Learn By Example: Statistics and Data Science in R

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Course Summary :

Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. 

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 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 RLine 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.

What am I going to 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


Pre-Requisites :

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.

Target Audience :

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

Curriculum :

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


Instructor :

Loonycorn A 4-ppl team;ex-Google.


Loonycorn is us, Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh. Between the four of us, we have studied at Stanford, IIM Ahmedabad, the IITs and have spent years (decades, actually) working in tech, in the Bay Area, New York, Singapore and Bangalore. Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum Navdeep: longtime Flipkart employee too, and IIT Guwahati alum We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Unanth! We hope you will try our offerings, and think you'll like them :-)


Average Rating
 (6 Reviews)


samim anvar

posted 11 month before

Easy to Understand

I enjoyed this course, learned statistics along with R. I liked the teaching style of explaining statistical concept and then showing how to calculate it using R. This also have awesome course material. I like "loony Corn" courses and purchased their other courses.


Jeetender Kumar

posted 6 month before

Very Irritating Background Music in videos

I like the course but there is very irritating and disturbing background music in every video .



posted 6 month before

I have enjoyed this course. For me, it was a great refreshing of statistics and R. They explain a statistical concept and then show you how to calculate it using R. I feel that the course material is great and the teaching style is very enjoyable.


Anjali Mehta

posted 5 month before

Good course

It's good course for learning data science using R. Each concept explained in nice way.


Sriramachandra Gangireddy

posted 2 month before

Very good

Very good course for Beginners. each topic explained very detailed.


venkatesh ks

posted 2 month before

stats videos are good, still to finish R