Probability, Random variables and distributions

This course being the first course on Data Science and statistics aspirants. Starts with four lectures  on essential concepts on probability which is a pre-requisite for any statistical or machine learning  study

1. Explains the basic underlying concepts and definitions on probability involving, Chance experiments, Sample Space, Events, Likelihood

Two important theorems on probability namely

2. Conditional probability and

4. Bayes theorem

3. Various properties on Probability

The rest of the section focusses on the subjects pertaining to the title of this course

The second section focuses on

Probability distributions Starts with identifying the difference between a variable and a random variable. Explains discrete and continuous random variables and their characteristics. Move on to explain the need for the probability distributions. explains the basics, characteristics and definitions around the discrete probability distribution and continuous probability distributions. explains the graphical representations of probability distributions involving histograms and continuous functions Every aspect is illustrated with a simple case study to appreciate the details

The third section focusses on

two important discrete probability distributions namely Binomial distribution & Geometric distribution Explains - the conditions to be met for each of these experiments - derivation of mathematical functions that describe these distributions - mean and standard deviation (variance) for each of these distributions - applications of these distributions in certain real world using examples

The forth section explains 

What is a Normal distribution? What is a standard Normal distribution ( z value / z curve )? How are probabilities evaluated for a standard Normal distribution and normal distribution? How to judge if a sample data is Normally distributed? What is a normal probability plot? How to transform data into a normal distribution when the sample is not? How to arrive at probabilities for a discrete probability distribution using normal approximations?