1. Course Overview
This 4-day physical programme is a condensed, instructor-led course inspired by the Google Data Analytics Certificate. It is designed for learners who need a practical and business-oriented introduction to data analytics within a short classroom format. The course covers analytics foundations, structured problem framing, data preparation, cleaning, basic analysis, data visualisation, and insight presentation. It also introduces how AI can support analytics-related work such as data structuring, prompt-assisted exploration, formula support, summary generation, and responsible decision support.
Positioning note: This course is aligned to the themes and tools highlighted in the Google programme, but it is a condensed corporate bootcamp and not a substitute for the full online certificate journey.
- 2. Programme Alignment Summary
Official Google programme signals
How this 4-day course responds
Beginner level; no degree or prior experience required.
Keep explanations practical, guided, and accessible for new learners.
Tools highlighted include spreadsheets, SQL, Python, Tableau, and AI-related skills.
Structure the bootcamp around those tools and convert them into classroom-friendly activities.
Programme emphasises asking questions, cleaning data, analysing data, and visualising findings.
Organise Days 1 to 3 around the analytics workflow from problem framing to visual communication.
Google materials reference AI use in data cleaning, data structuring, and visualisation support.
Use Day 4 to add hands-on AI application, prompting, ethics, and workplace adoption.
The online programme spans a much longer self-paced journey.
Position this version as a fast-track bootcamp for awareness, practice, and workplace application.
3. Target Audience
Beginners who want a structured and business-relevant introduction to data analytics.
Working professionals in operations, finance, HR, sales, marketing, administration, and management who use data for reporting or decision-making.
Supervisors, executives, and team leads who need a practical understanding of analytics workflows and insight communication.
Departments involved in digital transformation, performance reporting, or process improvement initiatives.
Learners who want exposure to AI-assisted analytics workflows in addition to core data skills.
4. Course Objectives
Explain the role of a data analyst and the major stages of the analytics process.
Translate business issues into clear analytics questions using structured thinking.
Prepare, organise, and clean data using spreadsheet and SQL concepts.
Perform basic analysis and interpret results for business use.
Create simple and meaningful data visualisations using dashboard and storytelling principles.
Recognise where Python, Tableau, and AI tools fit into modern analytics workflows.
Apply AI tools productively and responsibly for selected analytics support tasks.
Communicate findings clearly through summaries, charts, and business-focused recommendations.
5. Proposed 4-Day Course Outline
Recommended delivery mode: Face-to-face classroom training with demonstrations, guided exercises, case-based discussion, and small group activities.
Day 1 - Foundations of Data Analytics
Focus: Build the analytics mindset, understand the role of the data analyst, and learn how business problems are translated into data questions.
Module
Key content and activities
Expected outcome
Introduction to Data Analytics
What data analytics is, why it matters, and how organisations use data to improve decisions and performance.
Learners understand the business value of analytics.
The Role of the Data Analyst
Typical responsibilities, workflows, stakeholders, and deliverables for junior or associate analyst roles.
Learners recognise the analyst role and its practical scope.
Data Types and Business Context
Structured and unstructured data, qualitative and quantitative data, and the relationship between data and business questions.
Learners connect data categories to business use.
Asking Questions That Matter
SMART questioning, stakeholder needs, problem framing, and converting issues into measurable questions.
Learners can frame relevant analytics questions.
Structured Thinking Workshop
Guided team exercise: turn one business problem into a simple analytics brief.
Learners practise business-to-analysis translation.
Day outcome: Participants can describe the analytics lifecycle, identify relevant questions, and connect business needs to analysis work.
Day 2 - Data Preparation, Cleaning, and Exploration
Focus: Build practical confidence in preparing usable data with spreadsheet methods and SQL concepts.
Module
Key content and activities
Expected outcome
Working with Spreadsheets
Sorting, filtering, validation, basic formulas, tabular thinking, and clean layout practices for analysis-ready data.
Learners can organise data in a usable format.
Preparing Data for Exploration
Checking completeness, identifying duplicates, inconsistent values, missing fields, and formatting issues.
Learners know what to inspect before analysis.
Process Data from Dirty to Clean
Data quality checks, cleaning logic, integrity principles, and practical examples of poor data structure.
Learners can identify and correct common data issues.
Introduction to SQL for Cleaning
Tables, rows, columns, simple queries, filtering, sorting, and basic SQL concepts for data preparation.
Learners understand how SQL supports preparation work.
Hands-on Lab
Clean and standardise a sample dataset using spreadsheet techniques and guided SQL examples.
Learners apply preparation and cleaning steps in practice.
Day outcome: Participants can prepare, organise, and clean data for basic exploration and reporting tasks.
Day 3 - Analysis, Visualisation, and Mini Case Study
Focus: Turn prepared data into findings, charts, and business communication.
Module
Key content and activities
Expected outcome
Analyse Data to Answer Questions
Basic aggregation, comparisons, trend reading, interpretation, and making sense of evidence from prepared datasets.
Learners move from data preparation into insight generation.
Spreadsheet and SQL Analysis Practice
Use spreadsheet calculations and simple SQL logic to answer practical questions from a dataset.
Learners practise structured analytical thinking.
Introduction to Python and Tableau
High-level overview of how Python supports analysis and how Tableau supports visual exploration and dashboards.
Learners understand where these tools fit in the workflow.
Visualisation Principles
Choosing suitable charts, avoiding misleading visuals, presenting evidence clearly, and telling a concise data story.
Learners can select clearer presentation methods.
Mini Case Study and Presentation
Small-group exercise: prepare one short analysis summary with visual support and recommendations.
Learners practise communicating findings in a business-friendly format.
Day outcome: Participants can interpret results, build simple visual stories, and present findings with more structure and clarity.
Day 4 - AI in Action for Data Analytics
Focus: Use AI tools productively and responsibly to support analytics work, communication, and workplace efficiency.
Module
Key content and activities
Expected outcome
AI Fundamentals for Analysts
Understand practical AI concepts, what generative AI can and cannot do, and where AI fits into analytics support work.
Learners develop realistic expectations of AI support.
Prompting for Data Tasks
Prompt design for summarising data, generating formula ideas, structuring questions, drafting explanations, and refining outputs.
Learners can write more effective prompts.
AI for Daily Analytics Work
Use cases for data organisation, documentation support, memo drafting, insight summaries, and productivity enhancement.
Learners see immediate workplace applications.
Responsible and Ethical AI
Verification of outputs, privacy boundaries, sensitive data handling, over-reliance risks, and appropriate workplace usage.
Learners understand safer and more responsible AI practice.
Reflection and Department Action Plan
Each participant identifies one realistic AI-supported task they can apply in their role after training.
Learners leave with an action-oriented application plan.
Day outcome: Participants can apply AI tools to selected analytics-support tasks while recognising privacy, verification, and ethical boundaries.
Programme scope, delivery arrangements and suitability are confirmed during enquiry.