Master Complete Statistics For Computer Science - II
What you'll learn
- Binomial Distribution
- Poisson Distribution
- Geometric Distribution
- Hypergeometric Distribution
- Uniform or Rectangular Distribution
- Exponential or Negative Exponential Distribution
- Erlang or General Gamma Distribution
- Weibull Distribution
- Normal or Gaussian Distribution
- Central Limit Theorem
- Hypotheses Testing
- Large Sample Test - Tests of Significance for Large Samples
- Small Sample Test - Tests of Significance for Small Samples
- Chi - Square Test - Test of Goodness of Fit
- Knowledge of Applied Probability
- Knowledge of Master Complete Statistics For Computer Science - I
- Knowledge of Calculus
As it turns out, there are some specific distributions that are used over and over in practice for e.g. Normal Distribution, Binomial Distribution, Poisson Distribution, Exponential Distribution etc.
There is a random experiment behind each of these distributions. Since these random experiments model a lot of real life phenomenon, these special distribution are used in different applications like Machine Learning, Neural Network, Data Science etc.
That is why they have been given a special names and we devote a course "Master Complete Statistics For Computer Science - II" to study them.
After learning about special probability distribution, the second half of this course is devoted for data analysis through inferential statistics which is also referred to as statistical inference.
Technically speaking, the methods of statistical inference help in generalizing the results of a sample to the entire population from which the sample is drawn.
This 150+ lecture course includes video explanations of everything from Special Probability Distributions and Sampling Distribution, and it includes more than 85+ examples (with detailed solutions) to help you test your understanding along the way. "Master Complete Statistics For Computer Science - II" is organized into the following sections:
Uniform or Rectangular Distribution
Exponential or Negative Exponential Distribution
Erlang or General Gamma Distribution
Normal or Gaussian Distribution
Central Limit Theorem
Large Sample Test - Tests of Significance for Large Samples
Small Sample Test - Tests of Significance for Small Samples
Chi - Square Test - Test of Goodness of Fit
Who this course is for:
- Current Probability and Statistics students
- Students of Machine Learning, Data Science, Computer Science, Electrical Engineering , as Statistics is the prerequisite course to Machine Learning, Data Science, Computer Science and Electrical Engineering
- Anyone who wants to study Statistics for fun after being away from school for a while.
Hello students, I am Shilank Singh. I was born and raised in Mumbai, India. My interest in maths developed at an early age when I started attending pre-school. I attended Indian Institute of Technology, Delhi and post graduated in 2013 with a degree M.Sc. in Mathematics. In the fall of 2013, I was hired to teach Engineering Mathematics at Mumbai University and I have taught there for three years. Currently, I live in New Delhi and work as a Maths and Statistics Tutor over here since January, 2017.