Overview
Python programming language is my primary programming language for data analysis, machine learning, and reproducible reporting. I use Python to clean and preprocess datasets, perform exploratory and statistical analysis, visualize insights, and build predictive models. My Python workflows leverage a combination of powerful libraries including pandas, NumPy, Matplotlib, Seaborn, Plotly, and Scikit-learn, which allows me to efficiently handle data from multiple sources, analyze it, and communicate results through interactive or publication-ready visualizations.
I frequently work in Jupyter Notebook and VS Code to create well-documented, reproducible workflows. I also integrate Python scripts into automated pipelines for data preprocessing, feature engineering, and reporting.
Skills Demonstrated with Python programming language
- Data wrangling and preprocessing using pandas and NumPy: cleaning missing or inconsistent data, converting data types, handling duplicates, filtering and aggregating datasets, and creating new derived features for analysis.
- Data visualization using Matplotlib, Seaborn, and Plotly: generating both static and interactive visualizations to communicate insights effectively.
- Exploratory data analysis (EDA): identifying patterns, trends, and anomalies in datasets to guide deeper analysis.
- Statistical analysis: performing hypothesis testing, correlation analysis, and regression modeling.
- Predictive modeling using Scikit-learn: building, evaluating, and tuning machine learning models for classification and regression tasks.
- Reproducible reporting and automation: writing clean, modular code that can be reused or scheduled in automated data workflows.
Sample Projects with Python programming language
Project 1: Investment Analysis of Unicorn Companies
- Goal: Analyze a dataset of private companies valued at $1B+ (“unicorns”) to identify industry and geographic trends and inform investment decisions.
- Tools: Python, pandas, NumPy, Matplotlib, Seaborn, Plotly
- Key tasks:
- Cleaned and preprocessed the dataset: corrected bad data, removed duplicates, handled missing values, and standardized categorical variables such as industry and continent.
- Created derived metrics including “Years to Unicorn” and “High Valuation” for comparative analysis.
- Encoded categorical variables using label encoding and one-hot encoding for analysis.
- Generated visualizations: distribution of unicorns by industry, continent, and valuation; trends in years to reach unicorn status; and relationships between company age, valuation, and geography.
- Outcome: Produced an interactive report in Jupyter Notebook combining visualizations and insights that highlighted emerging markets and industries with high potential for future unicorns.
Project 2: Customer Churn Analysis with Python programming language
- Goal: Identify factors contributing to customer churn for a subscription service using historical user data.
- Tools: Python, pandas, Seaborn, Matplotlib, Scikit-learn
- Key tasks:
- Conducted exploratory data analysis to understand patterns in customer behavior.
- Handled missing data and converted categorical variables to numeric form for modeling.
- Built and evaluated predictive models using logistic regression and decision trees to identify key churn drivers.
- Visualized relationships between features and churn outcomes with correlation heatmaps, distribution plots, and bar charts.
- Outcome: Developed a predictive model with strong accuracy and insights that informed retention strategies, including targeted campaigns for high-risk customer segments.
Project 3: Interactive Data Dashboard for Sales Analysis
- Goal: Provide an interactive dashboard for a retail company to monitor sales performance across regions and products.
- Tools: Python, pandas, Plotly, Dash
- Key tasks:
- Aggregated and cleaned sales data from multiple sources.
- Created interactive charts and filters to explore sales by region, product category, and time period.
- Built a dashboard using Dash to allow stakeholders to explore insights dynamically.
- Outcome: Delivered a fully interactive, user-friendly dashboard that helped the company track sales performance and identify high-performing products and regions.
This portfolio demonstrates my ability to use Python programming language for data cleaning, analysis, visualization, modeling, and interactive reporting, leveraging a wide range of libraries and tools to produce actionable insights and reproducible workflows.