Big Data course and Learning Model for Online
education (LMO)
at the Laureate Online Education(University of Liverpool)

Yuri Demchenko (University of Amsterdam)
Emanuel Gruengard (Laureate Online Education)
RDA EDISON Workshop
21 September 2014, Amsterdam

Outline• Need for professional education in Big Data
• Big Data definition and Big Data Architecture
Framework (BDAF)• Common Body of Knowledge in Big Data
• Collaborative Online Learning Model Principles at
Laureate Online Education (LOE)• Big Data and Data Analytics Course• Bloom’s Taxonomy and Andragogy• Summary and next steps

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Professional Education Objectives
An effective professional education needs to provide for theprofessional level of knowledge to achieve the following
1) Master basic concepts and major application areas
2) Compare similar concepts (and concepts inter-relation) andalternatives, as well as application specific areas
3) Appraise basic technologies and their relation to the basicconcepts• Challenges due to Big Data is very wide technology domain– Comparing to still narrower Cloud Computing• New types of skills in Big Data– Analytics and research methods
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Improved: 6 (5+1) V’s of Big Data
VolumeVariety••••••Structured
Unstructured
Multi-factor
Probabilistic
LinkedDynamic••••

Terabytes
Records/Arch
Tables, Files
Distributed••••

Adopted in general
by NIST BD-WG
BDDAC2014 @CTS2014

Batch
Real/near-time
Processes
Streams

6 Vs ofBig Data

• Changing data
• Changing model• Linkage

Variability

Velocity••••
•

•
•••

Value

Correlations
Statistical
EventsHypothetical

Trustworthiness
Authenticity
Origin, Reputation
Availability
AccountabilityGeneric Big DataProperties• Volume• Variety• Velocity

Acquired Properties(after entering system)• Value• Veracity• Variability
Commonly accepted
3V’s of Big DataVeracity
Big Data Architecture Framework

4Big Data Definition: From 6V to 5 Parts (1)(1) Big Data Properties: 5V– Volume, Variety, Velocity, Value, Veracity– Additionally: Data Dynamicity (Variability)(2) New Data Models– Data linking, provenance and referral integrity– Data Lifecycle and Variability/Evolution(3) New Analytics–Real-time/streaming analytics, interactive and machine learning analytics(4) New Infrastructure and Tools––––

High performance Computing, Storage, Network
Heterogeneous multi-provider services integration
New Data Centric (multi-stakeholder) service models
New Data Centric security models for trusted infrastructure and data processing
and storage(5) Source and Target– High velocity/speed data capture from variety of sensors and data sources– Data delivery to different visualisation and actionable systems and consumers– Full digitised input and output, (ubiquitous) sensor networks, full digital control
BDDAC2014 @CTS2014

Big Data Architecture Framework

5Big Data Definition: From 6V to 5 Parts (2)Refining Gartner definition“Big data is (1) high-volume, high-velocity and high-variety information assets thatdemand (3) cost-effective, innovative forms of information processing for (5)enhanced insight and decision making”•Big Data (Data Intensive) Technologies are targeting to process (1) high-volume,
high-velocity, high-variety data (sets/assets) to extract intended data value andensure high-veracity of original data and obtained information that demand costeffective, innovative forms of data and information processing (analytics) forenhanced insight, decision making, and processes control; all of those demand(should be supported by) new data models (supporting all data states and stages
during the whole data lifecycle) and new infrastructure services and tools thatallows also obtaining (and processing data) from a variety of sources (includingsensor networks) and delivering data in a variety of forms to different data andinformation consumers and devices.
(1) Big Data Properties: 5V(2) New Data Models(3) New Analytics(4) New Infrastructure and Tools(5) Source and Target

BDDAC2014 @CTS2014

Big Data Architecture Framework

6Big Data Architecture Framework (BDAF)(1) Data Models, Structures, Types– Data formats, non/relational, file systems, etc.

