QUALITATIVE DATA ANALYSIS IN R PROGRAMMING LANGUAGE

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About Course

In a marketplace driven by feedback, reviews, and social interactions, the ability to turn unstructured text into strategic intelligence is a high-demand skill. This project-based course will take you from “messy” data to high-impact insights.

Do you want to learn how to analyze customers’ behavior to drive profit for businesses and organizations with R Programming Language?

While quantitative data shows you what is happening, qualitative data reveals why. At Boom Tech Innovations Company Limited (BTICL), we are launching a specialized course to help you bridge that gap.

What Will You Learn?

  • You will learn to identify key word associations and contexts using bigram analysis and QDA concordance techniques.
  • Students will then be able to apply advanced Natural Language Processing (NLP) methods to unstructured text.
  • You will master topic modeling to discover latent themes and subjects within large text datasets.
  • The course will also enable you to conduct lexicon-based sentiment analysis to classify the emotional tone of product descriptions.
  • This series aims to equip learners with the skills to leverage R Markdown for creating professional and visually appealing code presentations.
  • Furthermore, the series will cover techniques for enhancing the aesthetic appeal of R Markdown outputs, including image insertion, clickable links, alongside efficient handling of code execution, tables, and graphs.

Course Content

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  • Files For Hands-On Learning

MODULE 1 OF 4: FOUNDATIONAL COURSE REVISION (Click to see module content)
A foundational course in R programming for data analytics typically covers the essential skills needed to manipulate, analyze, and visualize data using R.

MODULE 2 OF 4: R MARKDOWN- ADDING BEAUTY TO YOUR CODE PRESENTATION (Click to see module content)
This series aims to equip users with the skills to leverage R Markdown for creating professional and visually appealing code presentations. Objectives include mastering file management and knitting processes to generate HTML, Word, and PDF documents. Furthermore, the series will cover techniques for enhancing the aesthetic appeal of R Markdown outputs, including image insertion, clickable links, and basic website development principles, alongside efficient handling of code execution, tables, and graphs.

MODULE 3 OF 4: QUALITATIVE DATA ANALYSIS- QDA (Click to see module content)
This module is designed to equip students with the skills to effectively interpret and analyze unstructured qualitative data. The primary objective is to move beyond mere observation, enabling students to uncover rich insights and meaningful patterns from text, interviews, and open-ended responses. Students will master techniques for identifying key themes and developing robust coding frameworks. Ultimately, this content aims to build proficiency in qualitative research methods, allowing students to support business decisions with evidence-based narratives. Upon completion, students will be able to synthesize complex information and present compelling stories hidden within the data. The final goal is to empower a new generation of data analysts who can provide a deeper, more human-centric understanding of an organization's challenges.

MODULE 4 OF 4: MODULE ASSIGNMENT

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