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About Course
These are state of art programs designed by Prime Classes – Promoted by a team of Data science practitioners with 15+ years of experience in the IT Industry. These programs are experiential application driven programs for highly motivated working professionals to become data science practitioners.
Curriculum
Introduction to Probability and Statistics for Data Science
This module aims at preparing you for the essential skill of thinking like a statistician. This module will enable you to change your analytical thinking process, and you will begin to start looking at data and numbers from a different perspective. This is a fundamental module and strong concepts in this area will enable you to differentiate yourself as a Data Scientist. This module covers • Probability theory and related algorithms • Descriptive statistical methods • Inferential statistical methods From a tools perspective, you will gain confidence with tools like R and Excel Fundamentals of Probability • Introduction to random variables • Probability theory • Conditional probability • Bayes Theorem The Concept of a data set • Understanding the properties of an attribute: Central tendencies (Mean, Median, Mode); • Measures of spread (Range, Variance, Standard Deviation) • Basics of Probability Distributions; Expectation and Variance of a variable Probability distribution and differences between discrete and continuous distributions • Discrete probability distributions: Binomial, Poisson • Continuous probability distributions: Normal distribution; t-distribution. Procedure for gaining inference about populations from samples. Understand the data attributes, distributions, sample vs population Procedure for statistical testing • Extend the understanding to analyze relationships between variables • How to conduct statistical hypothesis testing and introduction to various methods such as chi-square test, t-test, z-test, F-test and ANOVA • Covariance and Correlation and a Precursor to Regression • Hands-on Implementation in R
