Coursera - Process Mining: Data science in Action

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Coursera - Process Mining: Data science in Action (Size: 1.68 GB)
 4 - 2 - Lecture 3.2- On The Representational Bias of Process Mining (17 min.).mp436.77 MB
 5 - 3 - Lecture 4.3- Introduction to Conformance Checking (12 min.).mp424.94 MB
 5 - 6 - Lecture 4.6- Token Based Replay- Some Examples (15 min.).mp428.55 MB
 6 - 9 - Lecture 5.9- Refined Process Mining Framework (11 min.).mp425.37 MB
 6 - 5 - Lecture 5.5- Mining Social Networks (17 min.).mp437.42 MB
 6 - 2 - Lecture 5.2- Mining Decision Points (17 min.).mp436.14 MB
 4 - 5 - Lecture 3.5- Learning Dependency Graphs (21 min.).mp434.91 MB
 5 - 4 - Lecture 4.4- Conformance Checking Using Causal Footprints (10 min.).mp419.62 MB
 6 - 6 - Lecture 5.6- Organizational Mining (9 min.).mp419.47 MB
 4 - 4 - Lecture 3.4- Dependency Graphs and Causal Nets (21 min.).mp443.6 MB
 7 - 1 - Lecture 6.1- Operational Support- Detect, Predict and Recommend (17 min.).mp435.87 MB
 3 - 4 - Lecture 2.4- Transition Systems and Petri Net Properties (21 min.).mp441.71 MB
 7 - 3 - Lecture 6.3- Guidelines for Logging (10 min.).mp424.59 MB
 3 - 5 - Lecture 2.5- Workflow Nets and Soundness (17 min.).mp434.66 MB
 3 - 3 - Lecture 2.3- Petri Nets (2-2) (18 min.).mp436.69 MB
 5 - 1 - Lecture 4.1- Two-Phase Process Discovery And Its Limitations (15 min.).mp434.32 MB
 6 - 1 - Lecture 5.1- About the Last Two Weeks of This Course (10 min.).mp427.54 MB
 4 - 6 - Lecture 3.6- Learning Causal nets and Annotating Them (18 min.).mp439.81 MB
 6 - 3 - Lecture 5.3- Discovering Data Aware Petri Nets (12 min.).mp426.25 MB
 5 - 7 - Lecture 4.7- Aligning Observed and Modeled Behavior (18 min.).mp441.54 MB
 4 - 7 - Lecture 3.7- Learning Transition Systems (15 min.).mp432.82 MB
 2 - 4 - Lecture 1.4- Learning Decision Trees (27 min.).mp452.25 MB
 6 - 4 - Lecture 5.4- Mining Bottlenecks (11 min.).mp426.23 MB
 2 - 2 - Lecture 1.2- Different Types of Process Mining (21 min.).mp445.21 MB
 5 - 2 - Lecture 4.2- Alternative Process Discovery Techniques (23 min.).mp449.36 MB
 7 - 8 - Lecture 6.8- Process Models as Maps (12 min.).mp428.24 MB
 6 - 7 - Lecture 5.7- Combining Different Perspectives (13 min.).mp425.42 MB
 5 - 5 - Lecture 4.5- Conformance Checking Using Token-Based Replay (15 min.).mp431.75 MB
 3 - 8 - Lecture 2.8- Introducing ProM and Disco (25 min.).mp451.59 MB
 2 - 7 - Lecture 1.7- Cluster Analysis (13 min.).mp427.94 MB

Description

Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems). Example applications include: analyzing treatment processes in hospitals, improving customer service processes in a multinational, understanding the browsing behavior of customers using a booking site, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine. All of these applications have in common that dynamic behavior needs to be related to process models. Hence, we refer to this as "data science in action".

The course explains the key analysis techniques in process mining. Participants will learn various process discovery algorithms. These can be used to automatically learn process models from raw event data. Various other process analysis techniques that use event data will be presented. Moreover, the course will provide easy-to-use software, real-life data sets, and practical skills to directly apply the theory in a variety of application domains.

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Coursera - Process Mining: Data science in Action