Introduction to Data and ComputationEssential capabilities for everyone in Teaching, Learning and Research.

Kim Flintoff Learning Futures Advisor Curtin Learning and Teaching

Simon Huband Data Scientist, Learning Futures Curtin Learning and Teaching

David Gibson Director, Learning Futures Curtin Learning and Teaching

We  acknowledge  the  Nyungar  Wadjuk  people  as  the  tradi7onal  owners  of  country  on  which  Cur7n’s  Bentley  

campus  sits.    

We  wish  to  acknowledge  their  con7nuing  connec7on  to  land,  sea  and  community  and  pay  our  respects  to  them  and  their  culture;  and  to  elders  past,  present  and  future.

Abstract

Einstein's ideas were pivotal in shifting the way we think about the physical world - from the Newtonian to the Quantum models - in turn this changed the way we think about the world and allowed us to develop new ways of engaging with the world.

We are at a similar juncture now with the Information Age and the global Internet of Everything. The development of computational technologies allows us to think about and to make meaning from data about the world in what might be called the age of algorithms and computational thinking.

Data science, conducted with global computational resources changes the way we think about, define and solve problems.

An age of creativity for research teams working in partnership with computational resources may be upon us, extending data science impacts across all fields.

Einstein Schrödinger

Gödel Bohr

The Classroom will learn you. http://www.research.ibm.com/cognitive-computing/machine-learning-applications/decision-support-education.shtml

Learning FuturesSmart Classrooms of the Future

Curtin Institute for ComputationCIC Themes

Big Data Analytics

Simulation Visualisation EducationModelling and

Optimisation

The Curtin Institute for Computation (CIC) is a multidisciplinary institute, inspiring and fostering collaboration across computer science,

engineering, sciences, social sciences and the humanities.

Curtin Institute for ComputationCIC Themes

Big Data Analytics

Simulation Visualisation EducationModelling and

Optimisation

01What is “data”and where is it?

Is any collection of things that you intend to make

meaning from. It might come in the form of

numbers, words, pictures, stories, colours, sounds,

measurements, observations, descriptions.

Without context its has limited value or meaning.

Big data in many cases refers to the ability to create

this meaning from available data sources

Data can be structured, semi-structured or

unstructured.

Information - is data that has a known context, has

been processed in some way and can be applied to

some form of problem-solving or meaning-making.

k.flintoff@curtin.edu.au

What is Data - https://youtu.be/EMHP-q4GEDc

What is “big data”Big Data Background

"Big data is new and “ginormous” and scary – very, very scary. No, wait. Big data is just another name for the same old data marketers have

always used, and it’s not all that big, and it’s something we should be embracing, not fearing. No, hold on. That’s not it, either. What I meant to say is that big data is as powerful as a tsunami, but it’s a deluge that can be controlled . . . in a positive way, to provide business insights and

value. Yes, that’s right, isn’t it?"

- Lisa Arthur, Forbes

Big data according to GartnerBig Data Background

"Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision

making, and process automation."

- Gartner

The 3 V’s of Big DataBig Data Background

Volume generated and stored

Velocity at which data is generated and processed (time sensitivity)

Variety of types of data

Additional V’s of Big DataBig Data Background

Veracity correctness and accuracy

Value ability to derive worth

Variability inconsistency of meaning

Validity, Visibility, ...

CIC Theme: Big Data AnalyticsCIC Themes

Big data refers to data sets that are so large or complex that traditional data analysis techniques

cannot cope with them. Thus, new kinds of databases to store the data and algorithms to find meaningful patterns within the data are required.

Curtin Institute for ComputationCIC Themes

Big Data Analytics

Simulation Visualisation EducationModelling and

Optimisation

SimulationSimulation and Optimisation Background

"Simulation is the imitation of the operation of a real-world process or system over time. The act of simulating something first requires that a

model be developed...

"The model represents the system itself, whereas the simulation represents the operation of the system over time."

Source: Wikipedia - https://en.wikipedia.org/wiki/Simulation

Computer SimulationSimulation and Optimisation Background

"A computer simulation is a simulation, run on a single computer, or a network of computers, to reproduce behavior of a system. The

simulation uses an abstract model (a computer model, or a computational model) to simulate the system."

Source: Wikipedia - https://en.wikipedia.org/wiki/Computer_simulation

OptimisationSimulation and Optimisation Background

"An act, process, or methodology of making something (as a design, system, or decision) as fully perfect, functional, or

effective as possible"

- Merriam-Webster

Mathematical OptimisationSimulation and Optimisation Background

"... mathematical optimization (alternatively, optimization or mathematical programming) is the selection of a best element

(with regard to some criteria) from some set of available alternatives..."

