About Course

This specialised micro-credential equips learners with essential industry-relevant skills in forecasting, risk assessment, and AI-driven decision-making.

Learners will apply statistical and machine learning models to analyse time series data, identify trends, and optimise decision-making strategies. They will develop expertise in volatility modeling, correlation analysis, and risk management, addressing real-world challenges.

By leveraging advanced architectures to enhance predictive accuracy, feature extraction, and synthetic data generation, learners will gain competencies in automation, predictive analytics, and intelligent decision-making. These skills will drive innovation and improve efficiency, accuracy, and risk management across industries such as healthcare, finance, security, and AI-driven services.

Integrating online and in-person delivery, the micro-credential combines hands-on learning activities, where you will design and implement DNN, CNN, RNN, Transformer, GAN, and Autoencoder models for classification, sequential data analysis, and data manipulation. You will complete an assignment on time series modeling, participate in a case study on risk management, develop a forecasting project using RNN, LSTM, and Transformer architectures, and present a trading strategy simulation based on time series analysis.

This micro-credential is part of the CSM Pathway in Applied Computing.