Master the statistical concepts that power modern data science using Python. This course teaches you how to analyze, interpret, and present data by combining statistical theory with practical implementation using Pandas and NumPy. You will build a strong foundation in descriptive statistics, probability, hypothesis testing, and regression while applying these concepts to structured datasets in Python.

You will begin by exploring the fundamentals of data science, learning how to summarize datasets with measures of central tendency, dispersion, correlation, and visualizations such as histograms. Next, you will develop statistical reasoning through probability, event analysis, summation techniques, and hypothesis testing by interpreting p-values, test statistics, and error types. Finally, you will build and evaluate regression models, analyze residuals, interpret coefficients, and apply curve-fitting techniques to support predictive analysis. Designed for learners who want to strengthen their data science and statistical analysis skills, this course emphasizes hands-on learning by integrating Python, Pandas, and NumPy with core statistical methods. By the end of the course, you will be able to summarize and visualize data, evaluate statistical evidence, build regression models, and apply statistical thinking to real-world data science projects, preparing you for advanced analytics, machine learning, and data-driven decision-making.