"More generally, optimization includes finding "best available" values of some objective function given a defined domain (or a set of constraints), including a variety of different types of objective

Source: Wikipedia - https://en.wikipedia.org/wiki/Mathematical_optimization

Simulator Components (example)Simulation and Optimisation Background

Executable Model

Model Parameters

Inputs / Scenario (user)

"A computer model is the algorithms and equations used to capture the behavior of the system being modeled. By contrast, computer simulation is the actual running of the program that contains these

equations or algorithms. Simulation, therefore, is the process of running a model."

Source: Wikipedia - https://en.wikipedia.org/wiki/Computer_simulation

Results

Optimiser Components (example)Simulation and Optimisation Background

Executable Optimiser

Simulator Components

Solution / Decision(s)

Photo credit: https://commons.wikimedia.org/wiki/File:Front_pareto.svg

Objective Function(s)

Photo credit: https://commons.wikimedia.org/wiki/File:Visualization_of_two_dimensions_of_a_NK_fitness_landscape.png

Supply Chain ExampleSimulation and Optimisation Background

Mine Feed

(supply)Processing Product

Storage

Waste Storage

Rail Port Ships (demand)

Simulation vs OptimisationSimulation and Optimisation Background

Simulation Explore sensitivity to weather

Explore sensitivity to unschedules outages Manually design a maintenance schedule

Optimisation Generate “good” maintenance schedules

Inputs & OutputsSimulation and Optimisation Background

Simulation Optimisation

Inputs

• Mine feed schedule, ore qualities (hardness, wetness, …) (supply)

• Shipping schedule (demand) • Initial inventory levels • Constraints (equipment rates, …) • Weather forecast • Unplanned outage profiles • Maintenance schedule

• Mine feed schedule, ore qualities (hardness, wetness, …) (supply)

• Shipping schedule (demand) • Initial inventory levels • Constraints (equipment rates, …) • Weather forecast • Unplanned outage profiles

Outputs

• Throughput • Equipment utilisation • Demand satisfied • Bottleneck analysis • Sensitivity analysis

• Maintenance schedule(s)

KPIs & ObjectivesSimulation and Optimisation Background

Simulation Optimisation

KPIs1. Throughput 2. Equipment utilisation 3. Demand satisfied 4. Solution “robustness” (gracefully handle unplanned situations)

Objective Function User Judgement

Single or multi-objective combination of: 1. Maximise throughput 2. Maximise utilisation 3. Maximise demand satisfied 4. Maximise solution “robustness”

Sampling of Technical ConsiderationsSimulation and Optimisation Background

Simulation Optimisation

1. Stochastic or deterministic? 2. Agent-based behavioural model? 3. Discrete event simulation? 4. Flow algorithm? 5. Material transformation: transformation

functions, particle physics model, …? 6. …

Choices influence model parameters and thereby tuning. E.g., temporal resolution.

1. Mathematical formulation vs simulation-driven? 2. Black box or white box modelling? 3. Handling of stochastic elements? 4. Optimisation algorithm: Linear Programming,

Evolutionary Algorithm, … (there are lots) 5. Ensemble optimisation? 6. …

Choices influence implementation options, performance, tuning, and subtle concepts such as “decision visibility”.

Simulation PitfallsSimulation and Optimisation Background

"Computer simulations are good at portraying and comparing theoretical scenarios, but in order to accurately model actual case

studies they have to match what is actually happening today."

Source: Wikipedia - https://en.wikipedia.org/wiki/Computer_simulation

Optimisation PitfallsSimulation and Optimisation Background

Computational optimisation is only as good as the model it is built on.

Optimisers can and will exploit model weaknesses.

CIC Theme: SimulationCIC Themes

Simulation explores the use of computers to solve complex numerical models, particularly those that have to be evaluated many times to generate dynamical information. Researchers in this theme are significant users of the fastest supercomputer in

the Southern Hemisphere which is housed at the Pawsey supercomputing centre, resulting in Curtin being the largest

institutional user of this world-class resource.

CIC Theme: Modelling and OptimisationCIC Themes

Modelling and optimisation develops mathematical models to describe the behaviour of a physical system and then optimises the parameters of the model to improve the performance of the

system.

Curtin Institute for ComputationCIC Themes

Big Data Analytics

Simulation Visualisation EducationModelling and

Optimisation

0126VisualisationCommunicating the meaning of your data visually.

