Data Science with R

Data Science with R

What you’ll learn

• Data Science using R programming
• Become a Data Scientist
• Data Science Learning Path
• How to learn Data Science
• Data Collection and Management
• Model Deployment and Maintenance
• Setting Expectations
• Loading Data into R
• Exploring Data in Data Science and Machine Learning
• Exploring Data using R
• Benefits of Data Cleaning
• Cross Validation in R
• Data Transformation
• Modeling Methods
• Solving Classification Problems
• Working without Known Targets
• Evaluating Models
• Confusion Matrix
• Introduction to Linear Regression
• Linear Regression in R
• Simple and Multiple Regression
• Linear and Logistic Regression
• Support Vector Machines (SVM) in R
• Unsupervised Methods
• Clustering in Data Science
• K-means Algorithm in R
• Hierarchical Clustering
• Market Basket Analysis
• MBA and Association Rule Mining
• Implementing MBA
• Association Rule Learning
• Decision Tree Algorithm
• Exploring Advanced Methods
• Using Kernel Methods
• Documentation and Deployment