An out-of-the-box Shiny app template that we'll use to walk through the hypothetical example of a cybersecurity company. The components are:

• Shiny web app framework
• ggplot2 visualization toolkit
• maps package for geo-visualization tool
• dplyr and other members of the tidyverse family
• Bootstrap 4
• HTML and CSS

This serves as some boilerplate code we will use for the code-along session. It's a starting point that you can take and flesh it out into a minimal Intrusion Detection System. Throughout the session we'll be adding some extra web framework components, visualization components and walk through the best practice of integration HTML, CSS, and JavaScript into a Shiny project (for R developers) or Flask project (Python developers).

1. Forking it and then using git clone.

• Example: git clone https://github.com/onlyphantom/darkershiny.git

2. Run app.R

1. Download the repo from this repo.
2. Run app.R

The dependencies are automatically installed if R doesn't detect them. After installation, it is recommended that you stay connected to the internet while running app.R for the first time. R packages are installed on your computer so you will not need to have an active internet connection after this.

The code and its asset can be used to follow along the code-along session of Algoritma's Kickstart Series: Gentle introduction to Developing Web Apps in R & Python.

• Date: Saturday, February 16th 2019
• Time: 1300 - 1600
• Venue: Innovation Factory, Bandung
• Lead Instructor: Samuel Chan

• Date: Saturday, February 23rd 2019
• Time: 1300 - 1600
• Venue: Innovation Factory, Jakarta
• Lead Instructor: Samuel Chan

The workshop aims to provide a beginner-friendly introduction to packaging your data science work into a user-friendly, modernly-themed web analytics application.

Corporate consultant and course producer Samuel Chan has worked for a number of public-listed company prior to his role at Algoritma. His involvement has always heavily centered around product development, from building chatbots for companies to developing cloud-based products. In this workshop, he will walk us through his favorite tools, workflow, and productivity tips:

• Recommendations on productionizing R code
• The Shiny framework for data scientists
• Using Python's web frameworks for application development
• TensorFlow as a web service
• Cloud deployment options for R- or Python-based applications
• Production concerns: security, uptime, and continuous integration
• Productivity tools for real-life data science: Git, IDE extensions, Virtual Environments vs Docker

The dataset is sampled from AlienVault's [IP Reputation database](http://reputation.alienvault.com/ reputation.data) for educational purpose. AlienVault, Inc. is a developer of commercial and open source solutions to manage cyber attacks, including the Open Threat Exchange, the world's largest crowd-sourced computer-security platform.

The assets (badge, images and other intellectual property) is taken from Algoritma Data Science Education Center with permission.

The work is sponsored by:

• BLOCK71's Innovation Factory
• Algoritma, a data science education center on a mission to democratize world-class education, helping professionals to gain proficiency in machine learning and data visualization by building with real-world projects.