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Customer Demand Analysis using DBSCAN

  • Schedule

    7-9 September 2021 (3 Days)

    18.30 – 21.30 (WIB)

  • Online-Interactive Learning

    Via Zoom

  • Investment

    Rp. 3.300.000

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CLASS STARTS IN

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Course Summary

Demands for ride-sharing services in urban areas are sometimes remarkably high at a particular time of day. The high requests would result in longer waiting times, and more cancellations might occur. Cancellations in ride-sharing services are problematic; it means a bad user experience, and the company would lose revenue potential.

Application owners can anticipate this by using their users’ location data to create clusters to identify when and where the demand was usually high. A strategy can then be devised to position available drivers in a specific location at a particular time of day to reduce cancellations and increase customer trust and satisfaction.

This 3-day online workshop is a beginner-friendly introduction to customer demand analysis using DBSCAN. In this session, we will analyze demands from ride-sharing application data using an underrated clustering algorithm. Throughout the online course, we will provide you 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.

NOTE: This workshop will be delivered in Bahasa Indonesia.

LEARNING OUTCOMES

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

  • Work with R and tidyverse for data cleansing and manipulation process
  • Understand spatial clustering using DBSCAN
  • Building spatial cluster for demand analysis
  • Using an interactive map to visualize cluster

LEARN AND EARN

Join this workshop and get a chance to win total prizes worth IDR 25.000.000. Terms & conditions: algorit.ma/learn-earn.

Syllabus

  • Introduction to R for data science
  • Working with RStudio environment
  • Data manipulation and processing with R tidyverse.
  • Understanding DBSCAN terminology
  • Find optimum number of cluster
  • Cluster visualization
  • Spatial clustering on ride-sharing demands
  • Building interactive maps
  • tidyverse
  • dbscan
  • factoextra
  • cluster
  • leaflet
  • fishualize

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 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. 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

David Tahi Ulubalang

Having a wide variety of industrial experiences, David Tahi Ulubalang has picked up many angles in dealing with data-related cases. He majored in Computer Science from IPB and has more than 1.000 hours of teaching experience.

David is one of 5 holders of RStudio Trainer Certification in Indonesia. He has conducted numerous consultative training for BCA, Ministry of BUMN, PT Indo Kordsa, PT Indo Tambangraya Megah, to name a few.