R programming with Statistics for Data science : Learn Hands-On

 This course does not require a prior quantitative or mathematics background. It starts fundamental concepts of R programming, introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analyzing and preparing raw data to visualizing your findings. This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R. Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary. Course material in the form for articles include in this program Data Analysis with R: Datatypes and Data structures in R, Vectors, Arrays, Matrices, Lists, Data Frames, Reading data from files, Aggregating, Sorting & Merging Data Frames. Linear Regression: Regression, Simple Linear Regression in Excel, Simple Linear Regression in R, Multiple Linear Regression in R, Categorical variables in regression, Robust regression, Parsing regression diagnostic plots Descriptive Statistics: Mean, Median, Mode, IQR, Standard Deviation, Frequency Distributions, Histograms, Boxplots Inferential Statistics: Hypothesis testing, Test statistic, Test of significance.