01 Introduction

Welcome to Data Science Toolbox!

This week we will prepare you for your Data Science Journey. It is essential that you prepare before contact time. That means:

• Watch and reflect on the Lectures;
• Look at the worksheets and think about the questions; as a minimum, make notes on how you might go about answering them;
• Most importantly, look at the Workshop content and do the pre-preparation for it.

The first two blocks demand the most work to allow you to hit the ground running. There is less content in future blocks, with a corresponding increased amount of time for group assessments.

Overview:

In Block 01, we cover:

• What is Data Science Toolbox?
  • Use of Group Assessments.
• What is Data Science?
• An overview of Exploratory Data Analysis (EDA).
  • Exploratory Data Analysis with R.
• Setting up a basic Data Science Environment with Rstudio
  • NB: We cover Python starting Block 06.
  • Using Git (via GitHub Desktop) for collaborative projects.
• How to work with Cyber security data

Lectures:

• 1.0 - Introduction to the course (22.16)
• 1.1 - Introduction to Data Science (26.59)
  • Slides
• 1.2 - Exploratory Data Analysis (26.45)
  • Slides
  • Reference R code (NB: See 1.3.1 for explanation)

Worksheets:

• Worksheet 1.2 Exploratory Data Analysis

Preparation:

• Everyone needs to have followed the Block 01 preparation given in Appendix 1.
• Specifically, you must have installed Rstudio and Github Desktop, and seen the appropriate training content.
• You cannot properly use the interaction time unless you have done this preparation in advance!

Workshop:

In the workshop, we will be discussing how to collaborate and work together remotely. We will then discuss Exploratory Data Analysis in practice. This section uses the command line - see the github appendix for details and additional tutorials.

• 1.3.1 - Workshop Lecture for RStudio (29.05)
  • Rmd for 1.3.1 Introduction to R Studio
• 1.3.2 - Workshop Lecture for Exploratory Data Analysis (18.13)
  • Rmd for 1.3.2 Exploratory Data Analysis R markdown
• 1.3.3 Workshop Lecture on Assessments, split into the following parts:
  • 1.3.3.1 - GitHub Workshop Intro (4:33)
  • 1.3.3.2 - GitHub Workshop Git Setup (8:10)
  • 1.3.3.3 - GitHub Workshop Git Repositories for Projects (21:32)
  • 1.3.3.4 - GitHub Workshop Assignment 0 walkthrough (19:48)
  • 1.3.3.5 - GitHub Workshop Example Assignment walkthrough (9:44)

Workshop Activity:

Before the workshop, you will have attempted to understand the Workshop content. This workshop will discuss difficulties encountered during this content.

Assessments:

• The Example Assessment should be carefully examined.
• Assessment 0 will be set in this week; see Assessments. This is a formative assessment (i.e. does not contribute to your grade) and will be due in Week 3.

References:

The main references are:

• The GitHub Appendix
• The Beginning Git tutorial for Command Line Git.

Data Sources:

We use the following Cyber Security Data Sources:

• Bro log data from Secrepo
• This can be loaded into R in a nice form with a script (raw) that can be run directly from R using source https://raw.githubusercontent.com/dsbristol/dst/master/code/loadconndata.R.
• The KDD99 dataset, which was created for a competition with a task specification. We normally use the 10% and column names files, which you can download directly.

Navigation

Previous: About the Course (Block 00). Next: Block 02.