Programming for Data Analytics

This is an online document to support students studying Programming for Data Analytics at UNSW Sydney. If you are reading this and you are enrolled in this course, wonderful, you are in the right place! If not, you may consider taking this course with us.

Outline

• Course Topics
• Required Readings

Course Topics

NOTE: Plans for future weeks are subject to change.

Weeks 1-3

• (1.1) Introductory Housekeeping
• (1.2) Welcome to the Command Line
• (1.3) Command Line Basics
• (1.4) Command Line Scripting
• (1.5) Python Syntax Essentials
• (1.6) Data Structures and File Formats
• (1.7) Project Management for Data Analytics
• (1.8) The Business of Programming for Data Analytics
• (1.9) Additional Python Concepts

Weeks 4-7

• (2.1) Web Publishing Fundamentals
• (2.2) Python for Web Publishing
• (2.3) Web-Based Geographical Visualisations
• (2.4) Web Scraping Concepts
• (2.5) BeautifulSoup
• (2.6) The Ethics of Web Scraping

Weeks 8-10

• (3.1) Machine Learning Concepts
• (3.2) Machine Learning Models
• (3.3) Machine Learning in Python
• (3.4) The Social and Ethical Issues of Programming for Data Analytics

Required Readings

Readings 1, 5, 6, 8 & 9 are articles that will require a bit more attention. Readings 2, 3, 4 & 7 are videos for relaxing and entertaining learning. It is expected that you complete all readings before your final exam.

1. Schäfer et al. (2018), “Synthesizing CRISP-DM and Quality Management: A Data Mining Approach for Production Processes”, IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD), doi: 10.1109/ITMC.2018.8691266

2. Thé (2016), HTML, CSS, JavaScript Explained [in 4 minutes for beginners], https://www.youtube.com/watch?v=gT0Lh1eYk78

3. Vox (2016), Why All World Maps Are Wrong, https://www.youtube.com/watch?v=kIID5FDi2JQ

4. Real Life Lore (2018), The Coastline Paradox Explained, https://www.youtube.com/watch?v=kFjq8PX6F7I

5. DeVito et al. (2020), “How we learnt to stop worrying and love web scraping”, Nature Career Column, 8 September, doi: 10.1038/d41586-020-02558-0

6. Krotov and Silva (2018), “Legality and Ethics of Web Scraping”, paper published at the Americas Conference on Information Systems, New Orleans (USA).

7. CGP Grey (2017), How Machines Learn, https://www.youtube.com/watch?v=R9OHn5ZF4Uo

8. Marjanovic, O., Cecez-Kecmanovic, D., & Vidgen, R. (2021). “Algorithmic pollution: Making the invisible visible“. Journal of Information Technology, doi: 10.1177/02683962211010356

9. Riemer, K., & Peter, S. (2021). “Algorithmic Audiencing: Why we need to rethink free speech on social media“. Journal of Information Technology, doi: 10.1177/02683962211013358