Applied Data Science Lab

Issued by WorldQuant University

Earners of this badge have completed eight end-to-end, applied data science projects. In each project, they accessed data from files, SQL and NoSQL databases and APIs. They have demonstrated their ability to explore and clean data, create functions and ETL pipelines to prepare training sets. They have built machine learning models for supervised and unsupervised learning tasks, and have created visualizations to explain data characteristics and model predictions for non-technical audiences.

Type Learning
Time Months
Cost Free

Additional Details

Skills

Earning Criteria

Learners completed eight projects. Each project consists of four self-paced lessons, followed by an assignment that is programmatically graded. For each assessment, students must score 90% or better.
1. HOUSING IN MEXICO: 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 variables using correlation.
2. APARTMENT SALES IN BUENOS AIRES: 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.
3. AIR QUALITY IN NAIROBI: 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 performance through hyperparameter tuning.
4. EARTHQUAKE DAMAGE IN NEPAL: 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.
5. BANKRUPTCY IN POLAND: Learners build random forest and gradient boosting models to predict whether a company will go bankrupt. They 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: Learners build a k-means model to cluster US consumers into groups. They use principal component analysis (PCA) for data visualization, and they create an interactive dashboard with Plotly Dash.
7. A/B TESTING AT WORLDQUANT UNIVERSITY: Learners conduct a chi-square test to determine if sending an email can increase program 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.
8. VOLATILITY FORECASTING IN INDIA: 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.