Intro to Data Science

From visualizing trends over time to analyzing text data and mapping spatial patterns, the ability to transform complex, messy datasets into clear, actionable insights is essential for engaging with modern challenges. This course provides an introduction to the tools and techniques of data analysis and visualization, focusing on making social data comprehensible and accessible to diverse audiences.

No prior programming experience is required. The first part introduces essential computer skills, technical terminology, and the basics of R. The second part moves into applied projects: text analysis, geospatial data, and integrating multiple datasets into reproducible workflows for research, government, or industry.

Prereq None — no prior programming experience required

Tools R · tidyverse · ggplot2 · stringr · sf

What you will be able to do

Wrangle data

Import, clean, reshape, and join datasets using tidyverse tools

Visualize clearly

Build publication-quality charts and maps with ggplot2 and tmap

Analyze text

Extract entities and patterns from unstructured text using NER tools

Work reproducibly

Write R Markdown reports that fully document data and analysis

Course overview

Part 1 R fundamentals

Building a working foundation in R — from the environment and basic syntax to data structures and programmatic thinking.

R Markdown data types data frames subsetting purrr

Part 2 Data wrangling and visualization

Getting real-world data into shape and turning it into clear, publication-ready graphics.

dplyr tidyr ggplot2 joins stringr regex

Part 3 Text and spatial analysis

Extracting structure from unstructured text and working with geographically referenced data.

NER spacyr sf tmap leaflet choropleth

Part 4 Integration and communication

Combining tools into a single cohesive workflow and communicating findings to diverse audiences.

interactive viz R Markdown reproducibility communication