AI · AIC-108

AI for Data Analytics with Google Workspace

AI for Data Analytics with Google Workspace is a hands-on course in using Gemini in Google Sheets to prepare and analyse finance and operations data, Gemini Notebook to extract contract terms, and Data Studio to build dashboards. It suits finance, accounting, IT and data teams, and you leave with an executive dashboard and a one-page insight brief.

Updated 10 Nov 2026

From 5,310 THB / person 5,900 −10% excl. VAT 7% · group rates available

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PDFDownload the course outline

Duration12 hours · 2 days
FormatOnsite / live online
Next roundOn request
CertificateIncluded

Course overview

Every organisation produces data every day from many sources: revenue, invoices, receivables, budgets, utility costs for each site, service tickets and contracts stored as PDFs. Finance teams have to close the books and chase receivables, while IT and data teams have to keep the data usable, yet the data is usually spread across many files and systems. Each round of management reporting takes a long time and is hard to check, and although AI can help a great deal, it can also produce wrong numbers when used carelessly.

This course takes learners through the whole analytics workflow with a single set of tools in Google Workspace. It starts with Gemini in Google Sheets, using the Ask Gemini panel, the AI function and Fill with Gemini to prepare, clean and analyse data. Learners then use Gemini Notebook (formerly NotebookLM) to read contracts and extract their key terms back into a table, and build an executive dashboard in Data Studio (formerly Looker Studio), checking every AI result against pivot tables, formulas and citations along the way. Everything is done on simulated data from a fictional multi-site company, and the course ends with a dashboard and a one-page insight brief. (2 days, 6 hours per day, 12 hours in total, Beginner to Intermediate level.)

What you’ll gain

Explain the data analysis lifecycle and choose the right use of AI for each step
Frame business questions, define finance and operations KPIs and draft a data dictionary
Use Gemini in Google Sheets to clean, categorise and combine data from several sources into one central table
Analyse budget versus actual, receivables ageing, utility costs and SLAs, and verify the numbers yourself
Use Gemini Notebook to read, compare and extract contract terms with citations
Design and build an executive dashboard in Data Studio that communicates insights clearly
Write an insight brief that separates facts from assumptions, and present it
Use AI with organisational data safely, in line with PDPA and company policy

Who this course is for

Finance and accounting staff who prepare reports, follow up receivables or control budgets
IT teams, data teams and system administrators who prepare data for other departments
Operations and service teams who track SLAs, costs and indicators for each site
Supervisors and managers who read dashboards and make decisions from data
Organisations already on Google Workspace that want to cut time spent on repetitive reports with AI

Prerequisites

Basic Google Sheets or Excel skills such as sort, filter, SUM and AVERAGE
Everyday use of Google Drive and Google Docs
General experience with an AI chat tool; no programming or SQL needed
An organisation Google Workspace account on which the admin has enabled Gemini in Sheets, Gemini Notebook and Data Studio

Curriculum

Course Details

The course runs for 2 days, 6 hours per day (12 hours in total, 09:00-16:00), at Beginner to Intermediate level, as lectures with labs and workshops built on simulated data from Bluewave Group, a fictional multi-site company. The data covers revenue, receivables and ageing, budget versus actual, utility costs per site, service tickets with SLAs and sample PDF contracts. The scenarios apply to many industries, such as retail, logistics, services or building management. Learners use their own organisation's Google Workspace account with Gemini enabled by their

admin; the institute does not provide accounts or licences, and Data Studio Pro features are demonstrated only. The course differs from Data Analytics and Visualization with AI, a one-day course using Claude with Google Sheets and Looker Studio for content and marketing data, and from Google Data Studio for Data Visualization and NotebookLM and AppSheet for Work: this is a two-day, all-Google-Workspace workflow for finance and operations data that includes document extraction. Learners take home a lab guide, the sample data and a set of prompt templates.

Day 1 Preparing and Analysing Data with Gemini in Google Sheets

Section 1: Data Analytics in the AI Era and Organisational KPIs

The analysis lifecycle (Ask, Prepare, Process, Analyze, Share, Act) and the role of AI at each step
Four levels of analysis, descriptive, diagnostic, predictive and prescriptive, with real-world examples
Finance KPIs: collection rate, AR ageing, budget versus actual and unit cost
IT and operations KPIs: service SLAs, mean time to close, repeat tickets and utility cost per site
Workshop: frame the business questions, define KPIs and draft a data dictionary for the sample company

Section 2: Data Governance and PDPA When Using AI

What should and should not go into AI, such as personal data of customers, staff and counterparties, and financial data
Personal Google accounts versus organisation Workspace accounts, and the admin's role in turning Gemini on or off
The limits of AI with numbers (hallucination) and the difference between formula results and text answers
Do's and don'ts, and masking or substituting sensitive data before using AI
Lab: classify the sensitivity of the sample data sets and build a pre-AI checklist

Section 3: Lab: Gemini in Google Sheets

Use the Ask Gemini panel to ask about data and create tables, formulas and charts in plain language
Have Gemini explain complex formulas and help fix broken ones
The AI function and Fill with Gemini for categorising, summarising and extracting data across many rows
Limits of the AI function, such as reading only the range you give it, and generation quotas
Prompt pattern (role, context, task, format, constraint) and spot-checking accuracy on Thai text

