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Business has become more challenging day by day. The traditional methods and rules are replaced by the by Artificial Intelligence. One of the most challenging parts of this knowledge-oriented world is to manage knowledge.

The [course_title] course teaches the process of knowledge management and Big data management in Business. Throughout the course, you will learn how knowledge is captured, elicited, organised and created in the business. The course teaches you about big data and shows how you can use data analytics from a laymen perspective. The techniques of mining knowledge from big data, social problems with big data, cloud computing and cloud services, various case studies will also be illustrated in the course.

Assessment

This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.

Certification

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Course Credit: Hong Kong Polytechnic University

 

Course Curriculum

Module 1
Welcome Video 00:10:00
Team of instructors 00:06:00
Brief Introduction of PLE&N 00:01:00
PLE&N Configuration 00:06:00
Module 1 Intro Video 00:01:00
1.1 Introduction to Knowledge 00:06:00
1.2.1 A Brief History of Knowledge Management 00:11:00
1.2.2 Knowledge-based Economy 00:16:00
1.3.1 Types of Knowledge Processes 00:12:00
1.3.2 Interview with Melina Handriz on KM Framework 00:07:00
1.4.1 Two Main Types of KMS 00:02:00
1.4.2 Soft & Hard KM Tools (People-based & IT-Oriented) 00:01:00
1.4.3 Common Myths of KMS 00:03:00
1.4.4 KM System – Are they Real? 00:01:00
1.4.5 Linking Knowledge Management Technologies to Strategy 00:11:00
1.5.1 Categorizing KMS by Knowledge Processes 00:04:00
1.5.2 Codification & Personalization of KMS 00:04:00
1.5.3 EDMS & Knowledge Repositories 00:14:00
1.5.4 Collaboration 00:10:00
1.5.5 Shortfalls 00:04:00
1.6.1 KM Sharing Tools 00:03:00
1.6.2 Good Practices 00:01:00
1.6.3 Use of Storytelling to elicit Near-Miss Knowledge 00:07:00
1.6.4 Storytelling from Hong Kong Police 00:08:00
1.6.5 Communities of Practice 00:01:00
1.6.6 Knowledge Café 00:01:00
1.6.7 After Action Review 00:02:00
1.7.1 KM Process, Projects and Program 00:08:00
1.7.2 Managing KM Projects 00:07:00
1.7.3 Interview with Nicole Sy on KM Projects and Journey 00:08:00
1.7.4 KM Metrics 00:16:00
1.7.5 Interview with Chief Superintendent Dr. Eric Cheng on Strategic Planning 00:05:00
1.7.6 KM Practices 00:01:00
1.8.1 Relationship between KM and Big Data 00:14:00
1.8.2 Interview with Muhammad Saleem Sumbal on Big Data and Enterprise KM 00:14:00
Module 2
Module 2 Intro Video 00:02:00
Introductory Video on Wiki 00:06:00
2.1.1 Taxonomy 00:17:00
2.1.2 The Angns Company Case Study 00:06:00
2.1.3 Extraction of IC from Annual Reports 00:12:00
2.1.4 Intro to TaxoFolk 00:14:00
2.1.5 Demonstration of the TaxoFolk system 00:06:00
2.2.1 Search Engine 00:20:00
2.2.2 Common Method for Locating Experts 00:06:00
2.3.1 Enterprise Portal 00:16:00
2.3.2 Empty Portal 00:10:00
2.3.3 Case Study – The InTaxon Project 00:10:00
2.3.4 Interview with Major Barry Byrne 00:12:00
2.4 Knowledge Audit for Unstructured Business Process 00:08:00
2.5 Case Study – The K-MISS Project 00:11:00
2.6.1 Interview with Hong Kong Police on KM Journey 00:13:00
2.6.2 Interview with Detective Senior Superintendent Wyman Lee on Detective Plus 00:07:00
2.6.3 Interview with Superintendent Alex Law on Marine Policing 00:08:00
2.6.4 HKPolice – Auxiliary Police – Amy Lee 00:06:00
2.6.5 HKPolice – KM Training – Dr. Chiu 00:03:00
Module 3
00:00
3.1 Intro 00:01:00
3.1.1 What are SMEs 00:06:00
3.1.2 SMEs’ characteristics 00:10:00
3.1.3 Working in Smaller Companies 00:06:00
3.1.4 Why KM in SMEs 00:06:00
3.1.5 KM desicions in SMEs 00:16:00
3.1.6 Interview with Dr Bolisani – KM for SMEs 00:09:00
3.1.7 SME Case Studies 00:05:00
3.2 Intro 00:01:00
3.2.1 K_Challenge Retirement 00:08:00
3.2.2 K_Challenge Knowledge Retention 00:06:00
3.2.3 K_Challenge Knowledge Leakage 00:04:00
3.2.4 K_Challenge Knowledge Loss 00:03:00
3.2.5 K_Challenge Knowledge Risk Management 00:05:00
3.2.6 Interview with Haley on Knowledge Risk 00:07:00
3.3 Using KMS: a taxonomy of SME strategies 00:13:00
Module 4
Module 4 Intro Video 00:03:00
4.1.1 Introduction 00:01:00
4.1.2 Definition 00:04:00
4.1.3 An Analogy 00:03:00
