I’m writing this README to give whoever is reading it a little bit of backstory.

I recently completed my Master's in Agricultural Engineering (Agroecology), a field focused on building more sustainable agricultural systems. It might not seem like the obvious path into data analytics, but that's exactly where my journey started.

During my master's, I took a Crop Modeling course where I was introduced to programming, simulations, and data-driven decision-making. Later, my thesis involved working with millions of data points, six global climate models, and over 9,600 simulations to study how climate change could affect crop yields across Iran.

Somewhere along the way, I realized something unexpected: I genuinely enjoyed writing code more than writing papers.

What started as a tool for research quickly became something I wanted to take much further.

I went deeper into Python and machine learning, started building my own projects, and gradually expanded into SQL, business intelligence, forecasting, statistics, and data visualization. That eventually led me to the Hamrah Avval Data Analysis Bootcamp, where I worked on projects across all of these areas and my final project, Dideban, was selected as the 2nd Best Final Project in the bootcamp.

What I like most about data is that the stories behind the numbers aren't limited to one field. Mine started with agriculture, but since then I've worked with data from energy, finance, healthcare, automotive markets, customer behavior, sports, and more.

My GitHub is a collection of projects covering different parts of the analytics workflow, from data preparation and statistical analysis to machine learning, forecasting, SQL, business intelligence, and data visualization.

• Power BI, DAX & Power Query
• Data Modeling & KPI Development
• Tableau
• Grafana
• Interactive Dashboard Design

• Complex Joins & CTEs
• Window Functions
• Subqueries
• Analytical Reporting
• Query Optimization

• Regression, Classification & Clustering
• Feature Engineering
• Model Selection & Evaluation
• Hyperparameter Tuning
• Time Series Analysis & Forecasting
• Scikit-learn & Statsmodels

• Matplotlib & Seaborn
• Tableau
• Business Storytelling
• Analytical Reporting

2nd Best Final Project, Hamrah Avval Data Analysis Bootcamp

An end-to-end analytics project on 15 stocks across three industries in the Iranian stock market, covering momentum, ownership flow, risk, correlation, diversification, and sector behavior. Built from data collection and validation through feature engineering, statistical analysis, quality checks, documentation, and a six-page Power BI dashboard.

A large-scale machine learning project for predicting building energy consumption, with extensive data cleaning, weather-data alignment, feature engineering, model training, tuning, and evaluation.

Built end-to-end Power BI projects across mortgage and credit risk, the global used car market, and liver disease analytics, working with Power Query, data modeling, DAX, KPI design, drillthrough, custom tooltips, and interactive dashboards.

Built an interactive Tableau dashboard using Recency, Frequency, and Monetary analysis to segment customers by purchasing behavior and explore customer value, sales, profit, and retention opportunities.

Built analytical SQL projects using joins, CTEs, window functions, subqueries, aggregations, and multi-step queries to answer analytical and business questions.

Built and compared time-series forecasting models to predict household electricity consumption, including preprocessing, model evaluation, and forecasting with Prophet and SARIMA.

Programming & Analytics: Python, SQL, R, Advanced Excel

Python: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Statsmodels

Business Intelligence: Power BI, DAX, Power Query, Tableau, Grafana

Databases & Development: SQL Server, Git, GitHub

One of the things I enjoy most about data analytics is that every project starts with a different question. Sometimes it's predicting future energy consumption, sometimes it's building a dashboard for decision-makers, and sometimes it's uncovering patterns hidden inside a dataset with SQL.

That's what keeps this field exciting for me. The domain may change, but there is always a new problem to solve and another story hidden in the data.

📧 Email: torkanparvin@gmail.com
💼 LinkedIn: linkedin.com/in/torkan-parvin

Thanks for stopping by! Feel free to explore my repositories, and if you have any questions or would like to connect, I'd be happy to hear from you.