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.
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.
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
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
Section 1 - Introduction
You, This course and Us
Top Down vs Bottoms Up : The Google vs McKinsey way of looking at data
R and RStudio installed
Downloads for Sec 1
Section 2 - The 10 second answer : Descriptive Statistics
Descriptive Statistics : Mean, Median, Mode
Our first foray into R : Frequency Distributions
Draw your first plot : A Histogram
Computing Mean, Median, Mode in R
What is IQR (Inter-quartile Range)?
Box and Whisker Plots
The Standard Deviation
Computing IQR and Standard Deviation in R
Download for sec 2
Section 3 - Inferential Statistics
Drawing inferences from data
Random Variables are ubiquitous
The Normal Probability Distribution
Sampling is like fishing
Sample Statistics and Sampling Distributions
Downloads for Sec 3
Section 4 - Case studies in Inferential Statistics
Case Study 1 : Football Players (Estimating Population Mean from a Sample)
Case Study 2 : Election Polling (Estimating Population Proportion from a Sample)
Case Study 3 : A Medical Study (Hypothesis Test for the Population Mean)
Case Study 4 : Employee Behavior (Hypothesis Test for the Population Proportion)
Case Study 5: A/B Testing (Comparing the means of two populations)
Case Study 6: Customer Analysis (Comparing the proportions of 2 populations)
Downloads for Sec 4
Section 5 - Diving into R
Harnessing the power of R
Printing an output
Numbers are of type numeric
Characters and Dates
Downloads for Sec 5
Section 6 - Vectors
Data Structures are the building blocks of R
Creating a Vector
The Mode of a Vector
Vectors are Atomic
Doing something with each element of a Vector
Operations between vectors of the same length
Operations between vectors of different length
Using conditions with Vectors
Find the lengths of multiple strings using Vectors
Generate a complex sequence (using recycling)
Vector Indexing (using numbers)
Vector Indexing (using conditions)
Vector Indexing (using names)
Downloads for Sec 6
Section 7 - Arrays
Creating an Array
Indexing an Array
Operations between 2 Arrays
Operations between an Array and a Vector
Downloads for Sec 7
Section 8 - Matrices
A Matrix is a 2-Dimensional Array
Creating a Matrix
Solving a set of linear equations
Downloads for Sec 8
Section 9 - Factors
What is a factor?
Find the distinct values in a dataset (using factors)
Replace the levels of a factor
Aggregate factors with table()
Aggregate factors with tapply()
Downloads for Sec 9
Section 10 - Lists and Data Frames
Introducing Data Frames
Reading Data from files
Indexing a Data Frame
Aggregating and Sorting a Data Frame
Merging Data Frames
Downloads for Sec 10
Section 11 - Regression quantifies relationships between variables
What is Linear Regression?
A Regression Case Study : The Capital Asset Pricing Model (CAPM)
Downloads for Sec 11
Section 12 - Linear Regression in Excel
Linear Regression in Excel : Preparing the data
Linear Regression in Excel : Using LINEST()
Downloads for Sec 12
Section 13 - Linear Regression in R
Linear Regression in R : Preparing the data
Linear Regression in R : lm() and summary()
Multiple Linear Regression
Adding Categorical Variables to a linear model
Robust Regression in R : rlm()
Parsing Regression Diagnostic Plots
Downloads for Sec 13
Section 14 - Data Visualization in R
The plot() function in R
Control color palettes with RColorbrewer
Drawing a heatmap
Drawing a Scatterplot Matrix
Plot a line chart with ggplot2
Downloads for Sec 14
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 :-)
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.
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.
posted 5 month before
It's good course for learning data science using R. Each concept explained in nice way.
posted 2 month before
Very good course for Beginners. each topic explained very detailed.