Advanced Applied Data Science
This 3-day Advanced Applied Data Science (AADS) course covers key concepts and techniques needed to develop, deploy, and maintain effective machine learning models. The course includes topics such as feature engineering, outlier detection, advanced feature scaling, hyperparameter tuning, model deployment pipeline, detecting model drift, and ends with a hands-on project. Participants will gain practical skills and theoretical knowledge on how to identify and handle outliers, scale features, tune hyperparameters, deploy models in production, and monitor models for drift. By the end of the course, learners will have a comprehensive understanding of advanced data science concepts and techniques, and will be well-prepared to take on more advanced data science projects.
Upon completion of this advanced data science short course, participants will have gained practical skills and theoretical knowledge on feature engineering, outlier detection, advanced feature scaling, hyperparameter tuning, model deployment pipeline, and detecting model drift. They will also be able to apply these skills to real-world scenarios through the hands-on project. Additionally, participants will have a comprehensive understanding of advanced data science concepts and techniques, which will enable them to develop, deploy, and maintain effective machine learning models.
This course is suitable for individuals equipped with basic Data Science understanding, knowledge of different types of Machine Learning (ML) models, and is capable of running some ML models using Python Programming. Ideally, potential participants would have previously taken our Applied Data Science (ADS) Course.
- Understanding Feature Engineering Concepts
- Outliers and How to Find Them
- Advanced Scaling
- Model Validation
- Hyperparameter Tuning
- Model Deployment
- Model Monitoring
- End-to-End Project 1 and Project 2 to Implement your knowledge learnt from ADS and AADS