Statistics, Probability & EDA for Data Science using Python

Statistics and Probability:Statistics and Probability are essential pillars in Data Science and Machine Learning, providing the foundational tools needed for data analysis and interpretation. Our course is designed to give you a deep understanding of these crucial concepts through practical, hands-on learning. Throughout the course, we teach each concept in Statistics and Probability by working through real-world examples and implementing them using Python code.

You'll explore Descriptive and Inferential Statistics, including measures of central tendencies, measures of dispersion, and various statistical methods and variables, all illustrated with practical examples. In Probability, you'll learn about random variables, Probability distributions, probability density functions, and cumulative distribution functions, each concept reinforced with Python coding exercises to solidify your understanding. By learning Statistics and Probability through practical examples and Python code, you'll not only grasp these critical concepts but also gain the confidence to apply them in real-world scenarios.

This approach ensures that you are well-prepared to tackle advanced Data Science and Machine Learning challenges, making you proficient in these key areas and setting you up for success in the data-driven world. Please find the brief Syllabus to the Course. Statistics and Probability: 1.

Introduction to StatisticsIntroduction to Statistics, Types of statistics, Descriptive Statistics and its attributes, Limitations of descriptive statisticsStatistics and Probability: 2. Introduction to Inferential StatisticsInferential Statistics and its attributes, Two ways to use inferential statistics, types of variables and their statistical methods. Statistics and Probability: 3.