Certainly! Probability and Statistics for Data Science is a course designed to equip students with the foundational knowledge and skills necessary to understand and analyze data effectively. In this course, students will learn about the fundamental concepts of probability theory, which provides the framework for quantifying uncertainty and making informed decisions in the presence of randomness. Additionally, students will delve into statistical methods and techniques used for data analysis, including descriptive statistics, inferential statistics, hypothesis testing, and regression analysis. The course will also cover practical applications of probability and statistics in various domains, such as machine learning, data mining, and predictive analytics. By the end of the course, students will have a solid understanding of how probability and statistics contribute to the field of data science and will be equipped with the tools to apply these concepts to real-world data analysis tasks.
Live meet class: https://meet.google.com/zxj-uhtf-dxh?authuser=0
Curriculum
- 3 Sections
- 14 Lessons
- 0 Quizzes
- 1h Duration
Introduction
- Introduction to Probability and Statistics for Data science
- Discussion of Random Variable
- Continuous and Discrete Random Variable
- Understanding the Probabilities
- Types of Distributions
- Binomial, Spearman, Cosine
Statastics
- Mean, Median and Mode
- Standard, Deviation, and Variance
- Similarity measure
- Pearson, Cosine and Spearman
Hypothesis
- Understanding the Hypothesis Testing
- What is the P value
- Types of Testing
- T-Test, Paired T-Test, F-Test, Z-Test
