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What You'll Learn

Learn data analysis and machine learning using R — from data preparation to predictive modeling and visualization.
Course Benefits
Industry Certification

Internationally recognized qualification

Expert Instructors

Learn from industry professionals

Dedicated Support

Assistance during and after training

Practical Skills

Apply knowledge immediately

Comprehensive 5-day curriculum with all materials included
Hands-on exercises and real-world case studies
Valuable networking opportunities with peers and experts
Post-course resources and refresher materials
Training on Data Analysis and Machine Learning for Statisticians using R - Course Cover Image
Duration 5 Days
Level Advanced
Format In-Person

Course Overview

Featured

This training course provides statisticians and data professionals with practical knowledge of how to apply machine learning and advanced data analysis using R. The course blends traditional statistical techniques with modern data science approaches, enabling participants to build predictive models, interpret results, and make data-driven decisions efficiently.

Duration

5 Days

Who Should Attend

  • Statisticians and data analysts

  • Monitoring and evaluation professionals

  • Researchers and academics

  • Data managers in public or private sectors

Course Impact

Organizational Impact

  • Enhanced ability to analyze complex data and derive actionable insights.
  • Improved decision-making through advanced statistical and machine learning techniques.
  • Increased efficiency in data processing and model development.
  • Strengthened data-driven strategy and business operations.
  • Development of a skilled team proficient in R for data analysis and machine learning.

Personal Impact

  • Mastery of R for advanced data analysis and machine learning applications.
  • Enhanced ability to apply statistical and machine learning methods to real-world problems.
  • Improved career prospects with expertise in a widely-used data analysis tool.
  • Increased confidence in handling and interpreting complex datasets.
  • Expanded skill set in both statistical analysis and machine learning techniques.

Course Objectives

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

  • Manage and preprocess data using R

  • Apply statistical and machine learning methods for data analysis

  • Build and evaluate predictive models

  • Visualize and interpret analytical results effectively

  • Use R tools for automation and reporting

Course Outline

Module 1: Introduction to R

  • Introduction to R
  • Various libraries in R and importation of data
  • Data cleaning and reading using R
  • Working with variables, vectors, matrices, factors, data frames, lists, and arrays in R
  • Learning different data types in R
  • Learning about various models in R
  • Case Study: Analyzing and Cleaning Sales Data from a Retail Store to Create a Summary Report

Module 2: Introduction to Machine Learning

  • Introduction to Machine Learning
  • Comparison of Supervised and Unsupervised Learning
  • R libraries suitable for machine learning
  • Linear and Logistic Regression using R
  • Understanding robust models used in machine learning
  • Case Study: Building and Evaluating a Predictive Model for Customer Churn Using Logistic Regression

Module 3: Data Mining in R

  • K-Nearest Neighbour
  • Decision Trees
  • Logistic Regression
  • Support Vector Machines
  • Outlier Detection
  • Model Evaluation
  • Case Study: Using Decision Trees and Support Vector Machines to Identify Fraudulent Transactions in Financial Data

Module 4: Neural Networking using R

  • Understanding Neural Networks
  • Learning about Activation Functions, Hidden Layers, Hidden Units
  • Training a Perceptron
  • Important Parameters of Perceptron
  • Limitations of a Single-Layer Perceptron
  • Illustrating Multi-Layer Perceptron
  • Back-propagation – Learning Algorithm
  • Understanding Back-propagation – Using Neural Network Example in R
  • Case Study: Developing a Neural Network Model to Predict Product Demand Based on Historical Sales Data

Module 5: Clustering Analysis in R

  • K-means Clustering
  • Hierarchical Clustering
  • Density-Based Clustering
  • Gaussian Clustering Model
  • Case Study: Segmenting Customers Based on Purchase Behavior Using K-means and Hierarchical Clustering

Prerequisites

No specific prerequisites required. This course is suitable for beginners and professionals alike.

Course Administration Details

Customized Training

This training can be tailored to your institution needs and delivered at a location of your choice upon request.

Requirements

Participants need to be proficient in English.

Training Fee

The fee covers tuition, training materials, refreshments, lunch, and study visits. Participants are responsible for their own travel, visa, insurance, and personal expenses.

Certification

Upon successful completion of this course, participants will be issued with a certificate from Ideal Workplace Solutions certified by the National Industrial Training Authority (NITA) under License NO: NITA/TRN/2734.

Accommodation

Accommodation can be arranged upon request. Contact via email for reservations.

Payment

Payment should be made before the training starts, with proof of payment sent to outreach@idealworkplacesolutions.org.

For further inquiries, please contact us on details below:

Register for the Course

Select a date and location that works for you.

