Here you can find all the 8 projects of WorldQuant's Data Science Program along with my certification.
I'm exhilarated to share that I have successfully completed WorldQuant's Data Science Program, a transformative journey that has broadened my skills and knowledge in the field of data science! π β¨
Check my badge: https://www.credly.com/badges/4480df1c-d561-4ef8-9852-79b5748e0c73/public_url
Throughout the program, I had the opportunity to work on eight fascinating projects, each designed to enhance my understanding and practical application of key data science concepts. Let me provide a brief explanation of each project:
1- ππ’π¨π¦ππ‘π ππ‘ π ππ«πππ’: 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- ππ£ππ₯π§π ππ‘π§ π¦ππππ¦ ππ‘ ππ¨ππ‘π’π¦ πππ₯ππ¦: 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- πππ₯ π€π¨ππππ§π¬ ππ‘ π‘πππ₯π’ππ: 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- πππ₯π§ππ€π¨πππ πππ πππ ππ‘ π‘ππ£ππ: 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- πππ‘ππ₯π¨π£π§ππ¬ ππ‘ π£π’πππ‘π: 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- ππ¨π¦π§π’π ππ₯ π¦πππ ππ‘π§ππ§ππ’π‘ ππ‘ π§ππ π¨π¦: 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- π/π π§ππ¦π§ππ‘π ππ§ πͺπ’π₯πππ€π¨ππ‘π§ π¨π‘ππ©ππ₯π¦ππ§π¬: 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- π©π’πππ§ππππ§π¬ ππ’π₯ππππ¦π§ππ‘π ππ‘ ππ‘πππ: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.
I want to express my heartfelt gratitude to WorldQuant for providing an exceptional learning experience. The program's comprehensive curriculum and hands-on projects have equipped me with practical skills and a deep understanding of data science techniques.