Statistics and Mathematics for Data Science and Data Analytics

In this 11 hr course, you will delve into core statistics and probability concepts essential for data science and analytics. Covering topics such as descriptive statistics, probability distributions, hypothesis testing, and regression analysis, this course is designed to provide a solid foundation in analytical concepts, both theoretical and practical.

What I will be able to do after this course

• Understand descriptive statistics concepts including means, medians, variances, standard deviations.
• Master basic probability concepts, theorems, and distributions like normal and Poisson.
• Perform hypothesis testing, including understanding p-values, errors, and confidence intervals.
• Analyze regression methods, including linear regression, MSE, and RMSE metrics.
• Implement machine learning concepts such as decision trees and logistic regression.

Course Instructor(s)

Nikolai Schuler brings years of experience as both a mathematician and data science expert, ensuring he simplifies complex statistics topics in an approachable manner. His practical teaching style emphasizes real-world examples, helping learners relate theory to actual analytics problems. Nikolai is dedicated to making statistics accessible and actionable.

Who is it for?

This course is ideal for beginners aiming to establish a foundation in statistics and probability for use in data science and analytics. It is designed for those with no prior experience looking to start a career or enhance their analytical skills. The course offers practical knowledge beneficial for students, professionals, and enthusiasts eager to expand their data expertise.