Coursera - Process Mining: Data science in Action

1. 4 - 2 - Lecture 3.2- On The Representational Bias of Process Mining (17 min.).mp436.77MB
2. 5 - 3 - Lecture 4.3- Introduction to Conformance Checking (12 min.).mp424.94MB
3. 5 - 6 - Lecture 4.6- Token Based Replay- Some Examples (15 min.).mp428.55MB
4. 6 - 9 - Lecture 5.9- Refined Process Mining Framework (11 min.).mp425.37MB
5. 6 - 5 - Lecture 5.5- Mining Social Networks (17 min.).mp437.42MB
6. 6 - 2 - Lecture 5.2- Mining Decision Points (17 min.).mp436.14MB
7. 4 - 5 - Lecture 3.5- Learning Dependency Graphs (21 min.).mp434.91MB
8. 5 - 4 - Lecture 4.4- Conformance Checking Using Causal Footprints (10 min.).mp419.62MB
9. 6 - 6 - Lecture 5.6- Organizational Mining (9 min.).mp419.47MB
10. 4 - 4 - Lecture 3.4- Dependency Graphs and Causal Nets (21 min.).mp443.6MB
11. 7 - 1 - Lecture 6.1- Operational Support- Detect, Predict and Recommend (17 min.).mp435.87MB
12. 3 - 4 - Lecture 2.4- Transition Systems and Petri Net Properties (21 min.).mp441.71MB
13. 7 - 3 - Lecture 6.3- Guidelines for Logging (10 min.).mp424.59MB
14. 3 - 5 - Lecture 2.5- Workflow Nets and Soundness (17 min.).mp434.66MB
15. 3 - 3 - Lecture 2.3- Petri Nets (2-2) (18 min.).mp436.69MB
16. 5 - 1 - Lecture 4.1- Two-Phase Process Discovery And Its Limitations (15 min.).mp434.32MB
17. 6 - 1 - Lecture 5.1- About the Last Two Weeks of This Course (10 min.).mp427.54MB
18. 4 - 6 - Lecture 3.6- Learning Causal nets and Annotating Them (18 min.).mp439.81MB
19. 6 - 3 - Lecture 5.3- Discovering Data Aware Petri Nets (12 min.).mp426.25MB
20. 5 - 7 - Lecture 4.7- Aligning Observed and Modeled Behavior (18 min.).mp441.54MB
21. 4 - 7 - Lecture 3.7- Learning Transition Systems (15 min.).mp432.82MB
22. 2 - 4 - Lecture 1.4- Learning Decision Trees (27 min.).mp452.25MB
23. 6 - 4 - Lecture 5.4- Mining Bottlenecks (11 min.).mp426.23MB
24. 2 - 2 - Lecture 1.2- Different Types of Process Mining (21 min.).mp445.21MB
25. 5 - 2 - Lecture 4.2- Alternative Process Discovery Techniques (23 min.).mp449.36MB
26. 7 - 8 - Lecture 6.8- Process Models as Maps (12 min.).mp428.24MB
27. 6 - 7 - Lecture 5.7- Combining Different Perspectives (13 min.).mp425.42MB
28. 5 - 5 - Lecture 4.5- Conformance Checking Using Token-Based Replay (15 min.).mp431.75MB
29. 3 - 8 - Lecture 2.8- Introducing ProM and Disco (25 min.).mp451.59MB
30. 2 - 7 - Lecture 1.7- Cluster Analysis (13 min.).mp427.94MB
31. 2 - 5 - Lecture 1.5- Applying Decision Trees (21 min.).mp439.14MB
32. 2 - 6 - Lecture 1.6- Association Rule Learning (18 min.).mp439.69MB
33. 2 - 1 - Lecture 1.1- Data Science and Big Data (17 min.).mp438.07MB
34. 7 - 7 - Lecture 6.7- Mining Spaghetti Processes (8 min.).mp419.31MB
35. 6 - 8 - Lecture 5.8- Comparative Process Mining Using Process Cubes (13 min.).mp427.15MB
36. 7 - 2 - Lecture 6.2- Getting the Right Event Data (17 min.).mp435.21MB
37. 3 - 2 - Lecture 2.2- Petri Nets (1-2) (16 min.).mp433.85MB
38. 7 - 6 - Lecture 6.6- Mining Lasagna Processes (6 min.).mp412.71MB
39. 2 - 8 - Lecture 1.8- Evaluating Mining Results (15 min.).mp432MB
40. 4 - 3 - Lecture 3.3- Business Process Model and Notation (BPMN) (15 min.).mp431.18MB
41. 7 - 5 - Lecture 6.5- How to Conduct a Process Mining Project (11 min.).mp424.55MB
42. 3 - 7 - Lecture 2.7- Alpha Algorithm- Limitations (23 min.).mp449.37MB
43. 5 - 8 - Lecture 4.8- Exploring Event Data (21 min.).mp442.3MB
44. 2 - 3 - Lecture 1.3- How Process Mining Relates to Data Mining (20 min.).mp448.41MB
45. 3 - 1 - Lecture 2.1- Event Logs and Process Models (14 min.).mp430.68MB
46. 4 - 8 - Lecture 3.8- Using Regions to Discover Concurrency (18 min.).mp439.48MB
47. 7 - 9 - Lecture 6.9- Data Science in Action (9 min.).mp422.77MB
48. 4 - 1 - Lecture 3.1- Four Quality Criteria For Process Discovery (19 min.).mp441.43MB
49. 3 - 6 - Lecture 2.6- Alpha Algorithm- A Process Discovery Algorithm (25 min.).mp453.69MB
50. 7 - 4 - Lecture 6.4- Process Mining Software (16 min.).mp434.71MB