Learn By Example: Statistics and Data Science in R

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82 Lessons
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82 Lessons (9h)

  • Introduction
    You, This course and Us2:32
    Top Down vs Bottoms Up : The Google vs McKinsey way of looking at data12:58
    R and RStudio installed5:10
  • The 10 second answer : Descriptive Statistics
    Descriptive Statistics : Mean, Median, Mode10:07
    Our first foray into R : Frequency Distributions6:07
    Draw your first plot : A Histogram3:11
    Computing Mean, Median, Mode in R2:21
    What is IQR (Inter-quartile Range)?8:08
    Box and Whisker Plots3:11
    The Standard Deviation10:24
    Computing IQR and Standard Deviation in R6:06
  • Inferential Statistics
    Drawing inferences from data3:25
    Random Variables are ubiquitous16:54
    The Normal Probability Distribution9:31
    Sampling is like fishing6:14
    Sample Statistics and Sampling Distributions9:25
  • Case studies in Inferential Statistics
    Case Study 1 : Football Players (Estimating Population Mean from a Sample)6:49
    Case Study 2 : Election Polling (Estimating Population Proportion from a Sample)7:51
    Case Study 3 : A Medical Study (Hypothesis Test for the Population Mean)13:53
    Case Study 4 : Employee Behavior (Hypothesis Test for the Population Proportion)9:49
    Case Study 5: A/B Testing (Comparing the means of two populations)17:18
    Case Study 6: Customer Analysis (Comparing the proportions of 2 populations)11:50
  • Diving into R
    Harnessing the power of R7:26
    Assigning Variables8:48
    Printing an output13:03
    Numbers are of type numeric5:25
    Characters and Dates7:30
  • Vectors
    Data Structures are the building blocks of R8:24
    Creating a Vector2:23
    The Mode of a Vector4:18
    Vectors are Atomic2:24
    Doing something with each element of a Vector3:09
    Aggregating Vectors1:28
    Operations between vectors of the same length5:39
    Operations between vectors of different length5:30
    Generating Sequences6:25
    Using conditions with Vectors2:04
    Find the lengths of multiple strings using Vectors2:22
    Generate a complex sequence (using recycling)2:49
    Vector Indexing (using numbers)6:56
    Vector Indexing (using conditions)6:18
    Vector Indexing (using names)2:27
  • Arrays
    Creating an Array11:36
    Indexing an Array7:38
    Operations between 2 Arrays2:09
    Operations between an Array and a Vector2:45
    Outer Products6:23
  • Matrices
    A Matrix is a 2-Dimensional Array7:59
    Creating a Matrix2:00
    Matrix Multiplication2:49
    Merging Matrices2:06
    Solving a set of linear equations2:06
  • Factors
    What is a factor?6:48
    Find the distinct values in a dataset (using factors)1:28
    Replace the levels of a factor2:18
    Aggregate factors with table()1:40
    Aggregate factors with tapply()5:07
  • Lists and Data Frames
    Introducing Lists5:11
    Introducing Data Frames4:28
    Reading Data from files4:52
    Indexing a Data Frame5:38
    Aggregating and Sorting a Data Frame6:28
    Merging Data Frames3:30
  • Regression quantifies relationships between variables
    Introducing Regression12:22
    What is Linear Regression?16:06
    A Regression Case Study : The Capital Asset Pricing Model (CAPM)6:34
  • Linear Regression in Excel
    Linear Regression in Excel : Preparing the data9:53
    Linear Regression in Excel : Using LINEST()16:48
  • Linear Regression in R
    Linear Regression in R : Preparing the data13:05
    Linear Regression in R : lm() and summary()16:04
    Multiple Linear Regression12:16
    Adding Categorical Variables to a linear model7:44
    Robust Regression in R : rlm()3:14
    Parsing Regression Diagnostic Plots12:10
  • Data Visualization in R
    Data Visualization6:23
    The plot() function in R3:42
    Control color palettes with RColorbrewer4:15
    Drawing barplots5:25
    Drawing a heatmap2:52
    Drawing a Scatterplot Matrix3:41
    Plot a line chart with ggplot28:19

Use Real-Life Examples & Case Studies to Understand the R Programming Language


Loonycorn is comprised of four individuals--Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh--who have honed their tech expertises at Google and Flipkart. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.


R is a programming language and software environment for statistical computing and graphics that is widely used among statisticians and data miners for data analysis. In this course, you'll get a thorough run-through of how R works and how it's applied to data science. Before you know it, you'll be crunching numbers like a pro, and be better qualified for many lucrative careers.

  • Access 82 lectures & 9 hours of content 24/7
  • Cover basic statistical principles like mean, median, range, etc.
  • Learn theoretical aspects of statistical concepts
  • Discover datatypes & data structures in R, vectors, arrays, matrices & more
  • Understand Linear Regression
  • Visualize data in R using a variety of charts & graphs
  • Delve into descriptive & inferential statistics
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