Data Science and Machine Learning Workshop with R Training by Tonex
This comprehensive workshop, “Data Science and Machine Learning with R,” offered by Tonex, provides participants with a hands-on experience in leveraging the power of R for data analysis and machine learning. Participants will gain practical insights into R programming, statistical analysis, and machine learning techniques, enabling them to make informed decisions and predictions from complex datasets.
Tonex’s “Data Science and Machine Learning Workshop with R” equips professionals with practical skills in utilizing R for robust data analysis and machine learning. This workshop covers R programming basics, statistical analysis, and explores key machine learning algorithms. Participants will gain hands-on experience in predictive modeling, feature engineering, and model evaluation.
The course emphasizes data preprocessing and cleansing techniques, and introduces popular R packages for data science. Ideal for analysts, data scientists, and decision-makers, this workshop offers a comprehensive understanding of the end-to-end data science workflow, empowering participants to make informed decisions and predictions from complex datasets.
Learning Objectives:
- Develop proficiency in R programming for data manipulation and visualization.
- Acquire essential statistical analysis skills to uncover patterns and trends in data.
- Explore machine learning fundamentals and algorithms using the R ecosystem.
- Implement predictive modeling and classification techniques in real-world scenarios.
- Master the art of feature engineering and model evaluation for optimal performance.
- Understand the importance of data preprocessing and cleansing in the machine learning pipeline.
- Gain hands-on experience with popular R packages for data science and machine learning.
- Develop a comprehensive understanding of the end-to-end data science workflow with R.
Audience: This workshop is designed for professionals and enthusiasts seeking to enhance their skills in data science and machine learning using the R programming language. It is ideal for analysts, data scientists, engineers, and decision-makers looking to extract meaningful insights from data and build predictive models.
Course Outline:
Introduction to R Programming
- Basics of R syntax
- Data structures in R
- Functions and loops
- Data manipulation with dplyr
Statistical Analysis with R
- Descriptive statistics
- Inferential statistics
- Hypothesis testing
- Exploratory data analysis (EDA)
Machine Learning Fundamentals
- Supervised vs. unsupervised learning
- Model training and testing
- Cross-validation techniques
- Overfitting and underfitting
Predictive Modeling in R
- Linear regression
- Decision trees and random forests
- Support vector machines (SVM)
- Ensemble methods
Feature Engineering and Model Evaluation
- Importance of feature selection
- Techniques for feature engineering
- Metrics for model evaluation
- Model interpretation and validation
Data Preprocessing and Cleansing
- Dealing with missing data
- Outlier detection and handling
- Normalization and scaling
- Imbalanced data handling
R Packages for Data Science and Machine Learning
- Introduction to tidyverse
- Implementation of ggplot2 for visualization
- Utilizing caret for machine learning
- Advanced topics in tidymodels
End-to-End Data Science Workflow
- Project planning and goal definition
- Data acquisition and exploration
- Model development and deployment
- Continuous improvement and iteration