Data – What It Is, What We Can Do With It

This module provides an introduction to the framework for thinking about the various purposes of statistical analysis. Participants will gain insights into the use of data for descriptive, causal, and predictive inference, and develop a research study for causal analysis.

• Explore statistical inference and the components of scientific research.
• Understand the importance of scientific theories and their role in scientific research.
• Learn about big data and its implications, as well as examples of concerning research studies.

Through this module, students will delve into the causality framework, gaining an understanding of causal effects, randomized controlled trials, and observational studies. They will also learn strategies for estimating causal effects and different estimation methods.

• Explore causal effects, randomized controlled trials, and observational studies.
• Understand causal inference based on counterfactuals and difference-in-difference estimation.
• Engage with practice problems to reinforce learning.

This module focuses on descriptive statistics, highlighting the need for descriptive statistics and the various measures of central tendency and spread. Participants will gain proficiency in interpreting and analyzing descriptive statistics.

• Understand the significance of descriptive statistics and measures of central tendency and spread.
• Explore skewness and its relationship to the mean, median, and mode.
• Engage with practice problems to reinforce learning.

Throughout this module, students will learn about the elements of good visualizations and explore different types of visualizations, such as bar plots, histograms, box plots, scatter plots, and line graphs. They will also understand the importance of effective publication of visualizations.

• Understand the elements of good visualizations and different types of visualizations.
• Explore the effective publication of visualizations.
• Engage with practice problems to reinforce learning.