Mastering AI-Powered Data Analytics for Better Business Decisions & Insights.
Better business decisions and insights with Excel and Python.
Course overview.
AI DATA ANALYTICS TRAINING MALAYSIADevelop practical analytics skills inside Excel, moving from descriptive and diagnostic analysis to predictive models and business decisions.
The first day covers Excel’s forecasting tools, statistical functions, the Data Analysis ToolPak, scenario analysis and optimisation. Use Power Query and Python pandas to prepare data, and practise choosing the right technique for the business question.
The second day introduces Python in Excel for regression, classification, model validation and time-series forecasting. Work with scikit-learn and statsmodels, interpret results and assess the assumptions behind a prediction.
Hands-on labs use business examples from finance, HR and operations. A capstone project combines data preparation, predictive modelling, validation and forecasting into an analytics case study and decision-ready dashboard. Confirm required software features and organisational data-handling policies before the workshop.
Key takeaways.
AI DATA ANALYTICS TRAINING MALAYSIA- Master Excel's upgraded AI features (Analyze Data, Forecast Sheet, Linked Data Types, Python in Excel) for faster insights
- Apply regression, correlation, ANOVA and sampling with the Data Analysis ToolPak to uncover drivers and patterns
- Build presentation-ready forecasts with confidence intervals using Forecast Sheet and related functions
- Run what-if simulations, break-even analyses and scenario comparisons to support planning and budgeting
- Optimize complex business decisions with Solver under real-world constraints like staffing, budgets or inventory
- Use Python in Excel to apply machine learning models (scikit-learn) for prediction and classification directly inside workbooks
- Perform time-series forecasting and anomaly detection with statsmodels and Prophet for forward-looking insights
- Deliver a polished capstone project — a decision-ready analytics case study and dashboard to take back to work
Who should attend.
This workshop is crucial for business leaders, managers and professionals who want to harness AI-driven analytics to drive smarter decisions, uncover insights, and future-proof their careers. This training is highly recommended for:
- Directors & General Managers
- C-Suite Executives: CEOs, CFOs, CMOs, CTOs and other top-level leaders
- Managers & Team Leaders
- Finance, HR, IT, Operations & Analytics Professionals who work with business data daily
- Business Owners
- Anyone who wants to master AI & Python capabilities to make data-driven decisions
Prerequisites.
No prior knowledge of programming, AI or LLM is required.
The programme.
AI DATA ANALYTICS TRAINING MALAYSIASESSION 1The Case For Ai-powered Data Analytics
- Why Excel remains the #1 enterprise analytics tool for decision-making
- Recent Excel upgrades:
- Analyze Data (Home) — automatic insights, trends, outliers
- Forecast Sheet (Data) — one-click time-series forecasting
- Linked Data Types (Data) — AI-enriched fields (e.g. Currencies, Stocks, Geography)
- Python in Excel (Formulas) — advanced ML inside the grid
- Privacy & compliance: why Excel + in-built Python mitigates LLM data-exposure risks
- World Economic Forum 2030 skills: AI & Big Data as career-critical capabilities
SESSION 2Excel's Modern Forecasting Toolbox
- Forecast Sheet: Create quick, presentation-ready forecasts with tables, charts, and confidence intervals
- Powered by:
- FORECAST.ETS (Exponential Triple Smoothing)
- FORECAST.ETS.CONFINT (confidence bands)
- FORECAST.ETS.SEASONALITY (pattern detection)
- FORECAST.ETS.STAT/ERROR (fit and accuracy).
