Study checklist
Build a 10-question checklist to judge a study or a figure, and apply it to three examples.
Non-technical managers who lead data or AI projects.
Understand statistics and AI, decide under uncertainty, organise an analysis workflow and handle data ethics and security.
Skill labels as listed on Coursera.
Stated prerequisites: No previous knowledge of data, statistics or AI necessary
| Course | Level | Length | Rating (reviews) |
|---|---|---|---|
| How to Describe Data | Beginner | 9 h | 4.7 (29) |
| How Science Turns Data Into Knowledge | Beginner | 11 h | 4.7 (18) |
| Decoding AI: A Deep Dive into AI Models and Predictions | Beginner | 10 h | 4.7 (29) |
Stated prerequisites: None stated (no coding required)
| Course | Level | Length | Rating (reviews) |
|---|---|---|---|
| Model Thinking | Intermediate | 28 h | 4.8 (2,265) |
| Problem Solving Using Computational Thinking | Beginner | 12 h | 4.6 (1,313) |
| Mindware: Critical Thinking for the Information Age | Beginner | 13 h | 4.8 (1,453) |
| Leveraging GenAI to Develop Critical Thinking Skills | Beginner | 6 h | 4.3 (11) |
Stated prerequisites: Basic familiarity with computers and internet search
| Course | Level | Length | Rating (reviews) |
|---|---|---|---|
| AI-Powered Data Analysis: A Practical Introduction | Beginner | 5 h | 4.5 (30) |
| Systems Thinking for AI & Automation | Beginner | 4 h | — |
| Rethink How You Decide with AI | Beginner | 5 h | — |
Stated prerequisites: Some R programming
| Course | Level | Length | Rating (reviews) |
|---|---|---|---|
| Data Science as a Field | Intermediate | 11 h | 4.3 (41) |
| Ethical Issues in Data Science | Beginner | 24 h | 4.8 (53) |
| Cybersecurity for Data Science | Beginner | 19 h | 3.6 (36) |
| Fundamentals of Data Visualization | Intermediate | 15 h | 4.6 (44) |
This path takes 172 hours, about 4 months at 10 hours a week. A Coursera Plus subscription gives access to most of its courses during that time.
Build a 10-question checklist to judge a study or a figure, and apply it to three examples.
Model a real team decision with probabilities and expected values.
List the tasks in your team that AI can speed up, with gains and risks.
Write a one-page charter on data ethics, privacy and security.