Data Science 2017-2018
Course schedule
Each week we have a 45 minute lecture and a 45 minute practical session with exercises in R and Python. The homework is either reading a paper or completing an assignment.
- Dataset 1: OpenML speeddating data (link)
- Dataset 2: News and clickbait posts from Facebook
| Week | Lecture | Practical session | Homework (literature/assignment) |
|---|---|---|---|
| 1 | Introduction | Dataset 1: Task definition | Paper 1. “What Educated Citizens Should Know About Statistics and Probability” (2003) |
| 2 | Exploration and visualisation | Dataset 1: data exploration | Paper 2. “The dual frontier: Patented inventions and prior scientific advance ” (2017) |
| 3 | Model learning 1 | Dataset 1: model learning | Paper 3. “Machine learning: Trends, perspectives, and prospects” (2015) |
| 4 | Evaluation 1 | Dataset 1: evaluation | Literature assignment |
| 5 | Big data | Dataset 2: data exploration | Paper 4: “The Parable of Google Flu- Traps in Big Data Analysis” (2014) |
| 6 | Data collection | Dataset 2: data annotation | Practical assignment 1: Inter-rater agreement |
| 7 | Pre-processing & Feature extraction 1 | Dataset 2: data pre-processing | Paper 5: “Crawling Facebook for Social Network Analysis Purposes” (2011) |
| 8 | Pre-processing & Feature extraction 2 | Dataset 2: feature extraction | Practical assignment 2: Feature extraction |
| 9 | Model learning 2 | Dataset 2: model learning | Paper 6: “Developing Age and Gender Predictive Lexica over Social Media” (2014) |
| 10 | Evaluation 2 | Dataset 2: evaluation & reporting | Practical assignment 3: Evaluation |
| 11 | Analysis and dicussion | Dataset 2: error analysis | Paper 7: “Exploring the Query Halo Effect in Site Search- Leading People to Longer Queries” (2017) |
| 12 | Feature extraction 3 | Dataset 2: reporting | Final assignment |
| 13 | Ethical issues | Q&A |
The assessment of the course consists of a written exam (60% of course grade) and practical assignments (40% of course grade). The practical assignments comprise four small tasks (5% each) and one more substantial report (20%). The grade for the written exam should be 5.5 or higher in order to complete the course. The average grade for the practical assignments should be 5.5 or higher in order to complete the course. If one of the tasks is not submitted the grade for that task is 0.