Introduction to Statistics

Stanford's 'Introduction to Statistics' teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning.

- SA4.2 Beginner: Understand the data types available, data cleansing, and can complete analysis using common measures (e.g., frequency, dispersion, central tendency).

- SA4.2 Intermediate: Identify and solve data quality issues affecting the accuracy and utility of metrics and understand genuine variability in historic and current data.

- SA7.1 Beginner: Understand when advanced inferential statistical techniques are needed and the different methods available.

- SA7.1 Intermediate: Understand key regression models, cluster analysis, factor analysis, principal component analysis, etc.

- SA7.2 Beginner: Able to build basic regression models and analyze results from three or more groups.

- SA7.2 Intermediate: Apply the correct techniques to the business questions.

- SA15.1 Beginner: Can identify a business question and convert it into an appropriate hypothesis.

- SA15.1 Intermediate: Can determine the correct statistical tests for hypotheses, along with whether testing should be one-tailed or two-tailed.

- SA11.3 Intermediate: Present the results of longitudinal analysis clearly and explain the meaning and limitations of available data and its context.

SA15.2 Intermediate  You understand alpha and beta and what p-values and confidence limits mean and you know how to calculate them.