Learn By Example: Statistics and Data Science in R

Learn By Example: Statistics and Data Science in R

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 9h 07 | 2.52 GB

A gentle yet thorough introduction to Data Science, Statistics and R using real life examples

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.

With no prerequisites to the course, this is the hands-on course for Statistics and Data Science.

What You Will 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
Table of Contents

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