- Course Introduction and Python Basics
- Introduction & Descriptive Statistics
- This module will focus on introducing the basics of descriptive statistics - mean, median, mode, variance, and standard deviation. It will explain the usefulness of the measures of central tendency and dispersion for different levels of measurement.
- Data Visualization
- This module will focus on different types of visualization depending on the type of data and information we are trying to communicate. You will learn to calculate and interpret these measures and graphs.
- Introduction to Probability Distributions
- This module will introduce the basic concepts and application of probability and probability distributions.
- Hypothesis testing
- This module will focus on teaching the appropriate test to use when dealing with data and relationships between them. It will explain the assumptions of each test and the appropriate language when interpreting the results of a hypothesis test.
- Regression Analysis
- This module will dive straight into using python to run regression analysis for testing relationships and differences in sample and population means rather than the classical hypothesis testing and how to interpret them.
- Project Case: Boston Housing Data
- In the final week of the course, you will be given a dataset and a scenario where you will use descriptive statistics and hypothesis testing to give some insights about the data you were provided. You will use Watson studio for your analysis and upload your notebook for a peer review and will also review a peer's project. The readings in this module contain the complete information you need.
- Other Resources
- Cheat sheet for Statistics in Python
