This course offers a comprehensive exploration of statistical analysis tailored for data analysts. Participants will delve into key principles and methodologies essential for hypothesis testing and inference in data analysis. Through structured modules, participants will cover various topics including descriptive statistics and inference basics, hypothesis testing principles, one-sample and two-sample tests, chi-square tests and contingency analysis, analysis of variance (ANOVA), regression analysis and correlation, non-parametric tests, and Bayesian data analysis. Additionally, participants will gain insights into future trends shaping statistical analysis for data analysts.

Who This Course Is For:

This course is ideal for data analysts, statisticians, researchers, and professionals in fields such as data science, business analytics, market research, and academia who seek to deepen their understanding of statistical analysis techniques for hypothesis testing and inference. It is suitable for individuals looking to enhance their analytical skills and proficiency in interpreting and drawing insights from data. Additionally, professionals involved in decision-making processes based on data-driven insights will find this course valuable for honing their statistical analysis capabilities.