https://github.com/srosalino/data_wrangling_investigations

Series of 3 investigation works, regarding the subject of Data Wrangling (Acquire data from different sources; Understand how to clean and pre-process data; Transform data for analytics purposes; Perform feature engineering; Visualize data)
https://github.com/srosalino/data_wrangling_investigations

data-cleaning-and-preprocessing data-extraction-and-pre-processing data-visualization feature-engineering

Last synced: 4 days ago
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Series of 3 investigation works, regarding the subject of Data Wrangling (Acquire data from different sources; Understand how to clean and pre-process data; Transform data for analytics purposes; Perform feature engineering; Visualize data)

• Host: GitHub
• URL: https://github.com/srosalino/data_wrangling_investigations
• Owner: srosalino
• Created: 2024-07-12T12:08:12.000Z (4 months ago)
• Default Branch: master
• Last Pushed: 2024-07-12T12:15:12.000Z (4 months ago)
• Last Synced: 2024-07-12T14:06:30.445Z (4 months ago)
• Topics: data-cleaning-and-preprocessing, data-extraction-and-pre-processing, data-visualization, feature-engineering
• Language: Jupyter Notebook
• Homepage:
• Size: 2.17 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Vrije Universiteit Amsterdam Data Wrangling Program

**Course Objective**

The course is geared toward getting data ready for its end purpose. After this course, the student should be able to: 1. acquire data from different online and offline sources, 2. understand how to clean and pre-process data, 3. transform data for analytics purposes, 4. perform feature engineering, 5. visualize data.​

**Course Content**

Data wrangling is the process of gathering data in its raw form and molding it into a form that is suitable for its end use. This course is about how to gather the data that is available and produce an output that is ready to be used. There are a number of common steps in the data wrangling process that will be discussed: acquiring, cleaning, shaping and structuring the data, as well as feature engineering and visualization.