Data Science II: Predictive Modelling

The micro-credential introduces the fundamental machine learning techniques and tools for data science including predictive models, evaluation methodologies and classification. It provides practical experience applying these methods using industry-standard software tools to real-world data sets. Learning Outcomes • Identify the appropriate Data Science analysis for a problem and apply that method to the problem. • Communicate the results of a Data Science analysis. • Discuss the broader implications of Data Science analysis. Key Topics • Overview of machine learning • KNN Model • Naïve Bayes model • Text as data • Decision Tree Models • Artificial Neural Networks Activities • Self-directed learning • Case studies, group work and in class exercises • 6 quizzes - 1 per topic • A major work that consolidates all learning. Three submissions over the duration of the micro credential • Written reflection on the application of the knowledge gained in the micro credential to the learner’s role in the organisation - assessed Volume of Learning 75 hours Credit Points 5 AQF Level 7