized operations

2. Comprehensive Validation

   • Verified no outliers remain after replacement
   • Performance metrics tracking (time, memory)
   • Data integrity checks

3. Professional Documentation

   • Step-by-step execution logging
   • Automated audit trail generation
   • Statistics comparison (before/after)
   • Detailed code comments

SQL Expertise Demonstrated

Advanced SQL Features Used:

• Aggregate functions (SUM, MIN, MAX, COUNT)
• String manipulation (CONCAT, UPPER)
• JOINs (INNER JOIN for multi-table queries)
• GROUP BY with multiple columns
• Complex CASE statements
• HAVING clause for filtering aggregated results
• Multi-column ORDER BY (ASC/DESC)

Data Engineering Knowledge

ETL Concepts Covered:

• Pipeline design and testing
• Data source identification
• Transformation operations
• Tool selection criteria
• Data warehouse vs. data lake
• Common challenges and solutions

🎯 Learning Outcomes

Skills Developed

• ✅ Algorithm analysis and complexity evaluation
• ✅ SQL query optimization and best practices
• ✅ Data cleaning and preprocessing techniques
• ✅ Statistical outlier detection methods
• ✅ Python data manipulation with pandas
• ✅ ETL pipeline design principles
• ✅ Professional documentation practices
• ✅ Performance monitoring and optimization

Technical Proficiencies

• Python programming (advanced level)
• SQL database querying (MySQL)
• Data analysis with pandas/numpy
• Jupyter notebook development
• Git version control
• VS Code development environment
• Virtual environment management

📖 Documentation

Each challenge includes detailed documentation:

• SQL Challenges: Comprehensive markdown files with problem statements, table structures, sample data, expected outputs, and detailed explanations
• Python Challenge: Jupyter notebook with step-by-step implementation, markdown documentation, execution logs, and results analysis
• Session Outputs: Complete conversation logs capturing all questions, answers, and implementation details

🔍 Code Quality

Best Practices Implemented

• Clean, readable code with proper naming conventions
• Comprehensive comments and documentation
• Modular design with reusable functions
• Error handling and validation
• Performance optimization
• Memory efficiency
• Professional logging and monitoring

Testing & Validation

• All SQL queries tested with sample data
• Python solution validated against test cases
• Performance metrics within specified limits
• Data integrity verification
• Edge case handling

📝 Session Outputs

Complete session logs documenting the entire assessment process:

1. BairesDev-Assement-SESSION OUTPUT-02-05-2026.txt

   • Initial algorithm and data structure questions
   • SQL challenge solutions

2. BairesDev2-Assement2-SESSION2 OUTPUT-02-05-2026.txt

   • Continued technical assessments
   • Additional problem-solving

3. BairesDev3-Assement3-SESSION3-OUTPUT-02-07-2026.txt

   • Complete assessment with 19 questions
   • Algorithm, SQL, and ETL coverage

4. Python-Data-Science-Challenge-SESSION-OUTPUT-02-07-2026.txt

   • Detailed outlier detection challenge documentation
   • Complete implementation guide

5. Outlier-Detection-Challenge-SESSION-OUTPUT-02-07-2026.txt

   • Concise challenge summary
   • Results and key takeaways

🤝 Contributing

This is a personal assessment project. However, if you find any issues or have suggestions for improvements, feel free to open an issue or submit a pull request.

📄 License

This project is for educational and assessment purposes.

👤 Author

Brian Filliat

• GitHub: @brianfilliat
• Repository: VSCode-Python-2026

🙏 Acknowledgments

• BairesDev for providing comprehensive technical assessments
• FMCG company case study for the outlier detection challenge
• Open source community for pandas, numpy, and Jupyter

📅 Timeline

• February 5, 2026: Initial algorithms and SQL challenges completed
• February 7, 2026: ETL questions and Python data science challenge completed with perfect score
• Status: All assessments completed successfully ✅

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Last Updated: February 7, 2026
Status: Complete ✅
Overall Result: Perfect Scores Achieved 🏆