Statistics and Data Science in R : Learn By Example

About Course

• This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples. 

  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 analysing and preparing raw data to visualising 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. 

  What's Covered:

  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

Topics covered in this course

• Section 1: Introduction
  	Lecture 1	You, This course and Us	02:32
  	Lecture 2	Top Down vs Bottoms Up : The Google vs McKinsey way of looking at data	13:11
  	Lecture 3	R and RStudio installed	05:10
  Section 2: The 10 second answer : Descriptive Statistics
  	Lecture 4	Descriptive Statistics : Mean, Median, Mode	10:07
  	Lecture 5	Our first foray into R : Frequency Distributions	06:06
  	Lecture 6	Draw your first plot : A Histogram	03:11
  	Lecture 7	Computing Mean, Median, Mode in R	02:21
  	Lecture 8	What is IQR (Inter-quartile Range)?	08:08
  	Lecture 9	Box and Whisker Plots	03:11
  	Lecture 10	The Standard Deviation	10:24
  	Lecture 11	Computing IQR and Standard Deviation in R	06:06
  Section 3: Inferential Statistics
  	Lecture 12	Drawing inferences from data	03:25
  	Lecture 13	Random Variables are ubiquitous	16:54
  	Lecture 14	The Normal Probability Distribution	09:31
  	Lecture 15	Sampling is like fishing	06:14
  	Lecture 16	Sample Statistics and Sampling Distributions	09:25
  Section 4: Case studies in Inferential Statistics
  	Lecture 17	Case Study 1 : Football Players (Estimating Population Mean from a Sample)	06:45
  	Lecture 18	Case Study 2 : Election Polling (Estimating Population Proportion from a Sample)	07:50
  	Lecture 19	Case Study 3 : A Medical Study (Hypothesis Test for the Population Mean)	13:53
  	Lecture 20	Case Study 4 : Employee Behavior (Hypothesis Test for the Population Proportion)	09:49
  	Lecture 21	Case Study 5: A/B Testing (Comparing the means of two populations)	17:18
  	Lecture 22	Case Study 6: Customer Analysis (Comparing the proportions of 2 populations)	11:50
  Section 5: Diving into R
  	Lecture 23	Harnessing the power of R	07:26
  	Lecture 24	Assigning Variables	08:47
  	Lecture 25	Printing an output	13:03
  	Lecture 26	Numbers are of type numeric	05:24
  	Lecture 27	Characters and Dates	07:30
  	Lecture 28	Logicals	03:24
  Section 6: Vectors
  	Lecture 29	Data Structures are the building blocks of R	08:24
  	Lecture 30	Creating a Vector	02:22
  	Lecture 31	The Mode of a Vector	04:18
  	Lecture 32	Vectors are Atomic	02:24
  	Lecture 33	Doing something with each element of a Vector	03:09
  	Lecture 34	Aggregating Vectors	01:28
  	Lecture 35	Operations between vectors of the same length	05:39
  	Lecture 36	Operations between vectors of different length	05:30
  	Lecture 37	Generating Sequences	06:25
  	Lecture 38	Using conditions with Vectors	02:04
  	Lecture 39	Find the lengths of multiple strings using Vectors	02:22
  	Lecture 40	Generate a complex sequence (using recycling)	02:49
  	Lecture 41	Vector Indexing (using numbers)	06:56
  	Lecture 42	Vector Indexing (using conditions)	06:18
  	Lecture 43	Vector Indexing (using names)	02:27
  Section 7: Arrays
  	Lecture 44	Creating an Array	11:36
  	Lecture 45	Indexing an Array	07:38
  	Lecture 46	Operations between 2 Arrays	02:09
  	Lecture 47	Operations between an Array and a Vector	02:45
  	Lecture 48	Outer Products	06:23
  Section 8: Matrices
  	Lecture 49	A Matrix is a 2-Dimensional Array	07:58
  	Lecture 50	Creating a Matrix	02:00
  	Lecture 51	Matrix Multiplication	02:48
  	Lecture 52	Merging Matrices	02:06
  	Lecture 53	Solving a set of linear equations Preview	02:06
  Section 9: Factors
  	Lecture 54	What is a factor?	06:48
  	Lecture 55	Find the distinct values in a dataset (using factors)	01:28
  	Lecture 56	Replace the levels of a factor	02:18
  	Lecture 57	Aggregate factors with table()	01:39
  	Lecture 58	Aggregate factors with tapply()	05:07
  Section 10: Lists and Data Frames
  	Lecture 59	Introducing Lists	05:11
  	Lecture 60	Introducing Data Frames	04:28
  	Lecture 61	Reading Data from files	04:52
  	Lecture 62	Indexing a Data Frame	05:38
  	Lecture 63	Aggregating and Sorting a Data Frame	06:28
  	Lecture 64	Merging Data Frames	03:29
  Section 11: Regression quantifies relationships between variables
  	Lecture 65	Introducing Regression	12:21
  	Lecture 66	What is Linear Regression?	16:06
  	Lecture 67	A Regression Case Study : The Capital Asset Pricing Model (CAPM)	06:34
  Section 12: Linear Regression in Excel
  	Lecture 68	Linear Regression in Excel : Preparing the data	09:53
  	Lecture 69	Linear Regression in Excel : Using LINEST()	16:48
  Section 13: Linear Regression in R
  	Lecture 70	Linear Regression in R : Preparing the data	13:05
  	Lecture 71	Linear Regression in R : lm() and summary()	16:04
  	Lecture 72	Multiple Linear Regression	12:15
  	Lecture 73	Adding Categorical Variables to a linear model	07:44
  	Lecture 74	Robust Regression in R : rlm()	03:14
  	Lecture 75	Parsing Regression Diagnostic Plots	12:10
  Section 14: Data Visualization in R
  	Lecture 76	Data Visualization	06:23
  	Lecture 77	The plot() function in R	03:42
  	Lecture 78	Control color palettes with RColorbrewer	04:15
  	Lecture 79	Drawing barplots	05:25
  	Lecture 80	Drawing a heatmap	02:52
  	Lecture 81	Drawing a Scatterplot Matrix	03:41
  	Lecture 82	Plot a line chart with ggplot2	08:19

What you will get from this course?

• • 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
  • No prerequisites : We start from basics and cover everything you need to know. We will be installing R and RStudio as part of the course and using it for most of the examples. Excel is used for one of the examples and basic knowledge of excel is assumed.

Who should buy this course?

• • MBA graduates or business professionals who are looking to move to a heavily quantitative role
  • Engineers who want to understand basic statistics and lay a foundation for a career in Data Science
  • Analytics professionals who have mostly worked in Descriptive analytics and want to make the shift to being modelers or data scientists
  • Folks who've worked mostly with tools like Excel and want to learn how to use R for statistical analysis