# Learn By Example: Statistics and Data Science in R [Video]

 Learn Harness R and R packages to read, process and visualize data Understand linear regression and use it confidently to build models Understand the intricacies of all the different data structures in R Use Linear regression in R to overcome the difficulties of LINEST() in Excel Draw inferences from data and support them using tests of significance Use descriptive statistics to perform a quick study of some data and present results This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples. Let’s parse that. Gentle, yet thorough: This course does not require a prior quantitative or mathematics background. It starts by 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. Data Science, Statistics and R: 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. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context. Style and Approach With no prerequisites to the course, this is the hands-on course for Statistics and Data Science. 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 Data Visualization in R: Line plot, Scatter plot, Bar plot, Histogram, Scatterplot matrix, Heat map, Packages for Data Visualisation : Rcolorbrewer, ggplot2 Descriptive Statistics: Mean, Median, Mode, IQR, Standard Deviation, Frequency Distributions, Histograms, Boxplots Inferential Statistics: Random Variables, Probability Distributions, Uniform Distribution, Normal Distribution, Sampling, Sampling Distribution, Hypothesis testing, Test statistic, Test of significance 9 hours 7 minutes 9781788996877 20 Dec 2017
 Harnessing the power of R Assigning Variables Printing an output Numbers are of type numeric Characters and Dates Logicals
 Introducing Lists Introducing Data Frames Reading Data from files Indexing a Data Frame Aggregating and Sorting a Data Frame Merging Data Frames