POL3325G Lecture 1

January 7, 2025

Data science - what is it?

A set of tools and processes for handling large datasets, automating analyses and uncovering patterns in data that would be hard to detect by hand.
- relies on programming languages, such as R and Python
- rely on statistics to make valid inferences about populations from the samples that we are able to collect and analyze.
- increasingly used in social science research for quantitative analysis

Data science is a very broad term that captures the sets of tools and processes that we use to handle large datasets, to automate analyses, and uncover patterns in data that would be hard to detect by hand.
Data scientists uses programming languages such as R and Python to complete the analyses and handle data. R is both a programming language and a software environment meaning we use R to complete tasks, or in our case, RStudio, and we are also writing R code.
(Programmaing language - the way we communicate with a machine/computer. Made up of specific synatx.)
Data scientists also rely on statistics in order to make inferences about populations from the samples we are able to collect and analyze.
- What does this mean? Because we typically cannot analyze an entire population (for instance, think of the entire Canadian population - we cannot gather information about every person and their political beliefs), we must rely on samples that are representative. The principles of statistics are used to allow us to test assumptions or infer things about a population by relying on the analysis of samples.
- Statistics underpins hypothesis testing, estimation, causal inference. But statistics will not be the focus of this course, nor will statistical models or causal inference. Instead, we will focus mostly on using data science techniques to describe data.

“In the world around us, people look for patterns and stories in data. In data science, we explore methods from statistics and computing in combination what we know from other subject areas to better understand our data and how to interpret our findings. We learn to handle data more efficiently, the possibilities and limits of data-centric techniques… and the ways in which our humanity affects how we collect, curate and present data and stories from our immediate contexts to the world.”
- University of Calgary Data Science Program Definition