Introduces data science, its applications and the processes used to turn raw data into useful information and insights.
Teaches learners how data is generated, collected and obtained from different sources.
Introduces the organisation, storage and management of datasets for analysis.
Develops practical spreadsheet skills for preparing, analysing and presenting data.
Introduces statistical concepts needed to understand and interpret datasets.
Introduces probability concepts and their applications in data analysis and decision-making.
Teaches learners how to identify and correct problems in datasets before analysis.
Introduces SQL for retrieving, filtering and analysing data stored in relational databases
Introduces Python programming and its use in practical data analysis
Introduces commonly used Python libraries for manipulating and analysing datasets.
Teaches learners how to investigate datasets and identify trends, relationships and patterns.
Develops skills for presenting data through clear and meaningful charts and visual representations.
Teaches learners how to organise analytical results into dashboards and professional reports.
Introduces techniques for analysing relationships between variables and making basic predictions.
Provides an introduction to machine learning and explains how computers can learn patterns from data.
Introduces the basic process of preparing, training and evaluating machine learning models.
Develops awareness of responsible data use, privacy, security, fairness and ethical decision-making.
Learners apply the knowledge gained throughout the course to analyse a real-world dataset and communicate meaningful findings.