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Taught by a Stanfordeducated, exGoogler and an IIT, IIM  educated exFlipkart lead analyst. This team has decades of practical experience in quant trading, analytics and ecommerce.
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 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
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Section 1: Introduction  

Lecture 1  02:32  
This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples. 

Lecture 2  13:11  
Q. How do companies make decisions? A. Using data We talk about what it takes to go from data to making a decision from data. This sets the agenda for the rest of the course  each of the things on this journey is covered in the upcoming sections 

Lecture 3  05:10  
Get setup with R and Rstudio. All the examples that follow in this course will have source code attached. Download and run them in Rstudio 

Section 2: The 10 second answer : Descriptive Statistics  
Lecture 4  10:07  
Bosses are impatient. They often want you to cut to the chase, and give them an answer that's ok, but in a short amount of time. Descriptive statistics are the first place to start  they are often the 10s answer to any question about the data. 

Lecture 5  06:06  
Computing a frequency distribution using R 

Lecture 6  03:11  
A histogram is a good visual summary of your data. 

Lecture 7  02:21  
Computing the Mean, Median, Mode in R 

Lecture 8  08:08  
The mean, median and mode are point estimates to represent your data. IQR is a measure that explains the spread of the data. 

Lecture 9  03:11  
Visualize the IQR and outliers using box and whisker plots 

Lecture 10  10:24  
The standard deviation measures the spread of a dataset, and it so happens, the standard deviation is actually very profound. 

Lecture 11 
Computing IQR and Standard Deviation in R

06:06  
Section 3: Inferential Statistics  
Lecture 12  03:25  
Drawing inferences from data is key to being able to take decisions using data. There is a science to this, whose foundation is in random variables, probability distributions, and performing tests of statistical significance. 

Lecture 13  16:54  
Random variables are everywhere. Any data that you'll study is a random variable whose behaviour is determined by a probability distribution. 

Lecture 14  09:31  
The Normal Distribution is arguably the most wellknown and commonly seen probability distribution. It is characterized by its probability density function, mean and standard deviation. 

Lecture 15  06:14  
Sampling is a little like fishing. Sampling is crucial to induction  drawing conclusions about something by looking at some evidence. 

Lecture 16  09:25  
A sample is described by sample statistics like the sample mean. The sampling distribution is the probability distribution of sample means. 

Section 4: Case studies in Inferential Statistics  
Lecture 17  06:45  
Find a point estimate for the average weight of all football players using a sample of football players in 1 college team. 

Lecture 18  07:50  
Find a point estimate for the % of voters in favor of a candidate. 

Lecture 19  13:53  
A test of significance is an important step in building support for your findings and inferences. Here is the first example of a test of significance  is the population mean equal to a given value? 

Lecture 20  09:49  
Perform a test of significance to check whether the population % is equal to a certain value 

Lecture 21  17:18  
Perform a test of significance to compare 2 population means. The example used is A/B Testing  which is pretty widely used in internet companies to test out product features. 

Lecture 22  11:50  
Perform a test of significance to compare two population proportions 

Section 5: Diving into R  
Lecture 23  07:26  
The next few sections dive deep into all the data processing, slicing and dicing ability that R provides. The wide variety of R packages available is one reason why R is popular among many data scientists. 

Lecture 24  08:47  
Let's start with the basics. What are variables and how do we assign variables in R? 

Lecture 25  13:03  
print(), show(), message(), cat() are different ways to print something to screen. 

Lecture 26  05:24  
Numbers in R are of type numeric. 

Lecture 27  07:30  
R has builtin datatypes for dates and timestamps. 

Lecture 28  03:24  
Logical is a datatype that is the result of conditional tests in R 

Section 6: Vectors  
Lecture 29  08:24  
The wide variety of builtin data structures are what makes R different from other standard programming languages. These include vectors, arrays, matrices, data frames and lists. 

Lecture 30 
Creating a Vector

02:22  
Lecture 31  04:18  
The mode of a vector is the datatype of all its elements. 

Lecture 32 
Vectors are Atomic

02:24  
Lecture 33 
Doing something with each element of a Vector

03:09  
Lecture 34  01:28  
Finding the sum, product, or mean of a vector 

Lecture 35 
Operations between vectors of the same length

05:39  
Lecture 36 
Operations between vectors of different length

05:30  
Lecture 37  06:25  
Generate sequences using the : operator, rep() and seq() 

Lecture 38 
Using conditions with Vectors

02:04  
Lecture 39 
Find the lengths of multiple strings using Vectors

02:22  
Lecture 40 
Generate a complex sequence (using recycling)

02:49  
Lecture 41  06:56  
Access elements based on their position in the vector. 

