Essentials Of Data Science With R Software - 1: Probability And Statistical Inference

Any data analysis is incomplete without statistics. After getting the data, the statistical tools aims to extract the information hidden inside the data. The main objective of statistics is to work on a small sample of data but provide conclusions for the whole population. Such results cannot be obtained without learning the concepts and tools of theory of probability and statistical inference. With the advent of data science, it has become important to learn those tools from computational and data based aspects. Without learning the basic fundamentals of probability theory and statistical inference, it is difficult to implement them correctly on the data and draw correct statistical conclusions. Such fundamental topics have enormous applicability in data science and are to be learnt from data based computational perspectives through software. How to use them with the popular and freely available R statistical software and how to understand the correct statistical inferences is the objective of the course to be taught.INTENDED AUDIENCE :UG students of Science and Engineering. Students of humanities with basic mathematical and statistical background can also do it. Working professionals in analytics can also do it.PREREQUISITES : “Introduction to R Course” is preferred. Mathematics background up to class 12 is needed. Some minor statistics background is desirable.INDUSTRIES SUPPORT :All industries having R & D set up will use this course.

Week 1:Introduction to data science, basic calculations with R Software and probability theory Week 2:Probability theory and random variablesWeek 3:Random variables and Discrete probability distributionsWeek 4:Continuous probability distributions
Week 5:Sampling distributions and Functions of random variablesWeek 6:Convergence of random variables, Central limit theorems and Law of large numbersWeek 7:Statistical inference and point estimationWeek 8:Methods of point estimation of parameters
Week 9:Point and confidence interval estimationWeek 10:Confidence interval estimation and test of hypothesisWeek 11:Test of hypothesisWeek 12:Test of hypothesis for attributes and other tests