Data analytics and insights
Turning business data into decisions: data preparation, descriptive, diagnostic, predictive and prescriptive analytics, and data storytelling.
Turning business data into decisions, built around the four analytics types and the data story that carry the marks. Everything you need, nothing you don't.
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Independent study notes for accounting certification candidates. Not affiliated with any professional body.
What this subject is really about
This elective treats data as a business problem first and a technical one second. You read a business question, judge whether the data can answer it, choose an analytical technique, then communicate the result so someone acts on it. The syllabus now includes artificial intelligence in analytics, so it reads as current rather than dated.
It suits candidates who want analytics and business intelligence in their toolkit, or who work with finance data and are tired of handing it to someone else to interpret. The marks reward the insight and the recommendation as much as the analysis, so the notes focus on choosing the right technique and telling the story, not one vendor's buttons.
The eight chapters, and what each covers
The subject runs from preparing data, through the four analytics types, to communicating what you found. Here is the whole scope before you enrol.
Data analytics and business problems
Reading a business question as a data question.
- The analytics process
- Artificial intelligence in analytics
- Data literacy
- Data types in a finance context
Data preparation
Getting data fit to analyse.
- Collection and privacy
- Cleansing and wrangling
- Transformation
- Storage and management
Descriptive analytics
What happened.
- Data aggregation and visualisation
- Descriptive statistics
Diagnostic analytics
Why it happened.
- Outliers and anomaly detection
- Measuring relationships with correlation
Predictive analytics
What is likely to happen next.
- Cross-sectional data
- Time-series data
- Machine learning and Bayesian methods
Prescriptive analytics
What to do about it.
- What prescriptive analytics is
- Applying it to a decision
Data visualisation
Making the finding readable at a glance.
- Setting the scene of your data story
- The seven essential principles
Communicating with influence
Turning analysis into a decision.
- Understanding data storytelling
- The psychology and anatomy of a story
- Narrative structure
The techniques you'll use
The subject teaches concepts and judgement rather than one product, so the techniques transfer to whatever tool your workplace uses.
Descriptive (what happened), diagnostic (why), predictive (what next) and prescriptive (what to do). The spine of the subject.
Literacy, cleansing, wrangling, transformation and storage, the unglamorous work most of an analysis actually is.
Descriptive statistics, correlation, anomaly detection, time-series and the idea behind machine learning and Bayesian methods.
The modern tools the concepts map onto, and the principles that make a chart readable.
The seven principles and the narrative structure that turn a result into a decision.
Where AI fits in the process, and where judgement still has to sit with you.
How it's assessed
This subject has no invigilated exam. It is assessed by one submission in two parts, a written report with your analysis files and a recorded presentation, submitted together in the final week.
Written submission
Review case study data on a business problem, then submit a written report with your supporting analysis files: findings, actionable insights and recommendations.
Recorded presentation
A video communicating your insights on the business problem and how it should be addressed.
Insight led
The marks are in the insight and the recommendation, so the pack is worked cases, not a timed paper.
The traps worth knowing before the assessment
These are the mistakes markers see most often in analytics answers. The insight, not the chart, is what earns the marks.
Reaching for a technique before you have framed the business question. The question decides the analytics type, not the other way around.
Skipping data preparation. Analysis built on unclean or ill-suited data fails quietly, so judging and preparing the data is part of the marks.
Confusing correlation with causation. A relationship in the data is not a cause, and a diagnostic answer that treats it as one loses ground.
Stopping at descriptive. Saying what happened is the start; the marks build through why, what next and what to do.
A chart that needs explaining. If a visual cannot be read at a glance, it is not doing its job in the story.
Presenting analysis instead of a recommendation. The assessment wants an actionable insight, not a tour of your working.
The kind of question you'll face
A short scenario of the kind the subject uses, so you can see how the four analytics types build on one another.
Currawong Retail Ltd has seen product returns climb across two quarters and hands you the sales, returns and customer data. The board wants to know what is happening and what to do about it. How do you work the problem?
Walking the four analytics types in order: describe the rise, diagnose the cause against segment and product, predict the cost if nothing changes, then prescribe the action, and tell it as one clear story.
The worked case runs the four types on the data and lands on a recommendation, with the reasoning laid out so you can see where the marks sit.
How to study it around a full-time job
The subject runs over a 7-week study period, six teaching weeks then the assessment. Here is a plan that fits it around work.
Problems and data
Framing a business question and preparing data. Most of a real analysis is preparation, so do not rush it.
Describe and diagnose
Descriptive and diagnostic analytics. Practise reading what happened and testing why, without jumping to a cause.
Predict, prescribe, tell
Predictive and prescriptive analytics, then visualisation and storytelling. Tie every finding to an action.
Choose from the question
Take real questions and choose the analytics type from each, before touching any data.
Analyse and present
Work the case, write the report with your analysis files, then rehearse a three-minute insight story.
See what's in the pack
A real example of each part of the pack, not stock previews. This is the product doing the talking.
Plain English, assessment ready
The whole subject rewritten to be read fast. Here is the ladder the subject runs on, as it appears in the notes.
The four analytics types
Descriptive analytics says what happened. Diagnostic analytics explains why. Predictive analytics estimates what is likely to happen next. Prescriptive analytics recommends what to do about it. Any business question can be placed on this ladder, and the marks build as you climb it.
The analytics ladder on one page
- Descriptive: what happened -> aggregate, chart, state it
- Diagnostic: why -> correlation, outliers, segments
- Predictive: what next -> time-series, cross-sectional, models
- Prescriptive: what to do -> the recommendation and its effect
- Correlation is not causation
Cases marked like the real thing
Scenario-led cases that mirror the written submission, each with a model response to learn from.
Common questions
Do I need to know how to code?
No. The subject teaches the concepts and judgement behind the techniques, not one programming language. You should understand what each method does and when to use it.
Do I need a specific tool like Power BI or Tableau?
No. The subject teaches modern business intelligence concepts rather than one vendor's product, and our notes do the same. The judgement about which technique answers which question transfers to whatever tool you use.
Is there an exam?
No. It is assessed by a single submission with a written report, your analysis files and a recorded presentation, submitted together in the final week.
What do I need before I start?
Ethics and Business, or Ethics and Sustainability, is the prerequisite. The subject assumes some understanding of finance, management accounting, statistics and information technology.
Does it cover artificial intelligence?
Yes. Artificial intelligence in data analytics is part of the first chapter, so the content reflects how the work is actually changing.
Are these notes affiliated with the program?
No. Summo Notes is an independent study resource and is not affiliated with or endorsed by any professional body.
What is in the pack?
One full subject pack: condensed notes across the eight chapters, A quick reference to the four analytics types and the storytelling principles, worked analytics cases with model responses and structure guidance for the report and presentation.
What does it cost?
$95 AUD when it launches, as a single one-off payment for the whole subject. There is no subscription and no per-chapter pricing.
Everything in one pack
One pack per subject. Notes, a quick reference and worked cases, together.
- +Condensed notes across the eight chapters
- +A quick reference to the four analytics types and the storytelling principles
- +Worked analytics cases with model responses
- +Structure guidance for the report and presentation
One-off payment when it launches. Instant download.
The pack for this subject is in production. Join the list and we will email you the day it launches.
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Summo Notes is an independent study resource and is not affiliated with or endorsed by any professional body.