Statistics for Data Science

Deepen your analytical skills with this beginner-friendly course in real-world statistics. This course will teach you the statistical concepts & techniques you need to conduct rigorous inferential analyses and draw accurate conclusions from data sets.

• Simpson’s Paradox
• Examine a case study to learn about Simpson’s Paradox.
• Binomial Distribution
• Learn about binomial distribution where each observation represents one of two outcomes and derive the probability of a binomial distribution.
• Bayes Rule
• Build on conditional probability principles to understand the Bayes rule and derive the Bayes theorem.
• Sampling Distributions and Central Limit Theorem
• Use normal distributions to compute probabilities and the Z-table to look up the proportions of observations above, below or in between values.
• Hypothesis Testing
• Use critical values to make decisions on whether or not a treatment has changed the value of a population parameter.
• T-Tests and A/B Tests
• Test the effect of a treatment or compare the difference in means for two groups when we have small sample sizes.
• Logistic Regression
• Use logistic regression results to make a prediction about the relationship between categorical dependent variables and predictors.
• Course Project: Analyze A/B Test Results
• In this project, you will be provided a dataset reflecting data collected from an experiment. You’ll use statistical techniques to answer questions about the data and report your conclusions and recommendations in a report.