VCE Applied Computing Data Analytics study guide
VCE Applied Computing Data Analytics · Work through your exam, one topic at a time.
VCE Applied Computing: Data Analytics is a Victorian senior secondary study (Units 3 and 4) assessed through school-based coursework and an end-of-year written examination set by the Victorian Curriculum and Assessment Authority. It suits students who want to work with real datasets: gathering and validating data, cleaning and transforming it, building visualisations, and drawing defensible conclusions that inform decisions. The VCE Applied Computing Data Analytics study guide has 7 sections and about 2,388 words.
01Data Analytics, Data Management and the Data Life Cycle
Data analytics is the process of turning raw data into information that supports decisions. Distinguish data (raw recorded values), information (data given context and meaning) and knowledge (patterns understood well enough to act on).
384 words, about 2 min02Data Collection, Sources and Acquisition Methods
Primary data is collected first-hand for the specific purpose at hand - surveys, interviews, observations, sensors, experiments and system logs generated by the organisation. Secondary data already exists and was collected for another purpose - government open data, published reports, census statistics, social media archives and third-party datasets.
361 words, about 2 min03Data Preparation, Cleaning, Wrangling and Transformation
Preparation is usually the largest part of any analytics project. Cleaning addresses quality problems: removing duplicates, filling or flagging missing values, correcting inconsistent formats (dates, capitalisation, units), removing or investigating outliers, and fixing spelling variants so that "Vic", "Victoria" and "VIC" are treated as one category.
332 words, about 2 min04Data Visualisation, Presentation and Communication
The chart must match the data and the message. Bar charts compare categories; column charts do the same with vertical bars.
346 words, about 2 min05Analysis and Interpretation to Support Decision-Making
Analysis moves through levels: descriptive (what happened - totals, averages, trends), diagnostic (why - comparisons and correlations), predictive (what is likely - forecasting and modelling) and prescriptive (what should be done). Candidates should know measures of central tendency (mean, median, mode) and spread (range, standard deviation, interquartile range).
294 words, about 1 min06Legal, Ethical, Security and Privacy Issues
In Australia, the Privacy Act 1988 and the Australian Privacy Principles govern how organisations collect, use, store, disclose and destroy personal information. Key principles include collecting only what is necessary, informing people why data is collected, using it only for the stated purpose, keeping it secure, and allowing access and correction.
316 words, about 2 min07Project Management and Collaborative Development
A data analytics project needs a clear scope , goals , deliverables , timeline , resources and stakeholders . Candidates should understand the phases of a project life cycle - initiation and planning, data acquisition, preparation, analysis, visualisation and communication, and review - and that these often overlap or iterate.
355 words, about 2 min