Probability and Statistics for Data Science

Probability and Statistics for Data Science

This handbook has 8 modules and 33 lessons. It covers the foundations of statistics, describing data with numbers and charts, probability rules, random variables, common probability distributions, sampling and estimation, hypothesis testing and A/B testing, and finally regression and model evaluation applied in a data science workflow

Probability and Statistics for Data Science

Data science results are only as reliable as the statistics behind them. Without that foundation, it is easy to trust a biased sample, mistake chance for a real pattern, or call a small model improvement a success. Probability and statistics let you check data quality, measure uncertainty, compare models and test whether a change, such as a new webpage design, truly made a difference. They also help you avoid common errors like confusing correlation with causation or overfitting a model.

Probability and Statistics for Data Science

- Curious beginners who want to understand statistics and data science from the ground up. - Aspiring data analysts and data scientists building their core foundation. - Working professionals who use data or dashboards and want to interpret results with confidence. - Software developers and analysts moving into machine learning or data science. - Anyone who uses statistical tools but wants to know why the methods work.

Prerequisites

This course is suitable for:

• Basic arithmetic, fractions, percentages and simple algebra.
• Ability to read a table or a simple chart.
• Basic idea of what a dataset is, such as rows and columns in a spreadsheet.
• Optional: beginner-level Python, only needed to run the code snippets yourself (NumPy, pandas, SciPy, statsmodels and scikit-learn are used).
• No prior knowledge of statistics or probability is needed.