Lecture 42  06:18  
Access elements based on whether they pass a conditional test. 

Lecture 43  02:27  
Assign names to the elements of a vector 

Section 7: Arrays  
Lecture 44  11:36  
Creating an array can be done by using a vector and then arranging it along dimensions. 

Lecture 45 
Indexing an Array

07:38  
Lecture 46 
Operations between 2 Arrays

02:09  
Lecture 47 
Operations between an Array and a Vector

02:45  
Lecture 48  06:23  
Outer products are complex operations that operate on every pair of elements from two arrays. 

Section 8: Matrices  
Lecture 49  07:58  
A Matrix is a 2 Dimensional array. But it has special meaning and can be interpreted in a bunch of different ways. 

Lecture 50 
Creating a Matrix

02:00  
Lecture 51 
Matrix Multiplication

02:48  
Lecture 52  02:06  
rbind() and cbind() to merge matrices. 

Lecture 53 
Solving a set of linear equations
Preview

02:06  
Section 9: Factors  
Lecture 54  06:48  
A factor is a special type of vector used to represent categorical variables 

Lecture 55 
Find the distinct values in a dataset (using factors)

01:28  
Lecture 56 
Replace the levels of a factor

02:18  
Lecture 57 
Aggregate factors with table()

01:39  
Lecture 58 
Aggregate factors with tapply()

05:07  
Section 10: Lists and Data Frames  
Lecture 59  05:11  
Lists are fundamentally different from vectors, arrays and matrices  which are all homogenous data structures. 

Lecture 60  04:28  
Data Frames are how R stores data read from files and databases. 

Lecture 61 
Reading Data from files

04:52  
Lecture 62 
Indexing a Data Frame

05:38  
Lecture 63  06:28  
Using the aggregate() and order() functions 

Lecture 64  03:29  
Merge data frames based on one or more common columns 

Section 11: Regression quantifies relationships between variables  
Lecture 65  12:21  
Regression is the process of finding a model that describes the relationship between variables. 

Lecture 66  16:06  
Linear regression is the process of fitting a line or a linear model that best explains the relationship between 2 variables. Understand what residuals are, the ordinary least squares method and RSquared 

Lecture 67  06:34  
The Capital Asset Pricing Model describes a relationship between risk and return. Use it with regression to either find the risk or returns of a given stock. Regression is one of the ways to estimate the Beta in CAPM. 

Section 12: Linear Regression in Excel  
Lecture 68  09:53  
Find the Beta of Google by regressing Google returns against NASDAQ returns. We describe how to find, and prepare the data for fitting a linear model. 

Lecture 69  16:48  
LINEST() is a function in excel that fits a linear model for a given set of variables. However LINEST() has a bunch of issues, including its inability to deal with missing values. 

Section 13: Linear Regression in R  
Lecture 70  13:05  
Find the Beta of Google by regressing Google returns against NASDAQ returns. We describe how process data frames and prepare the data for fitting a linear model. 

Lecture 71  16:04  
lm() is used to build linear models in R. The results of lm() can be parsed using summary(). Building the linear model in R has a bunch of advantages over doing the same in Excel. 

Lecture 72  12:15  
Build a linear model with multiple independent variables : Regress the returns of an oil stock against S&P 500 and the returns of an exchange traded oil fund. 

Lecture 73  07:44  
We describe how categorical variables can be built into a linear model, and how to do this in R specifically 

Lecture 74  03:14  
rlm() helps you build Robust linear models that downweight the influence of outliers. 

Lecture 75  12:10  
lm() returns a bunch of diagnostic plots that are used to validate the assumptions underlying linear regression  QQ plots, Scalelocation and Cook's distance plots 

Section 14: Data Visualization in R  
Lecture 76  06:23  
Data Visualization gives you the power to effectively get your point across and to deeply understand your data. 

Lecture 77 
The plot() function in R

03:42  
Lecture 78 
Control color palettes with RColorbrewer

04:15  
Lecture 79 
Drawing barplots

05:25  
Lecture 80 
Drawing a heatmap

02:52  
Lecture 81 
Drawing a Scatterplot Matrix

03:41  
Lecture 82  08:19  
ggplot2 is a pretty cool R package for complex 2D graphics. Plot the time series of 4 different stocks in the same graph. 
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 Udemy!
We hope you will try our offerings, and think you'll like them :)