The Power of Statistics

Introduction to Course 4 in the Google Advanced Data Analytics Certificate introduces the role of statistics in data science and the foundations of descriptive and inferential statistics. Explore measures of central tendency, dispersion, and position, and learn to compute descriptive statistics with Python.

Module 2, Probability, delves into fundamental concepts of probability, including objective versus subjective probability, basic rules of probability, conditional probability, and probability distributions such as binomial, Poisson, and normal distributions. Work with probability distributions in Python to model data and explore real-world applications.

Sampling, in Module 3, introduces the sampling process, comparison of sampling methods, and the impact of bias on data. Learn about probability and non-probability sampling methods, the central limit theorem, and conduct sampling distributions with Python to infer population parameters.

Module 4, Confidence Intervals, covers the construction and interpretation of confidence intervals for proportions and means, including small sample sizes. Gain practical skills in constructing confidence intervals and interpreting their significance, and utilize Python for confidence interval calculations.

Module 5, Introduction to Hypothesis Testing, explores the differences between null and alternative hypotheses, types of errors, statistical significance, and A/B testing. Learn to use Python for conducting hypothesis tests and gain insights into experimental design and real-world case studies.

In Module 6, the end-of-course project offers workplace scenarios and portfolio project overviews. Create and assess your end-of-course project, gaining hands-on experience in applying the acquired statistical skills to real-world data analytics scenarios.