R Programming for Data Science & Machine Learning Course Overview
The 'R Programming for Data Science & Machine Learning' course offers a comprehensive introduction to R, equipping data analysts, statisticians, and aspiring data scientists with essential skills. Participants will learn to manipulate data, perform statistical analyses, and develop machine learning models, making it crucial for professionals aiming to excel in data-driven decision-making and predictive analytics.
Course outline & what you'll learn
- Setting up the R environment
Overview of R packages
- Data frames and tibbles
- Data transformation functions
- Filtering, selecting, and summarizing data
- Basic principles of data visualization
- Creating different types of plots
- Customizing visualizations
- Understanding data distributions
- Identifying patterns and anomalies
- Descriptive statistics
- Hypothesis testing
- Regression analysis
- ANOVA and chi-squared tests
Overview of machine learning concepts
- Types of machine learning: supervised vs. unsupervised
- Linear regression modeling
- Decision trees and random forests
- Model evaluation metrics
- Clustering algorithms
- Support vector machines
- Neural networks basics
- Handling large datasets
- Introduction to parallel processing in R
- Practical applications in various industries
- Capstone project to demonstrate learning
- Further learning resources
- Career opportunities in data science and machine learning
Why train with Traincrest
This Open Source course is delivered by Traincrest's certified instructors, live online or in the classroom, with hands-on labs and a 98% exam success rate. Trusted by 500+ companies and 50,000+ students worldwide.