Training on Data Analysis and Machine Learning for Statisticians using R

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

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.

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

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