This Statistics and Data Analysis course provides a solid foundation in statistical thinking and data interpretation. It is designed for beginners who want to understand how data is analyzed, interpreted, and used for decision-making in real-world scenarios such as data science, machine learning, and business analytics.

The course starts with an introduction to statistics and core concepts of data analysis. Learners then explore population and sampling methods, including random sampling techniques and how samples are used to estimate population behavior. It also covers important concepts such as expected value, variance, and sampling distributions.

In addition, the course explains the Central Limit Theorem and how normal approximation is used in statistical analysis. Students will learn about confidence intervals, which help estimate unknown parameters with a degree of certainty.

The course also introduces hypothesis testing, showing how to evaluate assumptions using statistical evidence. Practical examples are included to help learners understand how to apply statistical methods in real datasets.

By the end of this course, learners will have a strong understanding of probability, sampling, and statistical inference. This course is ideal for beginners, data analysts, students, and anyone interested in data science, analytics, or quantitative research.