Data Science Fundamentals (I)

R, Data Visualization
BASE Cowork Lounge, Jl. Jend. Sudirman Kav. 79, Jakarta Pusat
18-20 July, 2017
Time: 6pm - 9pm
30 participants
Difficulty: Intermediate

Days

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

Trainer

Samuel Chan

samuel@algorit.ma

Detailed Syllabus

Syllabus: Data Science Fundamentals (I)

18 July 2017

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

19 July 2017

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

20 July 2017

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

22 July 2017

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.

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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.