Unit 5 · Data and cybersecurity¶
In a city-builder game you never steer the city by looking at the buildings. You switch on the info views: the traffic overlay, the pollution map, the happiness bars. Those overlays are data, and without them you are flying blind. But a city with great overlays and no police, no walls and no locks on the doors will not last long either. This unit is about both halves: what data are worth to a company, how they live and die, how to get answers out of them, where to keep them safe, and which laws say how they must be protected.
Every session is hands-on. You will download a real open dataset and take it from a question to a chart, design a backup plan, run a passive security check of a real public website, and decide which data-protection and cybersecurity laws apply to your project company. Everything feeds Deliverable 5.
| Learning outcome | Hours | Term |
|---|---|---|
| RA5 — Assesses the importance of data, and of their protection in a globalised digital economy, defining security and cybersecurity systems both at device or system level and globally. | 5 | 3rd |
What you will learn
- Tell data from information, and follow one dataset of your company through its lifecycle, from capture to deletion
- Explain what makes something big data (the V's) and how big data, data analysis, machine learning, deep learning and AI fit together
- Follow the stages of data science on a real open dataset, from a question to a chart and a conclusion
- Choose where a company keeps its data in the cloud and design a 3-2-1 backup plan
- Run a passive security check of a public website: security headers, cookies before consent, privacy policy
- Decide whether the RGPD, the LOPDGDD and NIS2 apply to a company, and why
Lessons¶
| # | Lesson | What it covers |
|---|---|---|
| 1 | Data, information and lifecycle | Data vs information, the six stages of the data lifecycle, reading a real privacy policy |
| 2 | Big data | The five V's, structured and unstructured data, big data vs data analysis vs ML vs AI |
| 3 | Data science and the cloud | The stages of data science on a real Ibestat dataset, cloud storage, the 3-2-1 backup rule |
| 4 | Cybersecurity and regulation | Confidentiality, integrity, availability; device, system and global levels; a passive website audit; RGPD, LOPDGDD and NIS2 |
| 5 | Deliverable 5 · Data and security brief | The brief, the sections, the rubric and the individual defence |
What you are graded on¶
The nine assessment criteria of the unit's learning outcome, the lessons that train each one and the section of Deliverable 5 where you prove it.
| Criterion | Official wording, in English | Lessons | D5 section |
|---|---|---|---|
| a) | The difference between data and information has been established. | Data, information and lifecycle | A |
| b) | The data lifecycle has been described. | Data, information and lifecycle | A |
| c) | The relationship between big data, data analysis, machine learning, deep learning and artificial intelligence has been identified. | Big data | B |
| d) | The characteristics that define big data have been described. | Big data | B |
| e) | The typical stages of data science and how they relate in the process have been described. | Data science and the cloud | B |
| f) | The procedures for storing data in the cloud have been described. | Data science and the cloud | C |
| g) | The importance of cloud computing has been described. | Data science and the cloud | C |
| h) | The main objectives of data science in different companies have been identified. | Big data · Data science and the cloud | B |
| i) | The importance of security and its regulation in relation to data has been assessed. | Cybersecurity and regulation | D and group part |
How marks work is explained on The project page: the deliverable is 70% of the RA mark (your own section, your defence and the group part) and the class activities of each session are the other 30%. There is no written exam. The datasets for the sessions are on the Resources page.