(2) Big Data Management– Big Data Lifecycle (Management) Model• Big Data transformation/staging– Provenance, Curation, Archiving(3) Big Data Analytics and Tools– Big Data Applications• Target use, presentation, visualisation(4) Big Data Infrastructure (BDI)– Storage, Compute, (High Performance Computing,) Network– Sensor network, target/actionable devices– Big Data Operational support(5) Big Data Security– Data security in-rest, in-move, trusted processing environments
BDDAC2014 @CTS2014

Big Data Architecture Framework

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Big Data Infrastructure and Analytics Tools
Big Data Infrastructure• Heterogeneous multi-provider
inter-cloud infrastructure• Data management
infrastructure• Collaborative Environment(user/groups managements)• Advanced high performance(programmable) network• Security infrastructure

Big Data Analytics• High Performance Computer
Clusters (HPCC)• Analytics/processing: Realtime, Interactive, Batch,Streaming• Big Data Analytics tools andapplications
BDDAC2014 @CTS2014

Big Data Architecture Framework

8Data Lifecycle/Transformation Model

Data Model (1)Data Model (1)Data Model (3)Data Model (4)Data (inter)linking?
•PID/OID•
ORCID•
Identification•
Privacy, Opacity

Data Storage

DataCollection&Registration

Data
Filter/Enrich,Classification

Data
Analytics,
Modeling,
Prediction

DataDelivery,
Visualisation

Consumer
Data Analitics
Application

DataSource

Common Data Model?
•Data Variety and Variability•
Semantic Interoperability

Data repurposing,Analitics re-factoring,
Secondary processing••

Does Data Model changes alonglifecycle or data evolution?
Identifying and linking data

BDDAC2014 @CTS2014––––

Persistent identifiers
Data ownershipTraceability vs Opacity
Referral integrity

Big Data Architecture Framework

9Big Data and Data Science Skill Taxonomy• Data Science Competencies Taxonomy by HPC University
http://hpcuniversity.org/educators/competencies/– Undergraduate Level Computational Science Competencies– Graduate Level Computational Science Competencies– Basic Data Driven Science Competencies– Advanced Data Driven Science Competencies• Analysing the Analysers. O’Reilly Strata Survey – Harris,
Murphy & Vaisman, 2013
• The task of RDA IG on Education and Training

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10Analysing the Analysers. O’Reilly Strata Survey(2013)

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11Common Body of Knowledge (CBK)in Big Data and Data Intensive Technologies
CBK refers to several domains or operational categories into which Big Data theory andpractices breaks down
• The scope is very wide, need to combine few previously not connected domains• This is one of attempts verified by practical course development
CBK Big Data and Data Intensive Technologies
1.
Big Data Definition and Big Data Architecture Framework, Data driven and data centric
applications model, Stakeholders and Roles
2.
Big Data use cases and application domains taxonomy and requirements, Big Data inindustry and science
3.
Data structures, SQL and NoSQL databases
4.
Data Analytics Methods and Tools, Knowledge Presentation
5.
Big Data Management and curation, Big Data Lifecycle, Data Preservation and Sharing,
Enterprise Data Warehouses, Agile Data Driven Enterprise
6.
Cloud based Big Data infrastructure and computing platforms, Data Analytics applicationand new Data Scientist skills required
7.
Computing models: High Performance Computing (HPC), Massively Parallel Computing(MPP), Grid, Cluster Computing
8.
Big Data Security and Privacy, Certification and Compliance
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Big Data Course at LOE/UoL Structure
Seminar 1: Introduction. Big Data technology domain definition, Big Data Architecture
Framework
Seminar 2: Big Data use cases from science, industry and business
Seminar 3: Big Data Infrastructure components and platforms, Enterprise Data Warehouses,
MapReduce and Hadoop, distributed file systems and database architectures, data structures,
NoSQL databases.
Seminar 4: Big Data analytic techniques, introduction to RapidMiner. Statistical techniques formodeling data.
Seminar 5: Processes behind Big Data Analytics: Rule Extraction Algorithms and ClusterAnalysis, Decision tree induction.
Seminar 6: Classification and forecasting techniques: Machine Learning, Neural Networks andSupport Vector Machines/ Measurement techniques: Receiver Operating Curves and GainsCharts.
Seminar 7: Big Data Management, Enterprise Data Warehouses (EDW) and emerging Agile
Data Driven Enterprise (ADDE), Big Data Service and platform providers.
Seminar 8: Big Data Security and Privacy, data centric security models. Big Data privacy issues
and regulations, Privacy Enhancement Techniques.

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Laureate Online Education (LOE)• Laureate Online Education (LOE), the online education partner of theUniversity of Liverpool, provides fully online teaching/education
environment based on customized Blackboard platform.
• Laureate’s courses are designed to push the boundaries of access tohigher education from different countries, cultural backgrounds, and forstudents with varying educational background.
• The common method here is to push students beyond the boundaries of
their customary thinking (i.e., to push them to think “outside of the box”)
and stimulate their self-motivated learning.