4.1.4 Characteristics of the Cloud 00:03:00
4.1.5 The Concept of “Virtualization” 00:02:00
4.1.6 Internet, Web 2.0 and the Cloud 00:02:00
4.1.7 Animoto company case study 00:04:00
4.2.1 Introduction 00:01:00
4.2.2 Types of cloud services and their benefits 00:12:00
4.2.3 Common types of Cloud 00:04:00
4.2.4 Advantages of cloud-based Knowledge Management Systems 00:09:00
4.2.5 Common cloud applications 00:04:00
4.3.1 Introduction 00:01:00
4.3.2 Cloud for transformation 00:02:00
4.3.3 What is a Knowledge Cloud 00:12:00
4.4.1 Introduction 00:01:00
4.4.2 Knowledge Cloud applications 00:03:00
4.4.3 The Cloud as an intelligent Knowledge Center 00:04:00
4.4.4 Robots and intelligent software 00:01:00
4.4.5 Cloudsourcing 00:08:00
4.4.6 Drawing human intelligence from the cloud 00:03:00
4.5.1 Introduction 00:01:00
4.5.2 Introduction to a Personal Learning Environment & Network (PLE&N) 00:12:00
4.5.3 Results, Benefits and Advantages of the PLE&N 00:08:00
4.5.4 Learners’ Experiences, Sustainability, Latest Development of PLE&N 00:07:00
Module 5
Module 5 Intro Video 00:03:00
5.1.1 Introduction to PKM 00:21:00
5.1.2 Challenges and Tools for PKM 00:25:00
5.1.3 PKM Models 00:13:00
5.2 Applying KM to Project 00:14:00
5.3 Section Intro 00:01:00
5.3.1 Interview with Florian Kragulj – Design Thinking 00:08:00
5.3.2 Interview with Rudolf DSouza Design thinking 00:13:00
5.3.3 Interview with Prof de Bont Design thinking 00:11:00
5.3.4 Research Interview with Nikolina on Design Thinking 00:14:00
5.3.5 Design Thinking Interview in AKF2017 00:01:00
5.4.1 Introduction 00:01:00
5.4.2 Managing Knowledge in the Age of Digitalization 00:19:00
5.4.3 Interview with Dr. Bonnie Cheuk 00:13:00
5.4.4 Interview with Mr. Eric Hunter 00:06:00
5.4.5 Interview with Dr. Wong 00:09:00
5.4.6 Interview with Mr. Colin Farrelly 00:08:00
5.4.7 Interview with Mr John Obrien – Digitalisation 00:22:00
5.4.8 Networked Economy 00:07:00
5.5 Section Intro 00:01:00
5.5.1 Prof. Wilkesman – Organizing Routines or Innovations 00:10:00
5.5.2 Interview with Maurizio on KM&I4.0 00:12:00
5.5.3 Dr. Maurizio Massaro – IC Disclosure and digital communication 00:13:00
Module 6
Module 6 Intro Video 00:02:00
6.1.1 Introduction 00:01:00
6.1.2 What is a Web of document, how to create it and its limitations 00:10:00
6.2.1 Introduction 00:01:00
6.2.2 What is Web of data, structured data and open linked data 00:11:00
6.2.3 Applications of the open linked data cloud 00:06:00
6.2.4 Interview with Professor Klaus Tochtermann – Clarifications on questions posed in forum 00:06:00
6.2.5 Interview with Kim Salkeld on Open Data in Hong Kong 00:10:00
6.3.1 Introduction 00:01:00
6.3.2 Library use of the Web of data – the Econbiz system 00:05:00
6.3.3 Library use of Web of data – the EconStor system 00:05:00
6.4.1 Introduction 00:01:00
6.4.2 Social Web and Social Media Tools 00:09:00
6.5.1 Introduction 00:02:00
6.5.2 Sentiment analysis and social media monitoring tools 00:12:00
6.5.3 Social Media Strategy and Demonstration of Sentiment Analysis 00:10:00
6.6 Interview with Professor Klaus Tochtermann – Introduction to semantic technology 00:07:00
6.7.1 Introduction 00:01:00
6.7.2 Science 2.0 and its impacts 00:16:00
Module 7 (Part 1)
Module 7 Intro Video 00:13:00
7.1.1 Introduction 00:01:00
7.1.2 Data Mining Overview 00:24:00
7.2.1 Introduction 00:01:00
7.2.2 The Application lifecycle in On-line Business 00:26:00
7.3.1 Introduction 1 00:01:00
7.3.2 From Basics to OLAP 00:14:00
7.3.3 Introduction 2 00:01:00
7.3.4 Data Mining techniques 00:21:00
7.4.1 Introduction 00:01:00
7.4.2 Classic Data vs. Big Data 00:24:00
7.5.1 Introduction 00:01:00
7.5.2 Principles of Data Governance 00:20:00
Module 7 (Part 2)
7.6.1 Introduction 00:01:00
7.6.2 The Hadoop Stack Ecosystem 00:21:00
7.7.1 Introduction 00:01:00
7.7.2 Analytics & Applications and case studies 00:28:00
7.8.1 Introduction 00:01:00
7.8.2 Advanced Topics in Big Data Analytics 00:19:00
7.9.1 Introduction 00:01:00
7.9.2 Conclusions and Lessons Learned 00:37:00
7.10 Big Data overview (Module Summary and New Frontiers) 00:20:00
End-of-Course Video
End-of-Course Video 00:07:00
Additional Content in PLE&N 00:01:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

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