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Data Science: Wrangling

8 Weeks 1 - 2 Hours per week recommended
2680

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Data Science: Wrangling

8 Weeks
1 - 2 Hours per week

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About this course

In this course, part of our Professional Certificate Program in Data Science, we cover several standard steps of the data wrangling process like importing data into R, tidying data, string processing, HTML parsing, working with dates and times, and text mining. Rarely are all these wrangling steps necessary in a single analysis, but a data scientist will likely face them all at some point.

Very rarely is data easily accessible in a data science project. It’s more likely for the data to be in a file, a database, or extracted from documents such as web pages, tweets, or PDFs. In these cases, the first step is to import the data into R and tidy the data, using the tidy verse package. The steps that convert data from its raw form to the tidy form is called data wrangling.

This process is a critical step for any data scientist. Knowing how to wrangle and clean data will enable you to make critical insights that would otherwise be hidden.

What you’ll learn

  • Importing data into R from different file formats
  • Web scraping
  • How to tidy data using the tidy verse to better facilitate analysis
  • String processing with regular expressions (regex)
  • Wrangling data using dplyr
  • How to work with dates and times as file formats
  • Text mining

Professional Certificate in Data Science

Real-world case studies to jumpstart your career

Data Science: R Basics
Data Science: Visualization
Data Science: Probability
Data Science: Inference and Modeling
Data Science: Productivity Tools
Data Science: Wrangling
Data Science: Linear Regression
Data Science: Machine Learning
Data Science: Capstone
9 Courses 1 year 5 months
Complete Program $792.80
Program Details

Meet your instructors

Rafael Irizarry

Rafael Irizarry

Professor of Biostatistics at Harvard University
Rafael Irizarry is a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and a Professor of Biostatistics and Computational Biology at the Dana Farber Cancer Institute. For the past 15 years, Dr. Irizarry’s research has focused on the analysis of genomics data. During this time, he has also has taught several classes, all related to applied statistics. Dr. Irizarry is one of the founders of the Bioconductor Project, an open source and open development software project for the analysis of genomic data. His publications related to these topics have been highly cited and his software implementations widely downloaded.

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2680

Duration

8 Weeks

Experience Level

Introductory

Learning Partner

Harvard

Subject

Data Analysis & Statistics