Data Science with R Training at Massive Tech- This Course will make you an expert in data analytics using the R programming language. The Data Science with R programming course covers data exploration, data visualization, predictive analytics, and descriptive analytics techniques with R language. You will learn about R Packages, import and export of data in R, data structures in R, various statistical concepts, cluster analysis and forecasting.
This Data Science with R Training is beneficial for all aspiring data scientists including, IT Professionals or software developers looking to make career switch into analytics, professionals working in data and business analysis, graduates wishing to build a career in analytics, and experienced professionals willing to harness Data Science in their fields.
Prerequisites
There are no specific prerequisites for this Data Science with R Training. If you are a beginner in Data Science, this is one of the best courses to start with.
Key Features
- LIVE Instructor-led Classes
- 24x7 on-demand technical support for assignments, queries, quizzes, project, etc.
- Flexibility to attend the class at your convenient time.
- Server Access to Massive's Tech Management System until you get into your dream carrier.
- A huge database of Interview Questions
- Professional Resume Preparation
- Earn a Skill Certificate
- Enroll today and get the advantage.
Curriculum
- Overview.
- Business Decision and Analytics.
- Types of Business Analytics.
- Application of Business Analytics.
- Data Science Overview.
- 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
- Overview
- Identifying Data Structures.
- Demo Identifying Data Structures.
- Assigning Values to Data Structures.
- Data Manipulation.
- Assigning values and applying functions.
- Overview
- Introduction to Data Visualization.
- Data Visualization using Graphics in R
- Ggplot2
- File Formats of Graphic Outputs
- Overview
- Introduction to Hypothesis
- Types of Hypothesis
- Data Sampling
- Confidence and Significance Levels
- Overview
- Hypothesis Test
- Parametric Test
- Non-Parametric Test
- Hypothesis Tests about Population Means
- Hypothesis Tests about Population Variance
- Hypothesis Tests about Population Proportions
- Overview
- Introduction to Regression Analysis
- Types of Regression Analysis Models
- Liner Regression
- Demo Simple Liner Regression
- Non-Liner Regression
- Demo Regression Analysis with Multiple Variables
- Cross Validation
- Non-Linear to Linear Models
- Principal Components Analysis
- Factor Analysis
- Overview
- Classification and Its Types
- Logistic Regression
- Support Vector Machines
- Demo Support Vector Machines
- K-Nearest Neighbours
- Naïve Bayes Classifier
- Decision Tree Classification
- Demo Decision Tree Classification
- Random Forest Classification
- Evaluating Classifier Models
- Demo K-Fold Cross Validation
- Overview
- Introduction to Clustering
- Clustering Methods
- Demo K-means Clustering
- Demo Hierarchical Clustering
- Overview
- Association Rule
- Apriori Algorithm
- Demo Apriori Algorithm
Have Any Questions?
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