Data Analysis and Machine Learning for Statisticians using R Training Course

This training course offers a detailed exploration of data analysis and machine learning techniques using R.

It covers fundamental data handling, statistical modeling, and machine learning methods, including regression, data mining, neural networks, and clustering.

Participants will gain hands-on experience through practical case studies, equipping them with the skills to analyze complex data and apply machine-learning techniques to real-world problems.

Duration

5 days

Who Should Attend

1. Programmers
2. Data Analysts and anyone interested in machine learning/ Data Science/ Deep learning/
3. Statisticians 
4. Econometricians 

Organizational Impact

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

Personal Impact

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

Course Objectives

1. Understand and apply core statistical methods using R.
2. Develop and implement machine learning models for data analysis.
3. Gain proficiency in data wrangling, visualization, and exploration with R.
4. Evaluate and validate statistical and machine learning models.
5. Apply advanced techniques to solve real-world data analysis problems using R.

Course Outline

Module 1: Introduction to R

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

Module 2: Introduction to Machine Learning

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

Module 3: Data Mining in R

1. K-Nearest Neighbour
2. Decision Trees
3. Logistic Regression
4. Support Vector Machines
5. Outlier Detection
6. Model Evaluation
7. Case Study: Using Decision Trees and Support Vector Machines to Identify Fraudulent Transactions in Financial Data

Module 4: Neural Networking using R

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

Module 5: Clustering Analysis in R

1. K-means Clustering
2. Hierarchical Clustering
3. Density-Based Clustering
4. Gaussian Clustering Model
5. Case Study: Segmenting Customers Based on Purchase Behavior Using K-means and Hierarchical Clustering

Related Courses

1. Analyzing and Visualizing Data with Power BI Training Course
2. Data Mining and Analysis using Leximancer Training Course
3. Complete Data Protection and Privacy Training Course
4. Mastering System Analytics with Microsoft 365 Training Course
5. Fundamentals of Social Research Methods Training Course
6. Advanced Structural Design with STAAD Pro Training Course

Course Administration Details

Methodology

The instructor-led trainings are delivered using a blended learning approach and comprises presentations, guided sessions of practical exercise, web-based tutorials, and group work.

Our facilitators are seasoned industry experts with years of experience, working as professionals and trainers in these fields.

All facilitation and course materials will be offered in English. Therefore, the participants should be reasonably proficient in English.

Accreditation

Upon successful completion of this training, the participants will be issued an IRES certificate certified by the National Industrial Training Authority (NITA).

Training Modes and Location

The training will be held virtually or at any of our training locations (as requested).

The course fee covers the course tuition, training materials, two break refreshments, and lunch.

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

In addition to our virtual and self-paced training, we offer training in Kenya (Nairobi, Mombasa, Kisumu, Nakuru, and Naivasha) and at the following locations:

1. Dubai - United Arab Emirates
2. Cairo - Egypt
3. Johannesburg, Pretoria, and Cape Town - South Africa
4. Dar-es-Salaam, Zanzibar, and Arusha - Tanzania
5. Kigali - Rwanda
6. Accra - Ghana
7. Kampala - Uganda

Accommodation and Airport Transfer

Upon request, we can arrange accommodation and airport transfer for you, to suit your needs and requirements.

For bookings and reservations, please contact us through either of the following:

• Email: [email protected]
• Phone: +254715 077 817

Tailor-Made Program

Upon request, we can also customize this training to suit your needs or those of your organization.

You can have it delivered at our training centres or a convenient location.

For further inquiries, please contact us.