Description

Course Overview

This intensive three-day programme is designed for professionals who already work with
data and want to strengthen their capabilities in advanced analytics, statistical modelling,
predictive techniques, visualisation, and data-driven decision-making.
The course combines theory, demonstrations, practical exercises, and collaborative
workshops. Participants will work with realistic datasets and industry-relevant case studies
throughout the programme.
Who should attend?
• Data Analysts
• Business Intelligence Professionals
• Data Scientists (junior to intermediate)
• Business Analysts
• Finance and Operations Analysts
• Managers responsible for analytics teams
• Professionals transitioning into advanced analytics roles
Prerequisites
Participants should have:
• Basic understanding of statistics
• Experience working with spreadsheets or BI tools
• Familiarity with SQL, Python, R, or analytical software
• Knowledge of basic data visualisation concepts

Learning Outcomes
By the end of the course, participants will be able to:
1. Apply advanced analytical methods to complex datasets
2. Build and interpret predictive models
3. Perform exploratory and diagnostic analytics effectively
4. Use statistical methods to support business decisions
5. Create impactful dashboards and visual narratives
6. Evaluate model performance and analytical validity
7. Communicate analytical findings to stakeholders
8. Implement a structured analytics workflow

Recommended Tools & Technologies
• Python (Pandas, NumPy, Scikit-learn)
• SQL
• Power BI or Tableau
• Excel Advanced Analytics Tools
• Jupyter Notebook
• Optional: R, Alteryx, Snowflake
Course Format
• Instructor-led sessions
• Guided demonstrations
• Hands-on workshops
• Group discussions
• Team exercises
• Capstone case study
• Knowledge checks and quizzes

Module 1: Foundations of Advanced Analytics & Exploratory Data Analysis
Module 1 Objectives
Participants will:
• Understand the advanced analytics lifecycle
• Learn best practices in data preparation
• Perform exploratory data analysis (EDA)
• Identify patterns, anomalies, and trends
• Apply statistical thinking to business problems
Session 1: Introduction to Advanced Data Analytics
Topics Covered
• What is advanced analytics?
• Types of analytics:
o Descriptive
o Diagnostic
o Predictive
o Prescriptive
• Analytics maturity models
• Data-driven decision making
• Common analytics use cases across industries
• Ethical considerations in analytics
Activity
Group discussion:
“Identify analytics opportunities in your organisation.”
Session 2: Data Preparation & Data Quality
Topics Covered
• Data acquisition strategies
• Structured vs unstructured data
• Data cleaning techniques
• Handling missing values
• Outlier detection
• Data transformation
• Feature engineering basics
• Data governance principles
Hands-on Exercise
Participants clean and prepare a messy dataset for analysis.
Session 3: Exploratory Data Analysis (EDA)
Topics Covered
• Understanding distributions
• Correlation analysis
• Trend analysis
• Segmentation techniques
• Dimensionality reduction concepts
• Detecting anomalies
• Identifying business insights through EDA
Practical Activities
Participants:
• Generate summary statistics
• Build exploratory visualisations
• Analyse customer behaviour patterns
• Identify hidden insights
Session 4: Statistical Foundations for Analytics
Topics Covered
• Probability concepts
• Hypothesis testing
• Confidence intervals
• Regression fundamentals
• Statistical significance
• Sampling techniques
• Bias and variance
Workshop
Statistical analysis of operational performance data.
Module 1 Deliverables
Participants produce:
• Cleaned dataset
• EDA report
• Initial business insights summary
• Statistical findings document

Module 2 Predictive Analytics & Machine Learning
Module 2 Objectives
Participants will:
• Understand predictive modelling concepts
• Build machine learning models
• Evaluate model performance
• Interpret analytical outputs
• Apply predictive analytics to business scenarios
Session 1: Predictive Analytics Fundamentals
Topics Covered
• Predictive analytics workflow
• Supervised vs unsupervised learning
• Classification vs regression
• Training and testing datasets
• Cross-validation
• Overfitting and underfitting
• Feature selection methods
Demonstration
Building a simple predictive model from scratch.
Session 2: Regression Models
Topics Covered
• Linear regression
• Multiple regression
• Logistic regression
• Interpreting coefficients
• Evaluating regression performance
• Business forecasting applications
Hands-on Exercise
Predicting customer churn or sales trends.
Session 3: Classification & Clustering
Topics Covered
• Decision trees
• Random forests
• K-means clustering
• Customer segmentation
• Classification metrics:
o Accuracy
o Precision
o Recall
o F1 Score
o ROC-AUC
Practical Lab
Participants create segmentation models and classification models.
Session 4: Model Evaluation & Interpretation
Topics Covered
• Model explainability
• Confusion matrices
• Feature importance
• Bias detection
• Ethical AI considerations
• Interpreting outputs for business stakeholders
Group Exercise
Compare multiple predictive models and recommend the best solution.
Module 2 Deliverables
Participants produce:
• Predictive model notebook
• Model evaluation report
• Segmentation analysis
• Business recommendation summary

Module 3 Advanced Visualisation, Storytelling & Analytics Strategy
Module 3 Objectives
Participants will:
• Build advanced dashboards
• Communicate insights effectively
• Develop data storytelling skills
• Translate analytics into business action
• Design analytics implementation strategies
Session 1: Advanced Data Visualisation
Topics Covered
• Principles of effective visualisation
• Dashboard design best practices
• Choosing the right chart type
• Interactive dashboards
• KPI design
• Visual perception and cognitive load
• Common dashboard mistakes
Hands-on Workshop
Build an executive analytics dashboard.
Session 2: Data Storytelling & Communication
Topics Covered
• Structuring analytical narratives
• Presenting insights to executives
• Storyboarding techniques
• Communicating uncertainty
• Translating technical findings into business language
• Stakeholder engagement strategies
Activity
Participants present findings from previous exercises.
Session 3: Analytics Strategy & Operationalisation
Topics Covered
• Embedding analytics into business processes
• Analytics governance
• Data ethics and compliance
• Building analytics roadmaps
• Automation opportunities
• Real-time analytics concepts
• Measuring analytics ROI
Group Workshop
Design an enterprise analytics implementation plan.
Session 4: Capstone Case Study
Scenario
Participants work in teams to solve a realistic business problem using:
• Data preparation
• Exploratory analysis
• Predictive modelling
• Dashboard creation
• Executive presentation