Applied Mathematics: Parametric & Non-Parametric Methods

We invite you to the crucial second session of our Data Science In Action: A Project Based Bootcamp , where we move from statistical thinking to the essential mathematical techniques that drive predictive analytics.

Applied Mathematics: Parametric & Non-Parametric Methods
This session is designed to deepen your analytical foundation, giving you the mathematical clarity needed to select the right statistical tools for any data science problem. Understanding the math behind the methods is the key to building robust and accurate models.

What You Will Master in This Intensive Session
This isn’t just theory; we focus on the core mathematical principles and their practical application in the data science workflow.

Mathematical Foundations: Understand the deep mathematical concepts that underpin both parametric and non-parametric statistical methods.

Method Differentiation: Learn to clearly distinguish between distribution-based (parametric) and distribution-free (non-parametric) approaches using core mathematical logic.

Practical Application of Key Tests: Gain confidence in applying essential mathematical tests correctly, including:

* Parametric Tests: t-test and z-test.
* Non-Parametric Tests: Mann-Whitney U test, Wilcoxon Signed-Rank test, and Chi-square test.

Data-Driven Decision Making: Learn how to mathematically analyze data behavior (e.g., distribution, variance) to precisely determine which method is the most appropriate and ethical for a given data problem.

By mastering the mathematical reasoning behind these tests, you move beyond simply running code and gain the interpretive power of a true data scientist.

Date & Time: 23 Dec, 2025, 3:30 PM CET Via Microsoft Teams
Platform: MS Teams

Prerequisite: Familiarity with concepts covered in the “Statistics for Data Science” session is recommended.

Secure your spot now and build the mathematical expertise required for advanced data science!

Register Now - https://events.teams.microsoft.com/event/a2f0a34e-895f-48bc-baee-66ddcd4893ad@ed31f069-7ac4-4605-b9ce-8587fd96e4cc