Topic Modeling for Text Analysis in R

Online Data Science Series

  • Schedule

    24 – 26 November 2020 (3 Days)

    18.30 – 21.30 (3 hours / day)

  • Online-Interactive Learning

    Via Zoom

  • Investment

    IDR 550.000

Course Summary

Topic modeling is one of the popular methods used in text analysis. With topic modeling, businesses are able to identify the topics of a set of textual data by detecting patterns and recurring words, helping them to understand and summarize large collections of textual information from any text data.

This 3-days online workshop is a beginner-friendly introduction to topic modeling using R. Throughout the online course, we will provide participants with hands-on examples and a rich interactive experience. One Instructor and two Teaching Assistants will help participants troubleshoot or help with any difficulties encountered.

NOTES: This workshop will be delivered in Bahasa Indonesia.


Upon completion of this workshop, you will be able to:
  • Work with the R language and open source packages for data cleansing and manipulation process.
  • Automatically determine the cluster topic for a set of documents with the topic model.
  • Evaluate and produce visualization for generated topics.


  • Description of course materials, timeline, and objectives of the workshop
  • A comprehensive point of view of the role of data science
  • A brief explanation of text mining, topic modeling, and machine learning
  • Description of the workflow, tools, and set up for the course
  • Introduction to R programming language
  • Working with the RStudio environment
  • Using R markdown for reproducible research
  • Inspecting data structure
  • The essence of text mining or natural language processing
  • Working with a text corpus, a large and structured set of texts
  • Preparing your text data: data cleansing and manipulation
  • Word-tokenizing to identify word’s meaning
  • Using visualization to analyze text data
  • Examples of utilizing topic modeling in various industries
  • Understanding the principles and workflow of topic modelling
  • Understanding LDA (Latent Dirichlet Allocation), the algorithm behind topic modelling
  • Exploring & Interpreting the output of a topic model
  • Extracting topic from Covid-19 news articles
  • Optional: Extracting topic from online marketplace review


This testimonial video is taken after our previous Online Data Science Series: Time Series Analysis for Business Forecasting.


Your Instructor

Arga Adyatama is a Data Science Instructor at Algoritma Data Science School. He has exceptional skills in translating business requirements into a data-driven solution and using data visualization to communicate ideas. Arga has involved in numerous consultative data science training for our clients, to name a few:

  • Bank Central Asia
  • PT Indo Kordsa
  • PT. Sigma Metrasys Solution
  • Kementerian BUMN

Online Data Science Series

Workshops in our Online Data Science Series are tailored to casual learners, working professionals, and non-programmers that are taking their first steps into data science and machine learning.

Students are not assumed to have a working knowledge of R or prior proficiency in statistics/mathematics/algebra. At such the workshop follows a gentle learning curve and emphasize on hands-on, one-to-one tutoring from our team of instructors and teaching assistants

Frequently Asked Questions

Yes, you can still attend the workshop as it is a beginner-friendly workshop.

Our system will send you an email containing a link and details to join a Google Classroom.

Online learning will be conducted via Zoom.us, Link to join the Zoom Class will be announced via Google Classroom.

Learning materials can be obtain via Google Classroom

Yes, you will receive a certificate of completion.

Workshop Receivables:

  • Workshop Lecturer’s Notes

    Including 2x Course Books (PDF), HTML files, course transcripts (if any).

  • Highly-accelerated Learning

    Learn under the assistance of the mentorship of our lead instructor and a band of qualified teaching assistants throughout the 4-day course.


    Show current and prospective employers that you’ve completed the course with a signed certificate of completion.

  • Supplement Materials

    Receive supplement datasets to practice on, reference notes, working files (R Notebook or Jupyter Notebook), and other materials that will help you master the topics.