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.

Letâ€™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

Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the 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

- 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.

- 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

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

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

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

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.

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

What is IQR (Inter-quartile Range)?

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

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.

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

The Normal Distribution is arguably the most well-known and commonly seen probability distribution. It is characterized by its probability density function, mean and standard deviation.

Sampling is a little like fishing. Sampling is crucial to induction - drawing conclusions about something by looking at some evidence.

A sample is described by sample statistics like the sample mean. The sampling distribution is the probability distribution of sample means.

Find a point estimate for the average weight of all football players using a sample of football players in 1 college team.

Find a point estimate for the % of voters in favor of a candidate.

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?

Perform a test of significance to check whether the population % is equal to a certain value

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.

Perform a test of significance to compare two population proportions

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.

Let's start with the basics. What are variables and how do we assign variables in R?

print(), show(), message(), cat() are different ways to print something to screen.

The wide variety of built-in data structures are what makes R different from other standard programming languages. These include vectors, arrays, matrices, data frames and lists.

Find the lengths of multiple strings using Vectors

Creating an array can be done by using a vector and then arranging it along dimensions.

Outer products are complex operations that operate on every pair of elements from two arrays.

A Matrix is a 2 Dimensional array. But it has special meaning and can be interpreted in a bunch of different ways.

Find the distinct values in a dataset (using factors)

Lists are fundamentally different from vectors, arrays and matrices - which are all homogenous data structures.

Regression is the process of finding a model that describes the relationship between variables.

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 R-Squared

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.

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.

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.

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.

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.

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.

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

rlm() helps you build Robust linear models that downweight the influence of outliers.

lm() returns a bunch of diagnostic plots that are used to validate the assumptions underlying linear regression - Q-Q plots, Scale-location and Cook's distance plots

Data Visualization gives you the power to effectively get your point across and to deeply understand your data.

ggplot2 is a pretty cool R package for complex 2D graphics. Plot the time series of 4 different stocks in the same graph.