Course Overview
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
Target Audience
- Programmers
- Data Analysts and anyone interested in machine learning/ Data Science/ Deep learning/
- Statisticians
- Econometricians
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 Outline
Course Objectives
- Understand and apply core statistical methods using R.
- Develop and implement machine learning models for data analysis.
- Gain proficiency in data wrangling, visualization, and exploration with R.
- Evaluate and validate statistical and machine learning models.
- Apply advanced techniques to solve real-world data analysis problems using R.
Course Modules
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
Related Courses
Course Administration Details
Methodology
These instructor-led training sessions are delivered using a blended learning approach and include presentations, guided practical exercises, web-based tutorials, and group work. Our facilitators are seasoned industry experts with years of experience as professionals and trainers in these fields. All facilitation and course materials are offered in English. Participants should be reasonably proficient in the language.
Accreditation
Upon successful completion of this training, participants will be issued 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 to their travel expenses, visa application, insurance, and other personal expenses.
Accommodation and Airport Transfer
Accommodation and Airport Transfer are arranged upon request. For reservations contact the Training Officer.
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:
Payment
Payment should be transferred to the IRES account through a bank on or before the start of the course. Send proof of payment to outreach@indepthresearch.org
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