Data Science 1

This micro-credential introduces Data Science methods using Python and Jupyter Notebooks. The focus is on the typical data science workflow involving reading data in different formats, performing some analysis and communicating the results. Notebooks are used to document this workflow in a reproducible manner and develop a story around the analysis performed. Learning Outcomes • Develop superior skills using spreadsheets to summarise, model and visualise data to make decisions. • Understand how data can be interpreted in different ways in line with an agenda and ask appropriate questions Key Topics • Introducing Jupyter Notebooks and Python • Data Formats and Data Structures • Descriptive Statistics • Causality and Correlation; Visualisation of Data • Predictive Models; Reproducibility • Clustering Activities • Self-directed learning • Case studies, group work and in class exercises • A notebook completed each week demonstrating mastery of the techniques and methods introduced - assessed • A critical analysis of a notebook analysis provided to the student to give feedback as a document and an updated version of the Python notebook file - assessed • A collection of three data analysis tasks collected into a portfolio reflecting the knowledge gained over the course of the unit - assessed • Written reflections on the application of the knowledge gained in the unit to the learner’s role in the organisation - assessed Volume of Learning 75 hours Credit Points 5 AQF Level 8