Data Tidying and Importing with R

External: Coursera Courses ↗ · Coursera

Open Course on External: Coursera

Free to audit · Opens on External: Coursera

Data Tidying and Importing with R

Coursera · Intermediate ·🔄 Data Engineering ·3mo ago

Key Takeaways

Imports, cleans, and organizes messy real-world data in R using dplyr, tidyr, and other Tidyverse tools

Original Description

Build confidence working with messy, real-world data. In this course, you’ll learn how to import, clean, and organize data in R so that it’s ready for analysis, visualization, or modeling. Using dplyr, tidyr, and other Tidyverse tools, you’ll practice joining datasets, reshaping data, and creating efficient data pipelines that support reproducible work. You’ll also explore how to responsibly collect and scrape data from online sources, including ethical and legal considerations. By the end of this course, you’ll know how to transform raw datasets into structured, tidy formats and you’ll understand how responsible data handling and documentation are essential to high-quality, ethical data science.
Watch on External: Coursera ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related Reads

📰
I Built My Second ETL Pipeline. This Time, I Started Thinking Like a Data Engineer
Learn how to build a production-ready ETL pipeline with Python, Docker, PostgreSQL, and Kestra by thinking like a data engineer
Towards Data Science
📰
JuiceFS Sync for PB-Scale Data Transfers: Resumable Sync, Encryption, and Bandwidth Control
Learn how to efficiently transfer large volumes of data using JuiceFS Sync, which offers resumable sync, encryption, and bandwidth control, ideal for PB-scale data transfers.
Dev.to AI
📰
How Airflow is using AI to make data engineering more resilient, not more complex
Airflow uses AI to make data engineering more resilient by detecting data drift, resuming failed pipelines, and fixing issues automatically, reducing complexity and improving reliability.
Medium · AI
📰
What Can We Do When Memory Becomes the New Bottleneck in Data Engineering?
Learn how to overcome memory bottlenecks in data engineering using Pandas chunking, Dask, and Polars, and why it matters for processing large datasets
Towards Data Science
Up next
A Moment Frozen in Time | Arnav Iyengar | TEDxJenks Youth
TEDx Talks
Watch →