In-Person Training Schedules


January 2026
Date Days Venue Fee (VAT Incl.) Register
5 Jan - 9 Jan 2026 5 days Nairobi, Kenya KES 99,000 | USD 1,400 Enroll Now
5 Jan - 9 Jan 2026 5 days Cape Town, South Africa USD 3,500 Enroll Now
5 Jan - 9 Jan 2026 5 days Dubai, United Arabs Emirates USD 4,000 Enroll Now
5 Jan - 9 Jan 2026 5 days Zanzibar, Tanzania USD 2,200 Enroll Now
12 Jan - 16 Jan 2026 5 days Mombasa, Kenya KES 115,000 | USD 1,500 Enroll Now
12 Jan - 16 Jan 2026 5 days Kigali, Rwanda USD 1,800 Enroll Now
12 Jan - 16 Jan 2026 5 days Accra, Ghana USD 5,950 Enroll Now
12 Jan - 16 Jan 2026 5 days Kampala, Uganda USD 2,200 Enroll Now
19 Jan - 23 Jan 2026 5 days Dar es Salaam, Tanzania USD 2,000 Enroll Now
19 Jan - 23 Jan 2026 5 days Johannesburg, South Africa USD 3,100 Enroll Now
19 Jan - 23 Jan 2026 5 days Nakuru, Kenya KES 105,000 | USD 1,400 Enroll Now
19 Jan - 23 Jan 2026 5 days Dakar, Senegal USD 3,500 Enroll Now
26 Jan - 30 Jan 2026 5 days Pretoria, South Africa USD 3,100 Enroll Now
26 Jan - 30 Jan 2026 5 days Kisumu, Kenya KES 105,000 | USD 1,500 Enroll Now
26 Jan - 30 Jan 2026 5 days Naivasha, Kenya KES 105,000 | USD 1,400 Enroll Now
26 Jan - 30 Jan 2026 5 days Arusha, Tanzania USD 2,000 Enroll Now
5 Jan - 9 Jan 2026
5 days
Venue:
Nairobi, Kenya
Fee (VAT Incl.):
KES 99,000
USD 1,400
Enroll Now
5 Jan - 9 Jan 2026
5 days
Venue:
Cape Town, South Africa
Fee (VAT Incl.):
USD 3,500
Enroll Now
5 Jan - 9 Jan 2026
5 days
Venue:
Dubai, United Arabs Emirates
Fee (VAT Incl.):
USD 4,000
Enroll Now
5 Jan - 9 Jan 2026
5 days
Venue:
Zanzibar, Tanzania
Fee (VAT Incl.):
USD 2,200
Enroll Now
12 Jan - 16 Jan 2026
5 days
Venue:
Mombasa, Kenya
Fee (VAT Incl.):
KES 115,000
USD 1,500
Enroll Now
12 Jan - 16 Jan 2026
5 days
Venue:
Kigali, Rwanda
Fee (VAT Incl.):
USD 1,800
Enroll Now
12 Jan - 16 Jan 2026
5 days
Venue:
Accra, Ghana
Fee (VAT Incl.):
USD 5,950
Enroll Now
12 Jan - 16 Jan 2026
5 days
Venue:
Kampala, Uganda
Fee (VAT Incl.):
USD 2,200
Enroll Now
19 Jan - 23 Jan 2026
5 days
Venue:
Dar es Salaam, Tanzania
Fee (VAT Incl.):
USD 2,000
Enroll Now
19 Jan - 23 Jan 2026
5 days
Venue:
Johannesburg, South Africa
Fee (VAT Incl.):
USD 3,100
Enroll Now
19 Jan - 23 Jan 2026
5 days
Venue:
Nakuru, Kenya
Fee (VAT Incl.):
KES 105,000
USD 1,400
Enroll Now
19 Jan - 23 Jan 2026
5 days
Venue:
Dakar, Senegal
Fee (VAT Incl.):
USD 3,500
Enroll Now
26 Jan - 30 Jan 2026
5 days
Venue:
Pretoria, South Africa
Fee (VAT Incl.):
USD 3,100
Enroll Now
26 Jan - 30 Jan 2026
5 days
Venue:
Kisumu, Kenya
Fee (VAT Incl.):
KES 105,000
USD 1,500
Enroll Now
26 Jan - 30 Jan 2026
5 days
Venue:
Naivasha, Kenya
Fee (VAT Incl.):
KES 105,000
USD 1,400
Enroll Now
26 Jan - 30 Jan 2026
5 days
Venue:
Arusha, Tanzania
Fee (VAT Incl.):
USD 2,000
Enroll Now

Request Custom Training


We offer customized training solutions tailored to your organization's specific needs:

  • Training at your preferred location
  • Customized content to address your specific challenges
  • Flexible scheduling to accommodate your team
  • Cost-effective solution for training multiple employees
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Frequently Asked Questions

Find answers to common questions about this course

Basic understanding of R is helpful, but guided instruction is provided.
Yes, it integrates traditional statistical analysis with machine learning techniques.
Yes, each module includes guided exercises using real datasets.
R and RStudio, which are open-source and free to install.
Yes, a certificate of completion is awarded at the end of the course.
Training on Data Analysis and Machine Learning for Statisticians using R

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