Data Science with R Certification Course Overview
The Data Science with R programming certification training covers data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language. You will learn about R packages, how to import and export data in R, data structures in R, various statistical concepts, cluster analysis, and forecasting.
Skills Covered
Business analytics
R programming and its packages
Data structures and data visualization
Apply functions and DPLYR function
Graphics in R for data visualization
Hypothesis testing
Apriori algorithm
kmeans and DBSCAN clustering
Data Science with R Certification Course Curriculum
Elgibility
This Data Science with R certification training is beneficial for all aspiring data scientists including, IT professionals or software developers looking to make a career switch into Data analytics, professionals working in data and business analysis, graduates wishing to build a career in Data Science, and experienced professionals willing to harness Data Science in their fields.
- 1.1 Course Introduction
- 1.2 Introduction to Business Analytics
- Overview
- Business Decisions and Analytics
- Types of Business Analytics
- Applications of Business Analytics
- Data Science Overview
- Conclusion
- Knowledge Check
- 1.3 Introduction to R Programing
- Overview
- Importance of R
- Data Types and Variables in R
- Operators in R
- Conditional Statements in R
- Loops in R
- R script
- Functions in R
- Conclusion
- 1.4 Data Structure
- Overview
- Identifying Data Structures
- Demo Identifying Data Structures
- Assigning Values to Data Structures
- Data Manipulation
- Demo Assigning values and applying functions
- Conclusion
- 1.5 Data Visualization
- Overview
- Introduction to Data Visualization
- Data Visualization using Graphics in R
- ggplot2
- File Formats of Graphic Outputs
- Conclusion
- 1.6 Statistics for Data Science - I
- Overview
- Introduction to Hypothesis
- Types of Hypothesis
- Data Sampling
- Confidence and Significance Levels
- Conclusion
- 1.7 Statistics for Data Science - II
- Overview
- Hypothesis Test
- Parametric Test
- Non-Parametric Test
- Hypothesis Tests about Population Means
- Hypothesis Tests about Population Variance
- Hypothesis Tests about Population Proportions
- Conclusion
- 1.8 Regression Analysis
- Overview
- Introduction to Regression Analysis
- Types of Regression Analysis Models
- Linear Regression
- Demo Simple Linear Regression
- Non-Linear Regression
- Demo Regression Analysis with Multiple Variables
- Cross Validation
- Non-Linear to Linear Models
- Principal Component Analysis
- Factor Analysis
- Conclusion
- 1.9 Classification
- Overview
- Classification and Its Types
- Logistic Regression
- Support Vector Machines
- Demo Support Vector Machines
- K-Nearest Neighbours
- Naive Bayes Classifier
- Demo Naive Bayes Classifier
- Decision Tree Classification
- Demo Decision Tree Classification
- Random Forest Classification
- Evaluating Classifier Models
- Demo K-Fold Cross Validation
- Conclusion
- 1.10 Clustering
- Overview
- Introduction to Clustering
- Clustering Methods
- Demo K-means Clustering
- Demo Hierarchical Clustering
- Conclusion
- 1.11 Association
- Overview
- Association Rule
- Apriori Algorithm05
- Demo Apriori Algorithm
- Conclusion