Data Science II

Time Series Analysis & Forecasting

Introducing the principles and techniques behind time series analysis, equipping students to predict future trends in time-dependent datasets.

Advanced Regression Techniques

Exploring sophisticated regression models such as random forests, preparing students to predict both categorical and numerical data outcomes.

Unsupervised Learning

Understanding the power of clustering, dimension analysis and reduction techniques for certain business problems and improving predictive model performance.

Deep Learning

An introduction into neural networks for supervised and unsupervised applications, and an overview of different architectures, their applications and their limitations.

Feature Engineering

It’s not all about model tuning – experimenting with intuition-driven feature engineering techniques to maximize model performance.

Ethical Implications

Cultivating a sense of responsibility in students to assess and understand potential negative ramifications when implementing model predictions in real-world scenarios.