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Building Customer Segmentation Using PAM Clustering Algorithm

By building accurate customer segmentation, you will have more information to make product development, advertising, marketing, and pricing decisions.

  • Schedule

    28 – 30 June 2022

    18.30 – 21.30 (WIB)

  • Online-Interactive Learning

    Via Zoom

  • Investment

    Rp. 1.500.000

Course Summary

This 3-day online workshop is a beginner-friendly introduction to Partition Around Medoids (PAM) algorithms for Customer Segmentation. Segmentation makes it easier for your team to understand their customer’s needs. Your team can improve sales conversion rates and increase retention and customer satisfaction by understanding your customers.

We will provide participants with a rich interactive experience throughout the online course. One Instructor and two Teaching Assistants will help participants troubleshoot or help with any difficulties encountered.

Upon completion of this workshop, you will be able to:

  • Work with the R programming language and open-source packages for the data cleansing and manipulation process.
  • Use the R language to do exploratory data analysis.
  • Building customer segmentation profiles using PAM algorithms.
  • Implement clustering on any data type.

NOTE: The workshop will be delivered in Bahasa Indonesia

Syllabus

  • Description of the workshop’s objectives.
  • A brief explanation of clustering and area of implementation.
  • Description of the workflow, tools, and setup.
  • What is R?
  • Working with an RStudio environment.
  • Packages and loading libraries.
  • Basic R data structure.
  • Concept of machine learning.
  • Unsupervised vs supervised learning.
  • Clustering methodology and implementation.
  • K-mean Clustering.
  • Partitioning Around Medoids (PAM).
  • Euclidean distance vs Gower Distance.
  • Cluster analysis for multiple data types.
  • Choosing the optimum number of clusters.
  • Visualizing clusters.
  • Learn-By-Building: Building Customer Segmentation from Hotel Customers Data.

STUDENT TESTIMONIALS

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

LEARN FROM ANYWHERE

Our learning format is online-interactive, you will feel the interactive experience as if you were present in a physical classroom. You can access the class using your Zoom account on pre-defined dates.

  • LEARN AT YOUR OWN PACE

    Zoom recording, course Books (PDF & HTML files), the dataset for practice, reference notes, and working files are accessible through our Learning Management System account.

  • PROOF YOUR MASTERY

    Show current and prospective employers of your mastery in computer vision with a signed certificate of completion.

  • CONNECT WITH LIKE MINDED PEOPLE

    Be a part of our data-passionate community with 5000+ members and 1000+ alumni.

FOR ABSOLUTE BEGINNERS

Workshops in this series are tailored to casual programmers and non-programmers that are taking their first steps into data science. It assumes no prior knowledge or academic background, and attendees will be introduced to the beautiful art of writing R / Python code to produce data visualization and build machine learning models. The workshop has a gentle learning slope that is designed with non-technical professionals and academics in mind.

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.

YOUR INSTRUCTOR

Victor Nugraha

Victor Nugraha is a Data Science Instructor at Algoritma Data Science School with expertise in Data Visualization, Interactive Dashboard innovation, and Machine Learning implementation. Victor has been involved in numerous mentoring, data science projects, and consultative data science training for Bank Central Asia and Badan Penyelenggara Jaminan Sosial (BPJS).