Introduction to Statistical Analysis for Data Science | AIML End-to-End Session 32

Ready to dive deep into the world of
Artificial Intelligence
Machine Learning (AIML)?

Welcome to Session 32 of our End-to-End AIML series! In this session, we introduce Statistical Analysis, a fundamental aspect of data science that helps us draw meaningful insights from data. Understanding statistics is crucial for developing robust AI/ML models and making data-driven decisions.

What You'll Learn:

Introduction to Statistical Analysis: Understand the importance of statistics in data science and how it provides the foundation for AI/ML model development.
Descriptive vs. Inferential Statistics: Learn the difference between descriptive statistics (summarizing data) and inferential statistics (drawing conclusions from data).
Key Statistical Concepts: Explore concepts like mean, median, mode, variance, standard deviation, correlation, and regression analysis.
Probability Theory: Get an introduction to probability, including distributions like normal, binomial, and Poisson, which are essential in data science.
Hypothesis Testing: Learn the basics of hypothesis testing and how to apply it to data analysis, including concepts like p-values, t-tests, and confidence intervals.
Hands-On Coding: Follow along with practical coding exercises to implement statistical techniques using Python libraries such as Pandas and SciPy.
Whether you're new to statistics or looking to refine your skills, this session will provide you with a solid understanding of how statistical analysis supports data science and AI/ML projects.

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