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Getting and Cleaning Data Course

Getting and Cleaning Data Course Course

A foundational course for anyone working with real-world data. It emphasizes not just the what, but the how and why behind good data preparation practices using R.

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9.7/10 Highly Recommended

Getting and Cleaning Data Course on Coursera — A foundational course for anyone working with real-world data. It emphasizes not just the what, but the how and why behind good data preparation practices using R.

Pros

  • Teaches real-world data acquisition and transformation techniques
  • Strong focus on reproducibility and documentation
  • Highly practical assignments using R
  • Covers a wide range of file formats and sources

Cons

  • Requires basic knowledge of R programming
  • Less suitable for learners preferring Excel or Python workflows

Getting and Cleaning Data Course Course

Platform: Coursera

What will you in the Getting and Cleaning Data Course

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  • Acquire data from sources such as web pages, APIs, databases, and flat files

  • Clean and reshape datasets into tidy formats ready for analysis

  • Perform data manipulation using R and essential libraries like data.table

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  • Work with different file formats: CSV, XML, JSON, Excel, HDF5

  • Apply principles of reproducible research in data processing workflows

Program Overview

1. Introduction and Getting Raw Data
Duration: 2 hours

  • Understanding the difference between raw and tidy data

  • Downloading and reading data from local and online sources

  • Introduction to using data.table for fast data manipulation

2. Reading and Cleaning Data
Duration: 1 hour

  • Accessing data from MySQL databases and web APIs

  • Importing and handling data in multiple formats (Excel, XML, JSON)

  • Preprocessing steps including trimming, renaming, filtering

3. Data Tidying and Transformation
Duration: 10 hours

  • Reshaping data using functions like melt, dcast, and merge

  • Dealing with missing values and inconsistent formatting

  • Practical cleaning and transformation with real-world datasets

4. Reproducible Research and Final Project
Duration: 6 hours

  • Writing clean, reproducible code for data workflows

  • Creating R scripts and markdown documentation for analysis

  • Final project to demonstrate cleaning, transforming, and documenting data

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Job Outlook

  • Data Analysts: Improve reliability and integrity of analysis pipelines

  • Data Scientists: Gain strong foundational skills in preprocessing

  • Researchers: Support reproducibility in scientific data workflows

  • Students and Beginners: Build readiness for advanced data science or machine learning

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