single-course
Statistic Advance for Data Science

Statistic Advance for Data Science

Award

This course includes certification from TATA STEEL

About course

The Statistic Advance for Data Science course delves deeper into statistical concepts essential for data analysis. Participants explore the Central Limit Theorem, hypothesis testing, formulation of null and alternate hypotheses, and various testing approaches including critical value and p-value methods. The curriculum covers one-tailed and two-tailed tests, along with the advantages of the p-value approach. Learners also examine Type-I and Type-II errors, understanding their real-life consequences. Through practical examples and exercises, participants gain proficiency in advanced statistical analysis, enabling them to make informed decisions based on data insights in diverse domains.

Course Objective

By the end of this Course, You will be able to understand:

To learn Statistics that provides the means and tools to find structure in data. To find deeper insight into what truths your data is showing.

Course Curriculum

By the end of this Course, You will be able to understand:

  • Central Limit Theorem
  • Hypothesis Testing
  • Formulate Null and Alternate hypothesis
  • Critical Value approach
  • One-Tailed test
  • Two-Test test
  • P-value approach
  • Advantage of the P-Value approach
  • Type-I and Type-II error
  • Real-life consequences of both these errors