R Analytics Course Details

Course Overview: This course provides a comprehensive introduction to data analytics and machine learning techniques using the R programming language. Students will learn how to manipulate, visualize, and analyze data in R, and apply machine learning algorithms to solve real-world problems. The course includes both theoretical foundations and hands-on practical sessions to reinforce learning.

Learning Objectives

• Understand the fundamentals of R programming and its applications in data analytics.
• Learn exploratory data analysis techniques and data visualization using R.
• Gain proficiency in supervised and unsupervised machine learning algorithms.
• Develop skills in model evaluation, validation, and deployment.
• Apply machine learning techniques to real-world datasets and solve practical problems.

Course Outline

Introduction to R Programming

• Introduction to R and R Studio
• Basic syntax, data types, and functions in R
• Data import/export in R
• Introduction to R packages (e.g., dplyr, tidyr)

Data Manipulation and Visualization in R

• Data manipulation using dplyr and tidyr
• Data visualization with ggplot2
• Advanced plotting techniques and customization

Exploratory Data Analysis (EDA) with R

• Data cleaning and pre-processing
• Summary statistics and visualization for EDA
• Handling missing data and outliers

Supervised Learning Algorithms

• Linear regression
• Logistic regression
• Decision trees and random forests

Unsupervised Learning Algorithms

• Clustering techniques (k-means, hierarchical clustering)
• Dimensionality reduction (PCA, t-SNE)

Advanced Topics in Machine Learning with R

• Support Vector Machines (SVM)
• Ensemble methods (bagging, boosting)
• Model evaluation and validation techniques

Deep Learning with R

• Introduction to neural networks
• Building and training neural networks using TensorFlow and Keras in R

Machine learning is a field of study within computer science that involves the use of algorithms to simulate human learning processes. These algorithms are trained using statistical methods and are capable of making predictions. As the algorithms continue to learn, their prediction accuracy improves over time.

• Module 1 – Introduction to Machine Learning
• Module 2 – Supervised Learning and Linear Regression
• Module 3 – Classification and Logistic Regression
• Module 4 – Decision Tree and Random Forest
• Module 6 – Unsupervised Learning
• Module 7 – Natural Language Processing and Text Mining
• Module 8 – Introduction to Deep Learning
• Module 9 – Time Series Analysis

Capstone Project

• Individual or group project applying machine learning techniques to a real-world dataset
• Project presentation and report