Visualisation Examples: http://savedelete.com/design/data-visualization-examples/176982/

Data visualisation examples:

• Charts,

• graphs,

• maps

• Infographics

• Interactive displays

• Adaptive display

• Dynamic display

Many tools are applied to his kind of work:

Rapid Miner

Tableau

22 Free tools article

30+ Free Tools

Storytelling with Data Visualisation: http://blog.kurtosys.com/storytelling-data-visualization/

30

CIC Theme: VisualisationCIC Themes

The visualisation theme seeks to gain insight into research questions through displaying and interacting with representations of data or virtually simulated objects and environments on large, immersive

displays. These projects typically require expertise in computer science, interaction design, and usability. In many ways, it is both an art and a science. Researchers in the Institute are able to use the Curtin HIVE

(Hub for Immersive Visualisation and eResearch), where they can access four large-scale immersive and interactive systems.

Curtin Institute for ComputationCIC Themes

Big Data Analytics

Simulation Visualisation EducationModelling and

Optimisation

CIC Theme: EducationCIC Themes

Computation is an increasingly important discovery and research activity in most disciplines, leading to new fields such as computational science and engineering, learning sciences, humanities, design, macro

and microeconomics, and organisational behavior. Universities have found it difficult to bring these inherently cross-discipline subjects into the curriculum. The education arm of the

institute aims to build computational skills, literacies and competencies across the entire university community.

Innovating learning for Curtin centres

upon building a highly media rich,

interactive and personalised learning

experience for all our learners. To

facilitate this, CLT are working on a

number of internationally leading

projects and programs.

History

Curtin Learning and TeachingStrategic Innovations in Learning Engagement

01

"New technologies have resulted in unprecedented global competition and enabled learning to be delivered effectively on a much larger scale."

Our ChallengeTransforming Teaching and Learning

students have unprecedented choice

technology has removed geographic boundaries

employers expect job ready leaders

01Big dataHow do we get it, what do we do with it.

Photo credit: - https://www.flickr.com/photos/keoni101/7069578953/in/photostream/ (Image by Keoni Cabral CC 2.0)

Estimates suggest that the vast majority of data is unstructured.

Human activity generates data.

Sources of human data - behaviour, answering questions, biological data, measurements,

wearable technology, online behaviour, interaction with devices, machines, etc. spending,

buying, games, etc…

Other types of data - anything we count, record, measure, etc

Enhancing teaching and learning through educational data mining and learning analytics. US Dept of Education (2012)

Applying Data to Learning and TeachingEducational Data Mining: Predict the Future, Change the Future

01Big data in educationsometimes called “learning analytics”

Tin Can (Experience) API - http://tincanapi.com/overview/

01The Internet of Thingsthe world of connected everything

Governance and Recordkeeping Around the World Newsletter (April 2015) http://www.bac-lac.gc.ca/eng/services/government-information-resources/information-management/Documents/april-2015.pdf

“Approximately 14 billion objects (things) are connected to the Internet and is growing. We are now entering a new phase in how these objects are used and what will be their impact. The IoT brings with it enormous opportunities, to both the private and public sectors, in all areas including the management of information throughout its lifecycle.”

39

01The Internet of Thingswhat might it look like?

What is the Internet of Things (Infographic) - http://www.visualcapitalist.com/what-is-internet-things/

“Thingful® is a search engine for the Internet of

Things, providing a unique geographical index of

connected objects around the world, including

energy, radiation, weather, and air quality devices

as well as seismographs, iBeacons, ships, aircraft

and even animal trackers. Thingful’s powerful

search capabilities enable people to find devices,

datasets and realtime data sources by geolocation

across many popular Internet of Things networks,

and presents them using a proprietary patent-

pending geospatial device data search ranking

methodology, ThingRank®.”

01Data miningMaking sense of big data

Photo credit: -https://www.flickr.com/photos/franganillo/3678747186/ (CC Jorge Franganillo)

Data mining is the process of looking for patterns

and relationships within and across data

collections. Normally by applying some form of

computerised manipulation.

Analytics - a visual expression of a particular

arrangement and analysis of data.

Data can tell you what has happened but can only

be used to guess what will happen - this is called

predictive analytics.

Open Data

“Open data and content can be freely used,

modified, and shared by anyone for any

purpose” (http://opendefinition.org/)

01Computational ThinkingThinking logically, thinking with a structure.

Photo credit: http://en.wikipedia.org/wiki/Computational_complexity_theory

“ Computational Thinking is the thought processes involved in formulating problems and their solutions so that the solutions are represented in a form that can be effectively carried out by an information-processing agent. “ Cuny, Snyder, Wing

01Computational ThinkingBringing it into work and learning

Photo credit: https://www.behance.net/gallery/5798457/ISTE-Computational-Thinking-Poster

Scaffolding design thinking, computational

thinking and creative problem-solving

(innovation)

• Challenge based approaches

• Defining problems

• Collaborative solutions

• Authentic- Real world application - use

global challenges as an example.

