Unveiling Socio Demographic Patterns: Exploring Data Panels in R

Utilize panel data for better decision-making, accurate modeling, and efficient problem-solving.

  • Schedule

    27 – 29 February 2024

    18.30 – 21.30 (WIB)

  • Online-Interactive Learning

    Via Zoom

  • Investment

    Rp. 1.500.000

Course Summary

This course is designed for beginners who want to provide a comprehensive introduction to panel data processing in R. Participants will gain basic knowledge of R programming and panel data modeling concepts, from basic theory to practical applications. This in-depth journey will learn how to manipulate panel data, perform data exploration and most importantly for Socio Demographic Patterns, students will be given hands-on experience performing descriptive analytics with panel data models.

In this 3-day online training, participants will learn how to process and extract in-depth information from Socio Demographic Patterns using panel data analysis. By mastering these skills, participants will be prepared to apply their knowledge in academic, research and business environments. Ultimately, this course will empower participants to utilize panel data for better decision-making, accurate modeling, and efficient problem-solving.
NOTE: This workshop will be delivered in Bahasa Indonesia.

Learning Outcomes

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.
  • Understand what is socio demographic patterns
  • Understand panel data 
  • Perform panel data modeling with the plm() function
  • Interpret the results of panel data modeling


  • Introduction about Socio Demographic patterns
  • Introduction about panel data with simple representations.
  • Reading and Writing Panel Data.
  • The concept and road map of panel data model
  • Panel data formating
  • Data balancing check
  • Missing value checking and handling
  • Data structure adjustment
  • Exploratory Data analytics for Socio Demographic patterns
  • Cross-validation for panel data
  • Multicollinearity assumption check
  • Plm model building()
  • Selection of the best model
  • Assumption testing
  • Model interpretation


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


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.


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


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


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


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.


Yusuf Rafsanjani

Yusuf Rafsanjani is a data science instructor at Algoritma Data Science School with a robust educational background in statistics, providing a strong foundation for extracting insights from data through data visualization, assumption testing, and predictive model building using machine learning techniques. Proficient in R and Python programming languages.

With excellent communication skills, Yusuf excels in conveying complex concepts to learners, evidenced by his involvement as a team teacher in prestigious corporate training programs for companies such as Toyota Astra Motor, Bank DBS, Bank BRI, Bank HSBC, LAN RI, and KPU RI. he has successfully completed projects such as developing dashboards for operational algorithm teams to track and manage inventory requests efficiently.