Data Science

This Data Science online training with R programming is beneficial for all aspiring data scientists including, IT professionals or software developers looking to make a 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.

Course Introduction:

Lesson 01 – Introduction to Business Analytics

  • Overview
  • Business Decisions and Analytics
  • Types of Business Analytics
  • Applications of Business Analytics
  • Data Science

Lesson 02 – Introduction to R Programming

  • Overview
  • Importance of R
  • Data Types and Variables in R
  • Operators in R04:39
  • Conditional Statements in R
  • Loops in R
  • R script
  • Functions in

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
  • ggplot
  • File Formats of Graphic Outputs

Lesson 05 – Statistics for Data Science-I

  • Introduction to Hypothesis
  • Types of Hypothesis
  • Data Sampling
  • Confidence and Significance Levels

Lesson 06 – Statistics for Data Science-II

  • 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 Variables13:29
  • 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 ModelsDemo 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 Algorithm1
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