Introduction

Last updated on 2025-01-29 | Edit this page

Overview

Questions

  • How can the humanities benefit from data visualization?
  • What are some of the most useful graphs for humanities research?
  • What is inferential statistics?
  • How can python be used for data visualization, to serve statistical inference and data storytelling?

Objectives

After completing this lesson, learners will be able to …

  • Understand the use cases of data visualization for the humanities.
  • Understand the concept of statistical inference to humanities researchers.
  • Visualize data with python to infer information from it.
  • Use data visualization and statistical inference for data storytelling.

Who can benefit from this lesson?

The main goal of this lesson is to demonstrate the importance of data visualization and how it can unlock a variety of learning and research pathways—ranging from exploratory data analysis and statistical inference to understanding machine learning processes and data storytelling.

If you’re looking for ways to approximately predict specific values based on a given dataset for data storytelling, or if you’ve ever wondered how machine learning models that predict values (rather than categories) work, this lesson is for you. It will introduce you to the concept of statistical inference—a mathematical calculation used in predictive machine learning algorithms—through various data visualization techniques. These visualization methods will also enhance your data storytelling skills, not only in describing existing data but also in predicting values based on the available data.

Data visualization is central to this lesson, serving as both the means and the goal. You’ll not only learn to write Python code and engage in hands-on data visualization, but also discover how to explore, understand, and predict dataset values through visualization techniques.

How is this lesson structured?

  • The lesson begins with a brief overview of various graph types and their applications.
  • Next, you’ll explore statistical inference and linear regression, which will help you understand correlations and make predictions based on datasets. These concepts also provide foundational insights into how machine learning models work.
  • Finally, you’ll learn how to use visualization techniques in an exploratory analysis and storytelling process to identify patterns within a dataset and extract statistical insights, bringing together the concepts from the previous sections and engaging in hands-on data visualization in Python.

Key Points

  • Getting to know each other.
  • An overview of the lesson structure and objectives.