Multivariate Analysis and Data Mining Course

Introduction

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.

Duration

10 Days

Target audience

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

COURSE LEVEL:

Register for the course


Face to Face Schedules By Location
Nairobi Schedules:
Code Date Duration Location Fees
MAD001 6 Feb 2023 - 17 Feb 2023 10 days Nairobi, Kenya KES 150,000 | USD 2,200 Register
MAD001 6 Mar 2023 - 17 Mar 2023 10 days Nairobi, Kenya KES 150,000 | USD 2,200 Register
MAD001 3 Apr 2023 - 14 Apr 2023 10 days Nairobi, Kenya KES 150,000 | USD 2,200 Register
MAD001 1 May 2023 - 12 May 2023 10 days Nairobi, Kenya KES 150,000 | USD 2,200 Register
MAD001 5 Jun 2023 - 16 Jun 2023 10 days Nairobi, Kenya KES 150,000 | USD 2,200 Register
MAD001 3 Jul 2023 - 14 Jul 2023 10 days Nairobi, Kenya KES 150,000 | USD 2,200 Register
MAD001 7 Aug 2023 - 18 Aug 2023 10 days Nairobi, Kenya KES 150,000 | USD 2,200 Register
MAD001 4 Sep 2023 - 15 Sep 2023 10 days Nairobi, Kenya KES 150,000 | USD 2,200 Register
MAD001 2 Oct 2023 - 13 Oct 2023 10 days Nairobi, Kenya KES 150,000 | USD 2,200 Register
MAD001 6 Nov 2023 - 17 Nov 2023 10 days Nairobi, Kenya KES 150,000 | USD 2,200 Register
MAD001 4 Dec 2023 - 15 Dec 2023 10 days Nairobi, Kenya KES 150,000 | USD 2,200 Register
Kigali Schedules:
Mombasa Schedules:
Nakuru Schedules:
Kisumu Schedules:
Naivasha Schedules:
Virtual Trainer Led Schedules
Contact Us on (+254) 715 077 817 / (+254) 792 516 000 or email us [email protected] for a virtual schedule.
E-Learning

Contact Us on (+254) 715 077 817 / (+254) 792 516 000 or email us [email protected] for E-Learning course.


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

Course Administration Details:

METHODOLOGY

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.

ACCREDITATION

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).

TRAINING VENUE

The training will be held at IRES Training Centre. The course fee covers the course tuition, training materials, two break refreshments and lunch.

All participants will additionally cater for their, travel expenses, visa application, insurance, and other personal expenses.

ACCOMMODATION AND AIRPORT PICKUP

Accommodation and airport pickup are arranged upon request. For reservations contact the Training Officer.

Email:[email protected]/[email protected]

Mob: +254 715 077 817/+250789621067

TAILOR- MADE

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 Tel: +254 715 077 817/+250789621067

Mob: +254 792516000+254 792516010 , +250 789621067 ,or mail [email protected]/[email protected]

PAYMENT

Payment should be transferred to IRES account through bank on or before start of the course.

Send proof of payment to [email protected]/[email protected]


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