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Collaborative Online Learning Model Principlesa) Programs and courses are developed with input from nationally- and internationally-recognized
Subject-Matter Experts (SME), leading practitioners, associations/professional groups, andinternational representatives.
––

Educational materials combine strong conceptual foundation, technology basis and applied mechanisms,standardization, best practices and industry implementation.
Programs and courses fully leverage technological and media resources to optimize collaboration andcommunication.

b) Programs and courses are designed to create an inspiring and transforming student experienceand promote collaborative student experiences–––

Programs are future-oriented and forward thinking, both in providing course materials that reflects current
status and trends in the technology domain, and in facilitating critical and analytical students’ thinking.
Students are responsible for their learning and they exercise elements of control over their learning
environment. They are inspired through opportunities to engage in reflection and critical thinking, toconnect theory to practice, their own experience and educational group experience in the weekly classroom
discussions.
Work on individual and group projects and hands on assignment.

c) Laureate’s programs and courses are designed to expose students to diverse ideas, opinions,perspectives, and experiences - both brought by instructors and based on knowledge andexperience exchange in the classroomd) The course undergoes a quality review process that includes a critical reader review andrecommendations, and adoption to the common learning model. The quality reviews are continued
all along the course “life span”.
Difference from campus education• Top down vs bottom up approach in course development
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Bloom’s Taxonomy in Online Education
The courses are developed using best practices for online education andapplying Bloom’s taxonomy with strong emphasis not only on the Cognitive
Domain but also on the Affective Domain to facilitate deep and selfmotivated learning. This includes the following:• Present learning tasks in terms of problem solving, not only as
demonstration of accumulated knowledge, and encourage multiple
approaches to problem solving.
• Provide opportunities for collaboration with others, including: discussions;sharing of experience, perceptions, and alternate viewpoints; and groupactivities.
• Allow students to draw on their own experience as part of their learningand to incorporate their own goals into the work of the course.

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Andragogy in Adult Education
••
•
•
•
•
•

Andragogy provides effective approach to online higher education. The following
principles of andragogy (adult learning) [19, 20] are incorporated:
Define a rationale for learning and make a case for the value of doing the work.
Create environments where self-directed skills are nurtured.
Have different experiences, background, learning styles, motivation, interests,and goals.
Have a life-centered orientation to learning; motivate to learn the whole course
knowledge domain and show relevance to their professional or career needs.
Instruction should help the students perform tasks, deal with problems, and thrive
in real-life situations.
Rely on the internal motivation factors and provide such motivators as subject
mastering satisfaction, knowledge opening their wider vision and general
understanding.

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Role of Final Dissertation• The important role belongs to the final dissertation module where theformation of the future specialist is finalized– Duration 9 months– Supervised by Dissertation Advisor (DA)]• The students learn the basics of the research methods and apply them tothe dissertation development process that includes––––Hypothesis and hypothesis verifications
Research questions
Scholarly contribution
Solution development and evaluation

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Big Data Module Structure••The module consists of 8 weekly seminars that includes 2 Discussion Questions(DQ), Hands-in Assignment (HA, or homework) and project assignment.
Each seminar is provided with the Lecture Notes and textbook readingassignment.
––•

Discussion questions and asynchronous discussion are the main form ofeducational activity.
–––•

DQ answers are submitted to the discussion forum and the students are required to contribute to thediscussion.
The students benefit from the knowledge and experience sharing during discussion and learn how
to defend own answer.
Instructor plays a role of moderator and the students’ knowledge and activities assessor.

Discussion questions are designed in such a way that to stimulate the students’higher cognitive activities starting from the basic literature search to analyzing
and evaluating collected and their application to problem solving.
–•

There are no synchronous lectures which makes also possible education delivery to countries andto students with low Internet connectivity, as well as bypassing time zone issues.
Recorded lectures and accompanying videos are planned for the future.

