Fundamentals of Data Science (Fall 2025)

This Fundamentals module is classroom-tested from Data 6 Fall 2025.

In this module, students build a foundation in Python programming and data science, progressing from Python syntax to skills like data cleaning and array manipulation. Students apply these computational tools to data from public health contexts and analyze population estimates and health disparities. They also examine the ethical implications and potential biases in data collection. Students learn to interrogate how data is measured and its broader impact on society.

Throughout the module students will learn the following content topics:

• Introduction to Computer Programming:

  • Python Syntax

  • Operators, built in functions and print()

  • Variables and Name Assignments/Conventions

  • Data Types and Data Casting

  • Interpreting Error Messages

  • Call Expressions and Functions

• Introduction to Data Science:

  • History of Data Science as a field

  • Features of data sets and table attributes

  • Arrays and array operations

  • NumPy functions

  • Data cleaning

  • Measures of central tendency (mean, mode, median, range)

  • Categorical and Quantitative Variables

• Data Sensemaking:

  • Asking questions about ethics and data science

  • Interrogating what is being measured, what was left out, who collected the data and potential sources of bias

  • Implications of data analysis on society through applied examples.

• Applications of Data Science to Public Health:

  • Age-standarization

  • Population Estimates

  • Incidence vs Crude Rates

  • Aggregation and Disaggregation of Variables

  • Disparities in Health Outcomes