x̄ - > My Data Science Journey: Tackling Real-World Problems with WorldQuant University

As a data science enthusiast, I recently completed three impactful projects as part of the WorldQuant University Applied Data Science Lab, each tackling real-world challenges with data-driven solutions. From analyzing housing markets in Mexico and Buenos Aires to predicting air quality in Nairobi, these projects pushed me to hone my skills in data analysis, modeling, and critical thinking. I’m also gearing up for a fourth project on earthquake damage in Nepal, which promises to be just as exciting.

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1. Decoding Housing Prices in Mexico

In this project, I dove into a dataset of 21,000 properties in Mexico to answer a key question: What drives real estate prices—property size or location? I started by importing and cleaning the data from a CSV file, handling missing values and outliers. Using Python libraries like Pandas and Matplotlib, I created visualizations to explore patterns, such as scatter plots showing price trends across regions. By calculating correlations, I uncovered how location often trumped size in influencing prices, especially in urban hotspots.

2. Predicting Apartment Prices in Buenos Aires

Next, I built a linear regression model to predict apartment prices in Buenos Aires, Argentina. This project was all about creating a robust data pipeline. I dealt with missing values, encoded categorical features like neighborhood types, and worked to reduce overfitting by fine-tuning the model. The result? A model that could reasonably predict prices based on features like square footage and amenities.

3. Forecasting Air Quality in Nairobi

For my third project, I tackled air quality in Nairobi, Kenya, using an ARMA time-series model to predict particulate matter levels. I extracted data from a MongoDB database using the pymongo library, sourced from openAfrica. After performing exploratory data analysis to spot trends in air pollution, I tuned the ARMA model’s hyperparameters to improve accuracy.

4. On the Horizon: Earthquake Damage in Nepal

I’m currently preparing for my fourth project, which focuses on predicting earthquake damage to buildings in Nepal using logistic regression and decision tree models. This involves pulling data from a SQLite database and analyzing potential biases that could skew predictions, such as uneven representation of building types.

Why These Projects Matter

Each project challenged me to think critically about data, from cleaning and preprocessing to building and evaluating models. They also highlighted the power of data science to address global issues like housing affordability, environmental health, and disaster preparedness. Working through real-world datasets gave me hands-on experience with tools like Python, SQL, and MongoDB while keeping ethical considerations like model bias top of mind.