Data Science with R Programming
Data Science Certification Training With R
This course is the perfect option for aspiring data analytics that will help them to build a successful career in analytics or data sciences. The data science certification training course will have an all-around knowledge of business analytics and R.
About the course: This data analytics with R training teaches students in depth knowledge of various data analytics techniques that can be performed using R.
This course teaches in detail about the r language, r studio, and other r packages. Students will learn various methods to apply the functions of r such as DPYR, gain an understanding of data structure and perform data visualization using the various available graphics in R.
By the end of this course, the students would have mastered advanced statistical concepts such as linear and logistic regression, cluster analysis and forecasting along with data science training course.
Prerequisites: essential requirements for this course
The data science certification training course requires absolutely no knowledge of this background. People absolutely new to field of data analytics and science must take up this basic level course to get started.
Target students: With the increasing demand for skilled data scientist, students such as IT professionals, software developer and professionals working in data and business analytics can take up this course. Graduates, students or experienced professionals who want to learn more about data sciences can also take up this course.
Data Science Certification with R Course Outline:
Lesson 01 – Introduction to Business Analytics
- Overview
- Business Decisions and Analytics
- Types of Business Analytics
- Applications of Business Analytics
- Data Science Overview
Lesson 02 – Introduction to R Programming
- 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
Lesson 03 – Data Structures
- Overview
- Identifying Data Structures
- Demo Identifying Data Structures
- Assigning Values to Data Structures
- Data Manipulation
- Demo Assigning values and applying functions
Lesson 04 – Data Visualization
- Overview
- Introduction to Data Visualization
- Data Visualization using Graphics in R
- ggplot2
- File Formats of Graphic Outputs
Lesson 05 – Statistics for Data Science-I
- Overview
- Introduction to Hypothesis
- Types of Hypothesis
- Data Sampling
- Confidence and Significance Levels
Lesson 06 – 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
Lesson 07 – 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
Lesson 08 – 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
Lesson 09 – Clustering
- Overview
- Introduction to Clustering
- Clustering Methods
- Demo K-means Clustering
- Demo Hierarchical Clustering
Lesson 10 – Association
- Overview
- Association Rule
- Apriori Algorithm
- Demo Apriori Algorithm
