Mathematical Statistics for Data Science

🎉 Mathematical Statistics for Data Science - Ex-Google Data Scientist's Guide 📚

Course Description: Dive deep into the world of Mathematical Statistics with our comprehensive course designed by Brian Grecano, an experienced data scientist from Google. This course is your gateway to mastering estimation methods like the Method of Moments and Maximum Likelihood Estimation (MLE), understanding the properties of estimators through bias, variance, and efficiency, and exploring advanced topics in asymptotic statistics such as the Central Limit Theorem and Confidence Intervals.

Course Highlights:

• 57 Video Lectures: Engage with innovative lightboard technology to bring the lessons to life in a dynamic and interactive way.
• Lecture Notes: Comprehensive notes for each lesson that emphasize key vocabulary, examples, and explanations to complement your video learning experience.
• Practice Problems: Test your understanding with end-of-chapter problems designed to reinforce the concepts learned throughout the course.

Key Topics Covered:

1. Fundamental Probability Distributions: Understand the basics of Bernoulli, Uniform, and Normal distributions.
2. Expected Value: Explore the connection between expected value and sample mean.
3. Method of Moments: Learn how to develop estimators using this powerful method.
4. Expected Value of Estimators and Unbiased Estimators: Gain insights into the importance of these concepts in statistical inference.
5. Variance of Random Variables and Estimators: Master the concept of variance and its role in assessing the performance of estimators.
6. Fisher Information and Cramer-Rao Lower Bound: Dive into the theory behind these fundamental concepts for determining the accuracy of estimators.
7. Central Limit Theorem: Discover how this theorem allows us to approximate the distribution of sample means.
8. Confidence Intervals: Learn how to construct and interpret confidence intervals in statistical analysis.

Who This Course Is For:

• Aspiring Statisticians: Students with prior introductory statistics experience looking to deepen their understanding of mathematical foundations.
• Data Science Professionals: Those aiming to enhance their statistical knowledge, either for professional growth or to ace job interviews.
• Analytical Enthusiasts: Anyone interested in developing a statistical mindset and honing their analytical skills for real-world applications.

Pre-requisites: To fully benefit from this course, you should have a solid understanding of high school algebra and equation manipulation with variables. Some chapters may utilize introductory calculus concepts such as differentiation and integration. However, even without prior calculus knowledge, those with strong math skills can follow along and only miss minor mathematical details.

Join us on this statistical adventure and unlock the secrets of Mathematical Statistics for Data Science! 🔍🚀