Multivariate Analysis and Data Mining Course


Data Mining and Applied Multivariate Analysis have resulted in data-intensive managerial environments. A virtual flood of information flows through systems, such as enterprise resource planning and the Internet. What to do with all this data? How can it be transformed into actionable information? 

The objective of this course is to introduce business leaders to powerful methods for understanding and obtaining managerial insights from multivariate data. The course is designed for both managers who have direct responsibility for producing analyses and for managers who have to interact with area experts who produce the analyses. The methods include data reduction techniques - principle component analysis, factor analysis, and multidimensional scaling; classification methods - discriminate analysis and cluster analysis; and relational methods - multivariate regression, logistic regression, and neural networks. Emphasis is placed on the application of the method, the type of data that it uses, the assumptions behind it, and interpreting the output.

Multivariate data typically consist of many records, each with readings on two or more variables, with or without an “outcome” variable of interest. This course covers the theoretical foundations of multivariate statistics including multivariate data, common distributions, and discriminant analysis. Procedures covered in the course include multivariate analysis of variance (MANOVA), principal components, factor analysis, and classification.

The course will be taught using Python and R software.


10 Days

Target audience

Data analysts, Research Associates, Data managers, Statisticians, Database developers and administrators, Business Engineers, and Analytics Managers.

Foundation Level

Course Objectives

At the end of this IRES training course, you will learn how to:

  • Understand the underlying theory for the analysis of multivariate data.

  • Choose appropriate procedures for multivariate analysis.

  • Interpret the output of such analyses.

  • Describe the multivariate normal distribution

  • Depict multivariate data with scatterplots

  • Conduct principal components analysis

  • Conduct correspondence analysis

  • Conduct discriminant analysis

Course Outline

Module 1:

  • Introduction
  • Graphical Methods for Multivariate Data
  • Principal Component Analysis

Module 2:

Multivariate Data

  • Descriptive Statistics
  • Rows (Subjects) vs. Columns (Variables)
  • Covariance’s, Correlations and Distances
  • The Multivariate Normal Distribution
  • Scatterplots
  • More than 2 Variable Plots
  • Assessing Normality

Module 3

Multivariate Normal Distribution, MANOVA, & Inference

  • Details of the Multivariate Normal Distribution
  • Wishart Distribution
  • Hotelling T2 Distribution
  • Multivariate Analysis of Variance (MANOVA)
  • Hypothesis Tests on Covariances
  • Joint Confidence Interval

Module 4

Multidimensional Scaling & Correspondence Analysis

  • Principal Components
  • Correspondence Analysis
  • Multidimensional Scaling

Module 5

Discriminant Analysis

  • Classification Problem
  • Population Covariances Known
  • Population Covariances Estimated
  • Fisher's Linear Discriminant Function
  • Validation

Module 6

  • Dimension Reduction Techniques: principal components, correspondence analysis and projection pursuit
  • Classification and Clustering: multidimensional scaling, discriminant and cluster analysis, and classification and regression trees (CART)

Module 7

  • Analysis of Covariance Structures/Latent Variable Models: principle components (revisit), factor analysis and covariance structure models (time permitting)
  • Design of Experiments

Module 8

  • Multivariate regression methods (MLR,PCR,PLSR)
  • Strategies for model selection and validation (bias-variance trade-off)
  • Features and variables selection

Module 9

  • Classification methods (Machine learning)
  • Time series analysis
  • Prediction Error Methods for the Identification of dynamical systems
  • Kalman filters

Module 10

  • Metamodeling & hybrid modelling
  • Compressed sensing
  • Independent Component Analysis
  • PARAFAC, multiblock (sensor fusion) and IDLE modelling

Enroll for a Face-to-Face (In-Person) Class

Face-to-Face Registration By Location

We use the highest quality learning facilities to make sure your experience is as comfortable as possible. Our face to face schedules allow you to choose any classroom course of your choice to be delivered at any venue of your choice - offering you the ultimate in convenience and value for money.

Virtual Trainer Led Schedules

We ensure your learning experience is top-notch by utilizing the finest learning facilities available. Our virtual classes offer you the flexibility to attend courses remotely from anywhere, providing unparalleled convenience and exceptional value for your investment. We understand that your comfort and convenience are paramount, which is why we bring the classroom experience directly to you, wherever you may be. With our virtual class schedules, you have the freedom to choose from a wide range of courses and attend them from the comfort of your own space.

Customize Attendance Dates

FAQs & Course Administration Details:

This training can also be customized to suit the needs of your institution upon request. You can have it delivered in our IRES Training Centre or at a convenient location. For further inquiries, please contact us on Phone: +254 715 077 817 or Email:
The instructor led trainings are delivered using a blended learning approach and comprise of presentations, guided sessions of practical exercise, web-based tutorials and group work. Our facilitators are seasoned industry experts with years of experience, working as professional and trainers in these fields. All facilitation and course materials will be offered in English. The participants should be reasonably proficient in English.
Upon successful completion of this training, participants will be issued with an Indepth Research Institute (IRES) certificate certified by the National Industrial Training Authority (NITA).
Payment should be transferred to IRES account through bank on or before start of the course. Send proof of payment to
Accommodation and airport pickup are arranged upon request. For reservations contact the Training Officer. Email: Phone: +254 715 077 817.