Data Scientist
Current1. HOUSING IN MEXICO: Use a dataset of 21,000 properties to determine if real estate prices are influenced more by property size or location. Import and clean data from a CSV file, build data visualizations, and examine the relationship between two variables using correlation.2. APARTMENT SALES IN BUENOS AIRES: Build a linear regression model to predict apartment prices in Argentina. Create a data pipeline to impute missing values and encode categorical features, and improve model performance by reducing overfitting.3. AIR QUALITY IN NAIROBI: Build an ARMA time-series model to predict particulate matter levels in Kenya. Extract data from a MongoDB database using pymongo, and improve model performance through hyperparameter tuning.EARTHQUAKE DAMAGE IN NEPAL: Build logistic regression and decision tree models to predict earthquake damage to buildings. Extract data from a SQLite database, and reveal the biases in data that can lead to discrimination.4. BANKRUPTCY IN POLAND: Build random forest and gradient boosting models to predict whether a company will go bankrupt. Navigate the Linux command line, address imbalanced data through resampling, and consider the impact of performance metrics precision and recall.6. CUSTOMER SEGMENTATION IN THE US: Build a k-means model to cluster US consumers into groups. Then use principal component analysis (PCA) for data visualization, and create an interactive dashboard with Plotly Dash.7. A/B TESTING AT WORLDQUANT UNIVERSITY: Conduct a chi-square test to determine if sending an email can increase program enrollment at WQU. Then build custom Python classes to implement an ETL process, and create an interactive data application following a three-tiered design pattern.8. VOLATILITY FORECASTING IN INDIA: Create a GARCH time series model to predict asset volatility. Then acquire stock data through an API, clean and 9. store it in a SQLite database, and build an API to serve model predictions.
