Courses

• Data collection approaches at an awareness level: transactional systems, forms, surveys, observations, and external data sources.
• Common data quality issues: missing values, inconsistencies, duplicates, obvious outliers.
• Cleaning and preparing data in spreadsheets or introductory tools: basic sorting, filtering, simple data validation, and structured tables.
• Creating and managing simple data dictionaries or field descriptions at a vocational level.
• Importance of good documentation and repeatable processes in data preparation.

• Descriptive statistics at an applied level: counts, sums, minimum/maximum, averages, and simple measures of spread.
• Using spreadsheets or equivalent tools to generate summary statistics for numerical and categorical data.
• Introduction to cross-tabulations and simple comparisons (e.g. comparing groups or time periods, conceptually).
• Introductory understanding of correlation at a conceptual level (association awareness; no advanced inference).
• Using exploratory analysis to develop questions and hypotheses for further investigation.

• Principles of effective data visualisation: clarity, simplicity, appropriate chart choices.
• Common chart types at an introductory level: bar charts, line charts, pie charts (used carefully), column charts, and simple scatter plots.
• Designing charts that highlight key messages without distortion or clutter.
• Combining visuals and text to produce short, meaningful reports or dashboards (at a basic level).
• Avoiding misleading presentations and respecting data integrity in communication.

Overview of the data science lifecycle at a conceptual level:

• define question, gather data, prepare, analyse, interpret, and communicate.
• Conceptual introduction to predictive ideas (e.g. using past data to understand likely future patterns – non-technical).
• Awareness of basic model ideas (e.g. linear relationship awareness, classification vs grouping – purely conceptual, without advanced mathematics).

Examples of applied analytics in practice:

• Customer analysis (e.g. which products are popular).
• Operational analysis (e.g. workload or demand patterns).
• Performance monitoring (e.g. simple KPIs over time).
• Understanding limits of simple analysis and the importance of not over-interpreting patterns.

• Ethical considerations in data use: respect for individuals, fairness, transparency, and avoiding misuse of data.
• Awareness of privacy and confidentiality: handling personal data with care and following organisational policies.
• The importance of data governance at a conceptual level: clear roles, standards, and procedures for data management.
• Recognising biases and limitations in data and how they might affect conclusions.
• Professional practice: collaboration with technical and non-technical colleagues, documentation, and responsible communication of results.