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Learn By Example: Statistics and Data Science in R

Video Description

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

About This Video

  • 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

In Detail

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.

Table of Contents

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