Section 4: Workshop: Cleaning and Combining Data from Several Sources

Common problems: Buddhist and Gregorian dates mixed together, inconsistent site codes, numbers stored as text and customer names spelt differently
Handle blanks, duplicates and outliers with Gemini alongside formulas
Design a tidy central table and let AI help map columns between sources
Reconcile against the source with spot checks, and save prompts that work as templates

Section 5: Lab: Analysing Finance and Operations Data

Finance: budget versus actual, receivables ageing, collection rate and revenue trends by site
Operations: service tickets by type, site and SLA, with AI categorisation of complaints
Electricity and water costs by site: find anomalies, let AI propose hypotheses, then test them with data
Confirm key numbers with pivot tables and formulas, and separate facts from assumptions
Workshop: summarise five verified findings to carry into Day 2

Day 2 Extracting Data from Documents and Building an Executive Dashboard

Section 6: Gemini Notebook (formerly NotebookLM) for Business Documents

How it works: answers only from the sources you choose, with citations to the place in each document
Add sources from Google Drive such as PDFs, Google Docs and Google Sheets, and the limits of each type
Organise notebooks by document type, such as customer contracts, supplier contracts, policies and service reports
Ask questions that produce verifiable answers, and check the citations every time
Lab: build a notebook from the company's sample PDF documents

Section 7: Lab: Extracting Key Terms from Contracts

Ask about end dates, contract value, price adjustments, deposits, penalties and renewal terms
Compare terms across several contracts and spot anomalies or conflicts
Use the Studio panel to create a data table and a summary report, then check them against the originals
Export the table to Google Sheets and link it to the central table from Day 1
Re-check numbers from Gemini and Gemini Notebook against the source and state your level of confidence

Section 8: Dashboard Design Principles for Executives

Match the question to the chart: comparison, trend, composition, distribution and relationship
Charts to avoid, and why
Declutter, use colour to highlight what matters and write chart titles as insight sentences
Set the reading order for each audience, such as finance executives and operations managers

Section 9: Lab: Building a Dashboard in Data Studio

Meet Data Studio (formerly Looker Studio) and connect the central table from Google Sheets
Build scorecards, time series, bar charts and tables with filters for site, period and item type
Let AI propose a dashboard layout and help write calculated fields, then verify the results yourself
Demonstration of asking questions of data in natural language (Conversational Analytics), parts of which need Data Studio Pro
Have AI critique the dashboard and apply the suggestions you agree with

Section 10: Master Workshop: Data Storytelling and the Insight Brief

A data story structure: context, findings, implications and recommendations
Use AI to draft an executive summary that cites real numbers from the dashboard
Write a one-page insight brief that separates facts from assumptions
Five-minute group presentations, feedback and a plan for applying the work to your organisation's real data

Schedule & training options

For individuals — public rounds

No public rounds are open right now. Join the waiting list and we will contact you first when the next round opens, or ask us on LINE. Or call 02-570-8449 or 088-807-9770

Join the waiting list Ask about rounds

For organisations — in-house / private

Tailor the content to your team’s tools and projects
Your dates, at your office or live online
Quotation with tax ID for procurement

Corporate training quote

Instructors

Samit KoyomFounder & CEO, IT Genius Engineering

Frequently asked questions

How does this course differ from Data Analytics and Visualization with AI, Google Data Studio for Data Visualization and NotebookLM and AppSheet for Work?

Data Analytics and Visualization with AI is a one-day course that uses Claude with Google Sheets and Looker Studio for content and marketing data. Google Data Studio for Data Visualization focuses on building reports and charts in Data Studio itself, and NotebookLM and AppSheet for Work covers NotebookLM for documents and building apps with AppSheet. This course is a two-day, all-Google-Workspace workflow for finance and operations data, from preparing data with Gemini in Sheets and extracting contract terms with Gemini Notebook to a dashboard and an insight brief.

Which account do I need, and does the institute provide one?

The institute does not provide accounts or licences. You use your own organisation's Google Workspace account with Gemini access, with Smart features, Gemini in Sheets, Gemini Notebook and Data Studio enabled by your admin. Ask your IT team to test access to every feature before the training day. Features that need an extra plan, such as Data Studio Pro, are demonstrated only.

Can I attend with a personal Google account?

Only partly. A personal account can use Gemini Notebook and Data Studio at a basic level, but Gemini in Google Sheets needs an eligible Google Workspace or Google AI plan, and a personal account is not suitable for company data. We recommend your organisation's Workspace account.

Do I need programming or SQL skills?

No. You only need basic Google Sheets or Excel skills such as sort, filter and SUM. The course does not go into SQL, data warehousing or building machine learning models; it focuses on getting reliable, verifiable results from ready-made Google Workspace tools.

Can I bring my company's real data to class?

All labs use simulated data prepared by the institute. If you want to discuss your real report formats, mask or substitute sensitive values first, and do not bring personal or confidential data into the classroom. The course includes PDPA guidance and a checklist for working with real data afterwards.

Are Gemini Notebook and NotebookLM, and Data Studio and Looker Studio, the same tools?

Yes. Google renamed Looker Studio back to Data Studio in April 2026 and renamed NotebookLM to Gemini Notebook in July 2026. Existing reports and notebooks carry on working. Google's AI tool names and screens change often, so the training follows the latest version on the day.