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Text Mining with Bag-of-Words in R

IntermediateSkill Level
4.8+
132 reviews
Updated 05/2024
Learn the bag of words technique for text mining with R.
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RMachine Learning4 hr15 videos69 Exercises5,700 XP44,249Statement of Accomplishment

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Course Description

It is estimated that over 70% of potentially usable business information is unstructured, often in the form of text data. Text mining provides a collection of techniques that allows us to derive actionable insights from unstructured data. In this course, we explore the basics of text mining using the bag of words method. The first three chapters introduce a variety of essential topics for analyzing and visualizing text data. The final chapter allows you to apply everything you've learned in a real-world case study to extract insights from employee reviews of two major tech companies.

Prerequisites

Intermediate R
1

Jumping into Text Mining with Bag-of-Words

In this chapter, you'll learn the basics of using the bag-of-words method for analyzing text data.
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2

Word Clouds and More Interesting Visuals

3

Adding to Your TM Skills

4

Battle of the Tech Giants for Talent

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*4.8
from 132 reviews
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FAQs

What is the bag-of-words method and when is it useful?

Bag-of-words represents text as word frequency counts, ignoring word order. It is a foundational text mining technique useful for extracting patterns and insights from unstructured text data.

What real-world case study is included in this course?

The final chapter is a case study where you analyze employee reviews of two major tech companies to extract HR analytics insights using all the text mining skills you learned.

Will I learn to create word clouds?

Yes. Chapter 2 focuses on word clouds and other text visualizations that make your text mining results both informative and engaging.

What R prerequisites do I need?

You only need Introduction to R and Intermediate R. No prior text mining or NLP experience is required for this beginner-level course.

How large is this course compared to other DataCamp courses?

It is a substantial course with 4 chapters, 69 exercises, and 5,700 XP. Most learners complete it in about 4 to 5 hours.

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