Course Overview

In today’s increasingly data-driven world, the ability to analyse and interpret data effectively has become a critical skill across industries. Combining technical expertise, statistical understanding, and data visualisation capabilities allows professionals to extract meaningful insights that support informed decision-making and strategic planning.

The Data Analysis Masterclass: Visualization, Statistics, and Advanced Programs is a comprehensive 5-day training programme designed to build advanced data analysis capabilities. The course guides participants through the full data analysis lifecycle, from understanding data types and sources to applying statistical methods and creating impactful visualisations that communicate insights clearly.

Participants will also gain practical experience in managing data analysis projects, ensuring that insights are not only accurate but also actionable and aligned with business objectives. This masterclass is ideal for data analysts, business leaders, and researchers who aim to enhance their analytical expertise and apply advanced techniques to real-world challenges.

Agenda

Day — 1 Data Visualization and Descriptive Statistics

• Introduction to data types, sources, and variable categories
• Exploring a variety of data visualization techniques and their features:
  • Pie and Doughnut Charts
  • Bar Charts, Histograms, Line Graphs, and Scatter Plots
  • Heat Maps and Tukey Box Plots
  • Geographical Maps
• Understanding and applying measures of central tendency: Mean, Median, and Mode
• Exploring key measures of dispersion: Quartiles, Variance, and Standard Deviation
• Understanding statistical estimation methods:
  • Point Estimation
  • Confidence Intervals

Day — 2 Analyzing and Comparing Two Groups

• Types of t-tests used for comparing the means of two groups:
  • Equal Variances (t-test)
  • Unequal Variances (Welch’s t-test)
• Using the Two Variance Test (F-test) to determine whether variances are equal or significantly different
• Exploring Chi-Square tests for analysing categorical data:
  • Two Proportions (Chi-Square) Test
  • Distribution Tests (Chi-Square Goodness-of-Fit)
• Understanding the features and applications of the Attraction-Repulsion Matrix
• Exploring data profiling approaches: Vertical vs. Horizontal profiling techniques

Day — 3 Analyzing and Comparing Multiple Groups

• Exploring multiple mean tests for analyzing multiple groups:
  • Equal Variances (F-test and ANOVA)
  • Unequal Variances (F-test with Welch correction)
• Using the Levene test to evaluate homogeneity of variances across multiple groups
• Applying proportion and distribution tests (Chi-Square) for multi-group comparisons
• Exploring advanced profiling techniques for multiple groups:
  • Attraction-Repulsion Matrix
  • Vertical and Horizontal Profiling
• Discovering pairwise mean comparison methods for multiple groups:
  • General mean comparisons
  • Bonferroni adjustment
  • Tukey-Kramer adjustment

Day — 4 Simple Regressions

• Understanding the assumptions of Simple Linear Regression (SLR)
• Conducting Simple Linear Regression (SLR):
  • Line equation and validity testing (t-test)
  • R and R² interpretation
  • ANOVA table analysis
• Understanding the assumptions of Simple Logistic Regression
• Fundamentals of Simple Logistic Regression:
  • Probabilistic models and validity testing (Chi-Square)
  • Classification predictions and Odds Ratio interpretation
• Understanding when to use Simple Linear Regression vs Simple Logistic Regression

Day — 5 Data Analysis Projects – Best Practices

• Describing the lifecycle of data analysis projects:
  • Defining and formulating research questions
  • Designing the study
  • Preparing and cleaning data for analysis
  • Analysing results
  • Presenting and communicating findings
• Exploring various types of sampling techniques:
  • Random and Systematic Sampling
  • Multilevel, Stratified, and Cluster Sampling
  • Convenience, Quota, and Judgmental Sampling
• Overview of PMP principles for research projects
• Real-world examples of data analysis and research projects
• Lessons learned and best practices in data analysis and visualisation

Learning Outcomes

By the end of this course, participants will be able to:

• Build a strong foundation in data analysis by understanding data types, sources, and variables
• Use data visualisation techniques to communicate insights effectively through charts, graphs, and plots
• Apply statistical measures, including central tendency and dispersion, to summarise datasets
• Analyse and compare groups using statistical tests such as t-tests, F-tests, and chi-square tests
• Apply data profiling techniques to understand group behaviours and characteristics
• Conduct regression analysis, including linear and logistic regression, and interpret results
• Manage data analysis projects using best practices, from defining analytical questions to presenting findings
• Integrate PMP principles to improve the organisation and management of data analysis workflows

Who Should Attend

This course is designed for professionals seeking to deepen their understanding and practical application of data analysis techniques, including:

• Data Analysts and Data Scientists
• Business Intelligence Professionals
• Researchers
• Product Managers and Project Managers
• Marketing Professionals
• Consultants and Advisors
• Business Analysts
• Students and Graduates in Data Science or related fields