Data Science Fundamentals (I)



Course details :

This 3-day workshop is designed to help you master various data visualization techniques, using a combination of R’s built-in plotting capabilities, the ggplot library, and Google Visualization API.

Student will learn the core skills to build visually appealing, rich graphical narratives through practical, hands-on exercise using real, commercial datasets. At the end of the workshop, student will present their project and demonstrate their thought process leading up to their visual products.

Learning from data is virtually universally useful. Master it and you’ll be welcomed nearly everywhere! ~ John Elder, Elder Research

Please bring along:

  • 1x Laptop
  • Purchased ticket (from organizer’s website)

In a nutshell

  • Data Science Introduction

    Day 1

  • Statistics Fundamentals

    Day 1

  • Grammar of Graphics & ggplot

    Day 2

  • Data Visualization in Practice

    Day 3

Event Ended
Explore other data science workshops


Samuel Chan


Detailed Syllabus

Syllabus: Data Science Fundamentals (I)

Data Science Explained

  • Description of course materials and the learning environment
  • A comprehensive view on the roles of data science, the relating professions, career prospects and outlook.
  • Description of the workflow, tools, setup and programming languages in the course

R Programming Basics

  • Setting up the Workspace and Environment
  • Working with data types: scalar, vector, list, matrix, data frame
  • R’s built-in functions
  • Inspecting data using built-in functions
  • R’s plotting capabilities
  • R Markdown and reproducible research

Statistics Fundamental

  • Demonstrate the use of various statistics in exploratory data analysis: 5-number summary, mean, mode, interquartile range, variance, standard deviation and correlation
  • Plots: scatterplots, scatterplot matrices, line graphs, histogram, ab-line, x and y-axis styling, plot title, tips and tricks for plotting in R
  • Quick way to get a “sense” of the distribution of our dataset
  • Linear Regression, Confidence intervals and Hypothesis Testing

Plotting in R

  • Plotting options: base, lattice graphs, ggplot
  • Grammar of Graphics
  • Beautiful plots: scatterplots, line, histogram, violin plot, boxplot, jitter plot
  • Styling your plots: Title, Labels, Font Family, Axes
  • Styling legends and guides
  • Layering other aesthetics in ggplot
  • Multi-panel plots

Advanced styling

  • Working with built-in themes
  • Build your own theme
  • Using pre-made theme
  • Working with colors

Visual Narrative

  • Case in point: point vs jitter
  • Case in point: box plot vs violin plot
  • Combining plots to form a beautiful narrative
  • Tips for storytelling with data

Data Visualization in Practice

  • Combining a regression line with beautiful plotting aesthetics
  • Adding a confidence interval
  • Code solution to data visualization project exercise offered by Harvard University’s Institute for Quantitative Social Science (IQSS)’s workshop
  • Data sources for data visualization project

Other Libraries for Data Visualization

  • ggRepel for text labels
  • Latticeplot
  • Interactive plotting with manipulate()
  • Example Demo: Presenting a data visualization project with ggplot

Demo Day

Student will band into teams of 2 to present their data visualization project in a 15-minute per team demo session.

This workshop will cost 3 workshop credits for subscribers. Non-subscribers are welcomed to participate at a cost of IDR1,500,000.

3x Complimentary Student’s Pass

Studying mathematics, statistics, or a math-related field in a local university? Stand a chance to receive a complimentary student pass by sending us an email. Members of our staff will reach out to verify your scholarship.

Receive your free entry: