Data Analysis Masterclass: Visualization, Statistics and Advanced Programs

Course Info

Length: 1 Week

Type: In Classroom

Available Dates

Venue

  • Feb-17-2025

    Dubai

  • Mar-10-2025

    London

  • Apr-21-2025

    Dubai

  • Apr-28-2025

    Barcelona

  • Apr-28-2025

    Singapore

  • Apr-28-2025

    Amsterdam

  • May-05-2025

    Kuala Lumpur

  • May-05-2025

    Paris

  • May-05-2025

    Istanbul

  • May-12-2025

    London

  • June-23-2025

    Dubai

  • July-14-2025

    London

  • July-28-2025

    Barcelona

  • July-28-2025

    Singapore

  • July-28-2025

    Amsterdam

  • Aug-04-2025

    Kuala Lumpur

  • Aug-04-2025

    Istanbul

  • Aug-04-2025

    Paris

  • Aug-18-2025

    Dubai

  • Sep-15-2025

    London

  • Oct-20-2025

    Dubai

  • Oct-27-2025

    Singapore

  • Oct-27-2025

    Amsterdam

  • Oct-27-2025

    Barcelona

  • Nov-03-2025

    Paris

  • Nov-03-2025

    Istanbul

  • Nov-03-2025

    Kuala Lumpur

  • Nov-10-2025

    London

  • Dec-15-2025

    Dubai

Course Details

Course Outline

5 days course

Data Visualization and Descriptive Statistics  


  • Introduction to data types, sources and variable categories 
  • Exploring a variety of 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
  • Discovering key measures of dispersion: Quartiles, variance, and standard deviation
  • Exploring statistical estimation methods:



  • Point estimation
  • Confidence intervals 

Analyzing and Comparing Two Groups


  • Types of t-tests used for comparing the means of 2 groups: 


  • Equal variances (t-test)
  • Unequal variances (t-test with Welch correction)


  • Using the Two Variance test (F-test) to determine whether variances are equal/ significantly different 
  • Exploring Chi-Square tests for analyzing categorical data:


  • Two proportions (Chi-Square) test
  • Distribution tests (Chi-Square Goodness-of-Fit) 



  • Describing the features and application of the Attraction-Repulsion Matrix 
  • Exploring profiling approaches in data analysis: Vertical vs. Horizontal profiling techniques

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 the homogeneity of variances across multiple groups 
  • Applying Proportion and Distribution tests (Chi-Square) for multi-group comparisons 
  • Exploring advanced profiling techniques used for multiple groups 


  • Attraction-Repulsion Matrix
  • Vertical and horizontal profiling


  • Discovering Pairwise Mean Comparison Methods for multi-groups:



  • General mean comparisons
  • Bonferroni and Tukey-Kramer adjustments 

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 (SLR)
  • Fundamentals of simple logistic regression:


  • Probabilistic models and validity testing (Chi-Square)
  • Classification predictions and Odds Ratio Interpretation



  • Discussions on when to use simple linear regression and simple logistic regression  

Data Analysis Projects - Best Practices


  • Describing the lifecycle of data analysis projects:


  • Defining and formulating questions
  • Designing the study
  • Previewing and preparing data for analysis 
  • Analyzing 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 visualization