Regression, Data Mining, Text Mining, Forecasting using R

• Learn about the basic statistics, including measures of central tendency, dispersion, skewness, kurtosis, graphical representation, probability, probability distribution, etc.
• Learn about scatter diagram, correlation coefficient, confidence interval, Z distribution & t distribution, which are all required for Linear Regression understanding
• Learn about the usage of R for building Linear Regression
• Learn about the K-Means clustering algorithm & how to use R to accomplish this
• Learn about the science behind text mining, word cloud & sentiment analysis & accomplish the same using R

Data Science using Ris designed to cover majority of the capabilities of Rfrom Analytics & Data Science perspective, which includes the following:

• Learn about the basic statistics, including measures of central tendency, dispersion, skewness, kurtosis, graphical representation, probability, probability distribution, etc.
• Learn about scatter diagram, correlation coefficient, confidence interval, Z distribution & t distribution, which are all required for Linear Regression understanding
• Learn about the usage of Rfor buildingRegression models

• Learn about the K-Means clustering algorithm & how to use Rto accomplish the same
• Learn about the science behind text mining, word cloud,sentiment analysis & accomplish the same using R
• Learn about Forecasting models including AR, MA, ES, ARMA, ARIMA, etc., and how to accomplish the same using R
• Learn about Logistic Regression & how to accomplish the same using R