FIT5145: Workshop 1

FIT5145 Week 2 (Workshop 1)

FIT5145: Workshop 1

Preamble

Ensure you have R (4.4.3) and R studio installed!

If you haven't, check Moodle: Week 1/Real Time/Applied Class Resources/Setting up R Studio

We'll also guide you through the process during the lab.

FIT5145: Workshop 1

Activity 1: Being a data scientist (~20 mins)

Objective: Consider the job prospects of data science across countries

  1. Work through describing the job document on moodle (get at least 5 terms)
  2. Add the found information and post it to the Being a data scientist database back on Moodle.

All of these can be found on Moodle: Week 2/Real Time/Applied Class Resources

FIT5145: Workshop 1
  1. Discuss the findings:
  • How does the Aus job market for data science look cf. USA/UK? What do you think may be contributing factors to these differences?
  • What may be some technical challenges when preparing a data science skills report? (Compare scenarios where you manually vs automatically extract text using tools like bs4)
  • Review the entries in the database. How do the skills require relate to the skills listed on the Metromap resource and what general skills are not represented?
FIT5145: Workshop 1

Activity 2: Impact of DS on society (~20 mins)

  1. Do the preconception of the world quiz.
  2. Go over motion charts and learn about the impact on society.
  3. Discuss amongst yourselves:
  • Bubble charts: why are they appropriate for visualising global development issues?
  • Differences between bubble charts and standard business graphics?
  • Limitations of bubble charts for data visualisation?
  • Should we be adding predictions/analytics to bubble charts? Why/why not?
FIT5145: Workshop 1

Lab Activity: Pedestrian activity with R

Objectives

  1. Learn how to use R and R studio
  2. Reading data from a CSV file using tidyverse
  3. Familiarising with data manipulation, such as gather & spread and filter & mutate.

Work through the lab activity under Applied Class

  • Down the data (CSV) and the R markdown file from Moodle
  • Work through the R markdown file and complete the tasks

Let me know if you need help with setting up!

Skills Diversity + range of industries: hotel, games studio, restaurant reservation, grocery shopping, online casino, finance + ethics of customer targetting for gambling + no graph databases + little machine learning or text mining, more tools and output + little data munging (wrangling) Technical Challenges: + data science (as a field, as a job) has varied definitions + use of Mechanical Turk could cause trouble because of this + manual extraction time consuming Comparing job markets: + normalised numbers for AU lower than US and UK (2-4 times) + data scientist salaries hard to get + AU salaries varied over time (smaller data set) + data incomplete + AU a smaller market DS Job trends + language between recruiters very different + OpenCalais had noticable errors