인프런 영문 브랜드 로고
인프런 영문 브랜드 로고
Data Science

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Data Analysis

Process Mining for Everyone

This course is for those who are new to process mining or are not familiar with it. Those who take this course will learn the process mining theory and various analysis techniques and know-how required for practical analysis.

(4.7) 38 reviews

2,308 students

Process Mining
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This course is prepared for Beginners.

What you will learn!

  • Basic concepts of process mining

  • Structure of event logs and how to extract data

  • Hands-on training using Disco software

  • Buying Process Analysis

  • How to handle incomplete cases

  • Ways to Simplify Process Complexity

  • Various analysis questions and cases

Process Mining for Everyone

Process mining is a cutting-edge data analysis technology that focuses on processes. This technology allows us to visualize the actual process flow, allowing us to quickly and objectively understand the overall picture of the process. We can discover problem areas such as bottlenecks or rework, clarify our intuitions or prove them wrong. We can also select areas for process automation for the introduction of RPA (Robotic Process Automation). After process changes and automation are made, we can verify whether these changes and automations are actually effective. And we can gain insights that help develop optimization models using machine learning or deep learning. For this reason, leading domestic and foreign companies such as Volkswagen, Rabobank, Prudential, and Kakao M are actively utilizing process mining.

With the advent of the 4th industrial revolution, process mining is now becoming an essential competency for practical experts such as data analysts, business analysts, IT managers, field workers, auditors, and college students who want to get a job in related fields. This course is for those who are new to process mining or are not familiar with it. Those who take this course can learn the process mining theory required for practical analysis, various analysis techniques (such as deriving processes from various perspectives, complex process analysis methods, and incomplete case processing techniques) and know-how. Become a process mining expert through this course.

Learning Objectives

  • Learn the process mining theory required for practical analysis.
  • Learn various process mining analysis techniques that can be used in practical analysis.
  • Develop process mining capabilities to uncover insights for improvement.

Helpful people

  • RPA (Robotic Process Automation) related person or consultant
  • Data analysis specialists in deep learning, machine learning, and data mining
  • Person in charge or consultant for process innovation such as ERP and Lean
  • Online marketers interested in customer journey analytics
  • Process Manager
  • Person in charge of IT department
  • Person in charge or consultant related to gratitude
  • College and graduate students interested in fields related to data analysis, etc.

Note

  • We provide hands-on training using Disco, a process mining S/W that has been purchased and used by over 160 companies around the world and successfully utilized in thousands of projects. You can use the four provided datasets for practice without applying for a separate license.

Introducing the knowledge sharer

Lee Bo-kyung
The main lecturer of this course, Dr. Bo-Kyung Lee, is the chief Process Miner at PMIG, a process mining company. She co-wrote “Process Mining for Managers and Professionals,” which was selected as an excellent book by the National Academy of Sciences in 2017, and has carried out process mining projects for the National Election Commission, etc.

Recommended for
these people!

Who is this course right for?

  • People interested in process mining

  • People who dream of becoming data analysts

  • People who want to improve business processes

  • People who want to prepare for the 4th industrial revolution

  • People who want practical training

Hello
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4,121

Students

66

Reviews

6

Answers

4.7

Rating

2

Courses

Curriculum

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25 lectures ∙ (4hr 17min)

Course Materials:

Lecture resources
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