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Statistics for Data Science with Python

EDUCBA via Coursera

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Overview

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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.

Syllabus

Introduction to Data Science and Descriptive Statistics

This module introduces learners to the foundations of data science and statistics. It covers essential concepts such as measures of central tendency, dispersion, and correlation, while also demonstrating how to represent data visually through histograms. Learners will gain practical experience with Python tools like Pandas and NumPy to perform descriptive statistical analysis, making it easier to interpret and organize real-world datasets.

Probability, Summation, and Hypothesis Testing

This module explores probability fundamentals, event analysis, and hypothesis testing as cornerstones of statistical inference. Learners will calculate probabilities, analyze exclusive and independent events, and evaluate test scenarios using real data. By mastering p-values, denominators, and test statistics, learners will build strong analytical skills for interpreting uncertainty and validating data-driven assumptions.

Regression and Model Building

This module focuses on regression techniques for modeling relationships between variables. Learners will begin with the basics of regression outputs, then progress to fitting models with multiple explanatory variables, analyzing residuals, and validating assumptions. Advanced topics such as curve fitting and interpreting coefficients and intercepts will equip learners to design accurate predictive models for real-world applications.