Readings

Most of the course materials (slide decks, session recordings, exercises) are linked from the schedule.

However, weekly readings are not linked to the schedule and can be found below. If the link takes you to the Temple library, search for the article title.

Module 1 and 2

Week 2

• Dhar – 2013 – Data science and prediction
• Rock – 2018 – A hypothesis can’t be right unless it can be proven wrong
• Wikipedia – Survivorship bias
• Ziemann et al. – 2016 – Gene name errors are widespread in the scientific literature

Week 3

• Ball – 2018 – ‘News’ spreads faster and more widely when it’s false
• Fenkel – 2021 – The Most Influential Spreader of Coronavirus Misinformation Online
• Hayes – 2013 – In Data We Trust
• Kaplan et al. – 2021 – Is Confirmation Bias Guiding COVID Vaccine Recommendations?
• Open Data Handbook – Why Open Data?

Week 4

• Unwin – 2008 – Handbook of Data Visualization chapter

Week 5

• Davenport – 2013 – Telling a story with data
• Ritchie – What Is an Infographic?

Module 3

Week 7

• Moss – 2021 – Five Times Excel Led to Disaster
• Redman – 2013 – Data’s Credibility Problem
• Rosenblum and Dorsey – 2014 – Knowing Just Enough about Relational Databases
• Tableau – Guide to data cleaning

Week 8

• IBM – 2020 – What is ETL (Extract, Transform, Load)?

Week 9

• Aaltonen – 2013 – The beauty and perils of metrics
• Schambra – 2013 – The Tyranny of Success

Module 4

Week 11

• CFI – Pivot Table Guide
• Durcevic – 2020 – Move Beyond Excel, PowerPoint & Static Business Reporting with Powerful Interactive Dashboards

Week 12

• Feldman – 2013 – Techniques and applications for sentiment analysis
• Grubbs et al. – 2020 – Understanding Political Twitter
• Hanna et al. – 2021 – What is unstructured data?

Week 13

• Bertolucci 2013 – Big Data Analytics
• Model Training with Machine Learning – Data Science Primer
• UNSW – 2020 – Descriptive, Predictive, Prescriptive Analytics