- Other Core Forecast and Statistical Functions:
- TREND – Linear regression line for historical data
- FORECAST.LINEAR – Predict future values via regression
- GROWTH – Exponential growth modeling
- CORREL – Correlation strength between variables
- RSQ – Coefficient of determination (R²) to test regression fit
- Hands-on Lab: When to Use What
- Forecast Sheet: Quick, presentation-ready forecasts with charts and confidence intervals
- TREND / FORECAST.LINEAR / GROWTH: Regression and growth formulas to project future outcomes
- CORREL / RSQ: Validating relationships or regression quality
SESSION 3Data Analysis Toolpak – Business Analysis Deep Dive Toolkit
- One of Excel's most powerful built-ins for business insights you can't get with basic formulas
- Hands-on Lab: Core Techniques and Use Cases
- Descriptive Stat: Summarize key patterns in HR, finance or operations data
- Regression: Identify drivers of revenue growth or cost reduction
- Correlation: Spot relationships between employee engagement and performance
- ANOVA: Compare performance across branches, product categories or teams to see where results significantly differ
- Moving Averages: Smooth out volatility in sales, operations or staffing trends
- Sampling: Test a strategy on a representative dataset before scaling
- The ToolPak transforms raw data into evidence-based decisions: faster HR insights, sharper financial planning and more efficient operations
SESSION 4Optimizing Business Decisions Under Scenarios And Constraints
- Moving beyond "what happened" to "what should we do"
- Core Decision Tools:
- What-If Analysis – Explore outcomes by changing key inputs
- Goal Seek – Find the input needed to reach a target result
- Scenario Manager – Compare best, worst and base case business scenarios
- Solver – Optimize decisions under real-world constraints (e.g. staffing, budgets, inventory)
- Hands-on Lab: Real-World Applications
- Goal Seek: Calculate break-even points or target sales needed to hit revenue goals
- Scenario Manager: Compare financial forecasts under optimistic, base, and pessimistic assumptions
- Solver: Allocate resources or minimize costs while meeting operational constraints
- Why It Matters: Transform Excel from simple reporting into decision intelligence that supports planning, budgeting, and resource optimization
SESSION 5Preparing Data For Analysis In Excel: Power Query Vs Python Pandas
- The Challenge: In most analytics projects, the biggest obstacle isn't the formulas or charts — it's dealing with messy, inconsistent or fragmented data that blocks meaningful analysis
- Transformation methods in Excel:
- Power Query
- Designed for business users with an intuitive point-and-click interface
- One-click refresh ensures automated, repeatable transformation
- Python Pandas
- Designed for analysts who need flexibility
- Code-driven transformations enable custom, scalable and reusable workflows
SESSION 6Python In Excel – Unlocking Ai-powered Analytics
- Why Python in Excel is a game-changer: advanced analysis inside the spreadsheet; assess data handling and organisational policies before use
- In-built libraries every professional should know:
- pandas – Clean, reshape and merge messy datasets faster than manual formulas
- scikit-learn – Apply machine learning for prediction and classification (e.g. customer churn, sales forecasts)
- statsmodels / Prophet – Time-series forecasting with trends and seasonality
- Why It Matters: Python extends Excel from "analysis" to AI-powered decision intelligence, all while keeping workflows inside the corporate environment
SESSION 7Predictive Analytics With Scikit-learn
- Move from descriptive reporting to forward-looking prediction
- Core techniques: regression and classification models
- Business applications:
- Predict revenue growth from marketing spend
- Assess customer or employee churn risk
- Develop credit or risk scoring models
- Hands-on Lab: Build a predictive regression model in Excel using scikit-learn
SESSION 8Validating And Explaining Results With Statsmodels
- Why model validation is critical for decision-making
- Key tools: regression diagnostics, p-values, coefficients, and confidence intervals
- Business applications:
- Identify which factors drive staff attrition
- Quantify the effect of operational costs on margins
- Hands-on Lab: Run regression diagnostics and interpret outputs directly inside Excel
SESSION 9Time Series Forecasting With Statsmodels
- Advanced time series with trends and seasonality
- Business applications:
- Forecast sales, demand or staffing levels
- Detect anomalies in expenses or operational data
- Hands-on Lab: Build a seasonal forecast model with statsmodels and flag anomalies
SESSION 10Capstone Project – From Data To Decision
- End-to-end project using a real business dataset
- Dataset options:
- Sales/Finance dataset
- Predictive maintenance dataset
- Bring Your Own Data (BYOD)
- Workflow:
- Build predictive models with scikit-learn
- Validate drivers and assumptions with statsmodels
- Apply time-series forecasting for forward planning
- Outcome: A polished analytics case study and decision-ready dashboard participants can apply at work
- Throughout the workshop, participants will work through hands-on labs that build their analytics capabilities — moving from descriptive insights to predictive models — and walk away with practical skills for confident, data-driven decision-making.
Your trainer.
AI DATA ANALYTICS TRAINING MALAYSIAAlvin Yong is a UK-registered Fellow Chartered and Certified Accountant (FCCA). He obtained his Master of Science in Professional Accountancy (Merit) in 2017 from the University of London.
To bring.
AI DATA ANALYTICS TRAINING MALAYSIAReviews from our Malaysian clients.
AI DATA ANALYTICS TRAINING MALAYSIA“Content is very good, notes are sufficiently detailed, presentation is clear and pace is good. Alvin entertained questions calmly, clearly, and with sufficient depth. Very knowledgeable and qualified for the training subject.”
“Alvin is an excellent trainer. He led interesting, interactive and focused discussions during the workshop.”
“Alvin was well prepared, knowledgeable and easy to follow. Level of detail was where it needed to be and the documentation was very thorough. The course went quickly which was always a good sign.”
“Fantastic trainer in terms of knowledge and skills. Extremely friendly, helpful and patient.”
“Good understanding and knowledge of the subject. Attentive and responsive to participants' queries. Presented the subject very clearly.”
“The trainer is knowledgeable. He was helpful. He handled the class material well.”
“Very informative. Gave an excellent count of how-to's.”
“Alvin is a very people person. Sound knowledge in his area of expertise. He carried out the programme well.”
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