01 Introduction

Welcome to Data Science Toolbox!

This week we will prepare you for your Data Science Journey.

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.

Lectures:

• 01.1-Intro
• 01.2-EDA
  • Reference R code (NB: See 1.3.1 for explanation)

Workshop:

In the workshop, we will be focussing on remote collaboration and working. 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
  • See Appendix 1: Installing and working with Rstudio
• 1.3.2 - Workshop Lecture for Exploratory Data Analysis (18.13):
  • Rmd for 1.3.2 Exploratory Data Analysis R markdown
  • HTML for 1.3.2 Exploratory Data Analysis R markdown
  • (Warning: video uses a slightly out-of-date Rmd document.)
• 1.3.3 Workshop Lecture on Assessments
  • See Appendix 3: Installing and working with Github Desktop
  • 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 is discussing the Assignment, which we cover in class.
  • (Example Assessment link)

Assessed work:

• Portfolio 01 long form worksheet.
• Block01 on Noteable via Blackboard:
  • Go to the Data Science Toolbox blackboard page
  • Select Noteable
  • Select R with stan as your “personal notebook server” and press “Start”
  • Go to “Assignments”
  • Select Block01 and press “Fetch”
  • Click Block01> which opens up a drop down containing the only assignment, Block01. Select this.
  • The assignment opens in Jupyter. Complete the worksheet. When you are done, save and return to the Assignments tab. Press validate, and when it is successful, press submit.

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.
• Portfolio 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.

Reference material

• Worksheet 1.2 Exploratory Data Analysis

  References:

The main references are:

• Appendix 1 Rstudio
• Appendix 2 Replicability
• Appendix 3 GitHub
• The Beginning Git tutorial for Command Line Git.
• R for Data Science by Hadley Wickham and Garrett Grolemund
• For an overview about how Data Science fits into other disciplines, see my mathcareers article on What to know before studying data science.

  Data Sources:

This section used 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.