Practical use of Bloom’sGroup project and hands on assignments

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Lessons Learnt• No books to cover more than 1/3 of the course– Plenty of Data Analytics and Machine Learning– No Big Data or Scientific Data Infrastructure• Some books Data Warehouse, Scientific/HPC computing, NoSQL
databases• Potential use of Cloud Computing and Big Data platforms on
clouds is promising but not for such wide students
background like at LOE• Instructor training for online courses guiding and moderation• Use of MOOC or video lectures – to be considered• Time to develop good course

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Summary and Further Development• Develop Data Science (Big Data) Infrastructure
course for campus education

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Strata Survey Skills and Data Scientist Self-ID

Analysing the Analysers. O’Reilly Strata
Survey – Harris, Murphy & Vaisman, 2013•
Based on how data scientists think
about themselves and their work•
Identified four Data Scientist clusters

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Skills and Self-ID Top Factors
Business
ML/BigData
Math/OR
Programming
Statistics

ML – Machine Learning
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Bloom’s Taxonomy – Cognitive Activities
Example Cloud Computing
Knowledge
Exhibit memory of previously learned materials by recalling facts, terms, basic concepts and answers••••

Knowledge of specifics - terminology, specific facts
Knowledge of ways and means of dealing with specifics - conventions, trends and sequences, classifications and categories, criteria, methodology
Knowledge of the universals and abstractions in a field - principles and generalizations, theories and structures
Questions like: What are the main benefits of outsourcing company’s IT services to cloud?

Comprehension
Demonstrate understanding of facts and ideas by organizing, comparing, translating, interpreting, describing, and stating the main ideas••

Translation, Interpretation, Extrapolation
Questions like: Compare the business and operational models of private clouds and hybrid clouds.

Application
Using new knowledge. Solve problems in new situations by applying acquired knowledge, facts, techniques and rules in a different way•

Questions like: Which cloud service model is best suited for medium size software development company, and why?

AnalysisExamine and break information into parts by identifying motives or causes. Make inferences and find evidence to support generalizations••

Analysis of elements, relationships, organizational principles
Questions like: What cloud services are needed to support typical business processes of a web trading company? Give suggestions
how these services can be implemented with PaaS or IaaS clouds. Provide references to support your statements.

Synthesis
Compile information together in a different way by combining elements in a new pattern or proposing alternative solutions••

Production of a unique communication, a plan, or proposed set of operations, derivation of a set of abstract relations
Questions like: Describe the main steps and tasks for migrating IT services of an example company to clouds? What services and data
can be moved to clouds and which will remain at the enterprise premises.

Evaluation
Present and defend opinions by making judgments about information, validity of ideas or quality of work based on a set of criteria••

Judgments in terms of internal evidence or external criteria
Questions like: Do you think that cloudification of the enterprise infrastructure creates benefits for enterprises, short term and longterm?

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24Mapping Bloom’s Taxonomy from Cognitive Domain
to Professional Activity Domain
•
••

•
•

Perform standard tasks,
use API and Guidelines
Create own complex
applications using
standard API (simpleengineering)
Integrate different
systems/components,
e.g. Cloud provider andenterprise (complexengineering)
Extend existing services,
design new services
Develop newarchitecture and models,
platforms andinfrastructuresEDISON Wsh 21 Sept 2014

Remember(Knowledge)
Understand(Comprehension)

Apply(Application)Analyse(Analysis)

Big Data Education at LOE

Evaluate(Evaluation)

Create(Synthesis)

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Pedagogy vs Andragogy
Pedagogy (child-leading) and Andragogy (man-leading)• On-campus and on-line education• Developed by American educator Malcolm Knowles, statedwith six assumptions related to motivation of adult learning:– Adults need to know the reason for learning something (Need toKnow)– Experience (including error) provides the basis for learning activities(Foundation)– Adults need to be responsible for their decisions on education;
involvement in the planning and evaluation of their instruction (Selfconcept)– Adults are most interested in learning subjects having immediate
relevance to their work and/or personal lives (Readiness).
– Adult learning is problem-centered rather than content-oriented(Orientation)– Adults respond better to internal versus external motivators(Motivation)
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Applying Andragogy to Self-Education and Online
Training - Problems• Andragogy concept is widely used in on-line education
but– Based on active discussion activities guided/moderated byinstructor/moderator– Combined with the Bloom’s taxonomy• Self-education (guided) and online training specifics– Course consistency in sense of style, presentation/graphics,etc– Requires the course workflow to be maximum automated• Especially if coupled with certification or pre-certification– Less time to be devoted by trainee• Estimated 1 hour per lesson, maximum 3 lessons per topic– Knowledge control questionnaires at the end of lessons ortopics
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