HOUSING IN MEXICO

For each assessment, students must score 90% Learners use a dataset of 21,000 properties to determine if real estate prices are influenced more by property size or location. They import and clean data from a CSV file, build data visualizations, and examine the relationship between two...

APARTMENT SALES IN BUENOS AIRES

For each assessment, students must score 90%. Learners build a linear regression model to predict apartment prices in Argentina. They create a data pipeline to impute missing values and encode categorical features, and they improve model performance by reducing overfitting. -Loaded multiple...

AIR QUALITY IN NAIROBI

For each assessment, students must score 90%. Used a dataset from one of Africa's largest open data platforms, openAfrica. Learners build an ARMA time-series model to predict particulate matter levels in Kenya. They extract data from a MongoDB database using pymongo, and improve model...

EARTHQUAKE DAMAGE IN NEPAL

For each assessment, students must score 90%. Learners build logistic regression and decision tree models to predict earthquake damage to buildings. They extract data from a SQLite database, and reveal the biases in data that can lead to discrimination. -Queried an SQL database. -Performed a...

BANKRUPTCY IN POLAND

For each assessment, students must score 90%. Learners work with financial data from the Emerging Markets Information Service to look at financial indicators from Poland and Taiwan and build models to predict whether a corporation will go bankrupt.

CUSTOMER SEGMENTATION IN THE US

For each assessment, students must score 90%. Learners work with consumer finance data from the US Federal Reserve to identify households that have a hard time getting credit. There after, they build an unsupervised model to segment households and finally create an interactive dashboard.

A/B TESTING AT WORLDQUANT UNIVERSITY

For each assessment, students must score 90%. Learners design and conduct an A/B test to improve quiz completion for enrollment at WQU. They build custom Python classes to implement an ETL process, and they create an interactive data application following a three-tiered design pattern.

VOLATILITY FORECASTING IN INDIA

For each assessment, students must score 90%. Learners create a GARCH time series model to predict asset volatility. They acquire stock data through an API, clean and store it in a SQLite database, and build their own API to serve model predictions.