Currency:

## Why this course ?

• The R programming language leans more frequently to the cutting edge of data science, giving businesses the latest data analysis tools.
• Students or professionals who want to make a career progression in data science should learn R through a comprehensive training .
• Demand for data scientists is greatly increasing. Demand for R developers will no doubt be on the rise.
• R language is the magic wand for data scientists to handle and analyse huge amounts of complex and unstructured data productively.

Data Science with R

Program Duration
and Fees # Duration

54 Hours # Price

N/A

#### Features

• ###### Advantages of attending the training
Join the classroom/online training.
###### Led by an expert instructor
Led by an expert instructor who can virtually look over your shoulder

#### 1. Introduction To Analytics and Basic Statistics

• Types of Analytics
• Properties of Measurements
• Scales of Measurement
• Types of Data
• Measures of Central Tendency
• Measures of Dispersion
• Measures of Location
• Presentation of Data
• Skewness and Kurtosis

#### 2. Introduction to Probability Theory

• Three Approaches towards Probability
• Concept of a Random Variable
• Probability Mass Function
• Probability Density Function
• Expectation of A Random Variable
• Probability Distributions

#### 3. Sampling Theory And Estimation

• Concept of population and sample
• Techniques of Sampling
• Sampling Distributions

#### 4. Theory of Estimation

• Concept of estimation
• Different types of Estimation

#### 5. Testing of hypothesis

• Concept of hypothesis
• Null hypothesis
• Alternative hypothesis
• Type-I error
• Type-II error
• Level of Significance
• Confidence Interval
• Parametric Tests and Non Parametric Tests
• One Sample T test
• Two independent sample T test
• Paired Sample T test
• Chi square Test for Independence of Attributes.

• One Way Anova
• Two Way Anova

#### 7. Exploratory Factor Analysis

• Principal Component Analysis
• Estimating the Initial Communalities
• Eigen Values and Eigen Vectors
• Correlation Matrix check and KMO-MSA check
• Diagrammatic Representation of Factors

#### 8. Cluster Analysis

• Types of Clusters
• Ward’s Minimum Variance Criteria
• Semi-Partial R-Square and R-Square
• Diagrammatic Representation of clusters
• Problems of Cluster Analysis

#### 9. Linear Regression and Multiple Linear Regression

• Concept of Regression and features of Linear line.
• Assumptions of Classical Linear Model
• Method of Least Squares
• Understanding the Goodness of Fit
• Test of Significance of The Estimated Parameters
• Multiple linear Regression with their Assumptions
• Concept of Multocollinearity
• Signs of Multicollinearity
• The Idea Of Autocorrelation

#### 10. Logistic Regression

• Concept and Applications of Logistic Regression
• Principles Behind Logistic Regression
• Comparison between Linear probability Model and Logistic Regression
• Mathematical Concepts related to Logistic Regression
• Concordant Pairs, Discordant Pairs and Tied Pairs
• Classification Table
• Graphical Representation Related to logistic Regression.

#### 11. Time Series Analysis

• Concept of Time Series and its Applications
• Assumptions of Time Series Analysis
• Components of Time Series
• Smoothening techniques
• Stationarity
• Random Walk
• ARIMA Forecasting
• Box Jenkins Technology
• Merits and Demerits of BJ Technology

• Support
• Confidence
• List

#### 14. Text Mining Analysis

• Processing Huge text file
• Stopwords
• Sentiment score

## Customers who bought this course also bought

×

#### Checkout

Course Name: R programming