Coursera - Process Mining: Data science in Action

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Added on April 2, 2016 by nex_xenin Other > Tutorials
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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.).mp4	36.77 MB
		5 - 3 - Lecture 4.3- Introduction to Conformance Checking (12 min.).mp4	24.94 MB
		5 - 6 - Lecture 4.6- Token Based Replay- Some Examples (15 min.).mp4	28.55 MB
		6 - 9 - Lecture 5.9- Refined Process Mining Framework (11 min.).mp4	25.37 MB
		6 - 5 - Lecture 5.5- Mining Social Networks (17 min.).mp4	37.42 MB
		6 - 2 - Lecture 5.2- Mining Decision Points (17 min.).mp4	36.14 MB
		4 - 5 - Lecture 3.5- Learning Dependency Graphs (21 min.).mp4	34.91 MB
		5 - 4 - Lecture 4.4- Conformance Checking Using Causal Footprints (10 min.).mp4	19.62 MB
		6 - 6 - Lecture 5.6- Organizational Mining (9 min.).mp4	19.47 MB
		4 - 4 - Lecture 3.4- Dependency Graphs and Causal Nets (21 min.).mp4	43.6 MB
		7 - 1 - Lecture 6.1- Operational Support- Detect, Predict and Recommend (17 min.).mp4	35.87 MB
		3 - 4 - Lecture 2.4- Transition Systems and Petri Net Properties (21 min.).mp4	41.71 MB
		7 - 3 - Lecture 6.3- Guidelines for Logging (10 min.).mp4	24.59 MB
		3 - 5 - Lecture 2.5- Workflow Nets and Soundness (17 min.).mp4	34.66 MB
		3 - 3 - Lecture 2.3- Petri Nets (2-2) (18 min.).mp4	36.69 MB
		5 - 1 - Lecture 4.1- Two-Phase Process Discovery And Its Limitations (15 min.).mp4	34.32 MB
		6 - 1 - Lecture 5.1- About the Last Two Weeks of This Course (10 min.).mp4	27.54 MB
		4 - 6 - Lecture 3.6- Learning Causal nets and Annotating Them (18 min.).mp4	39.81 MB
		6 - 3 - Lecture 5.3- Discovering Data Aware Petri Nets (12 min.).mp4	26.25 MB
		5 - 7 - Lecture 4.7- Aligning Observed and Modeled Behavior (18 min.).mp4	41.54 MB
		4 - 7 - Lecture 3.7- Learning Transition Systems (15 min.).mp4	32.82 MB
		2 - 4 - Lecture 1.4- Learning Decision Trees (27 min.).mp4	52.25 MB
		6 - 4 - Lecture 5.4- Mining Bottlenecks (11 min.).mp4	26.23 MB
		2 - 2 - Lecture 1.2- Different Types of Process Mining (21 min.).mp4	45.21 MB
		5 - 2 - Lecture 4.2- Alternative Process Discovery Techniques (23 min.).mp4	49.36 MB
		7 - 8 - Lecture 6.8- Process Models as Maps (12 min.).mp4	28.24 MB
		6 - 7 - Lecture 5.7- Combining Different Perspectives (13 min.).mp4	25.42 MB
		5 - 5 - Lecture 4.5- Conformance Checking Using Token-Based Replay (15 min.).mp4	31.75 MB
		3 - 8 - Lecture 2.8- Introducing ProM and Disco (25 min.).mp4	51.59 MB
		2 - 7 - Lecture 1.7- Cluster Analysis (13 min.).mp4	27.94 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