• Rapid iteration (modelling/prototyping/

testing)

• Novel juxtaposition

• Bluesky speculation

01Computational ThinkingApplying it to challenge based learning

Photo credit: http://challenge.curtin.edu.au

Welcome to Curtin Challenge, where you can

develop your skills, build your networks, and

shape your future. Challenge is a fun and

interactive way to learn, and is just one of the

many ways Curtin University is transforming

your University experience.

Curtin Challenge is a platform where you can

explore different themes of interest, to

achieve your personal and professional goals.

Challenges allow you to develop your skills,

build your networks, and shape your future

while earning badges and achievements.

01Computational ThinkingApplying it to challenge based learning

Photo credit: https://www.apple.com/au/education/docs/CBL_Classroom_Guide_Jan_2011.pdf

Challenge Based Learning

mirrors the 21st century

workplace. To stay true to its

intent, make sure participants:

• Work in collaborative groups

• Use technology commonly used in

daily life

• Tackle real-world problems using a

multidisciplinary approach

• Share the results with the world

01Computational Pedagogyfrom literacy to fluency; from using to making; from watching to creating

CodingTransform learning

Fluencies

Hacking

Making

01Computational PedagogyBringing it into work and learning

Download report: http://www.nap.edu/catalog/13170/report-of-a-workshop-on-the-pedagogical-aspects-of-computational-thinking

In 2008, the Computer and Information

Science and Engineering Directorate of

the National Science Foundation asked

the National Research Council (NRC) to

conduct two workshops to explore the

nature of computational thinking and its

cognitive and educational implications.

The first workshop focused on the scope

and nature of computational thinking and

on articulating what "computational

thinking for everyone" might mean. A

report of that workshop was released in

January 2010.

01Bluesky LearningInnovation is creative problem-solving

Photo Credit: "Newton Blue Sky". Licensed under CC BY 2.5 via Wikipedia - http://en.wikipedia.org/wiki/File:Newton_Blue_Sky.jpg#/media/File:Newton_Blue_Sky.jpg

“Gentleness, Virtue, Wisdom, and Endurance, These are the seals of that most firm assurance Which bars the pit over Destruction's strength;

And if, with infirm hand, Eternity, Mother of many acts and hours, should free

The serpent that would clasp her with his length; These are the spells by which to reassume

An empire o'er the disentangled doom.

To suffer woes which Hope thinks infinite; To forgive wrongs darker than death or night;

To defy Power, which seems omnipotent; To love, and bear; to hope till Hope creates

From its own wreck the thing it contemplates; Neither to change, nor falter, nor repent;

This, like thy glory, Titan, is to be Good, great and joyous, beautiful and free; This is alone Life, Joy, Empire, and Victory.”

― Percy Bysshe Shelley, Prometheus Unbound

01SMART LearningAdapting educational contexts to the era of big data

From David Gibson - https://prezi.com/ynemeqygnohl/theta-2015 (used with permission)

Synchronous Multiply Connected Asynchronous ROI Transformed

Ubiquitous technology Networked Integrated Systems

Let computers compute . The age of the right brain. http://www.nytimes.com/2008/04/06/technology/06unbox.html

Big data needs more creative types:

“The data artist blends engineering and statistical know-how with

intuition and novel problem-solving abilities to uncover insights and

create value from data.

“Data Scientist” is a fine job title for those who navigate terabytes of

information in search of patterns and relevance, connecting dots to

create value and competitive advantage.”

Creativity is the future of workThe dawn of the creative economy

Big data needs more creative types: http://www.forbes.com/sites/teradata/2015/01/30/big-data-needs-more-creative-types/

Developing capacity as data artists might be the key to business success.

The “adjacent possible” may redirect business intelligence to transformation.

What is Educational Data Mining (EDM)?http://edtechreview.in/dictionary/394-what-is-educational-data-mining

“Goals of EDM: 1. Predicting students’ future learning behavior by creating student models that incorporate such detailed information as students’ knowledge, motivation, metacognition, and attitudes; 2. Discovering or improving domain models that characterize the content to be learned and optimal instructional sequences; 3. Studying the effects of different kinds of pedagogical support that can be provided by learning software; and 4. Advancing scientific knowledge about learning and learners through building computational models that incorporate models of the student, the domain, and the software’s pedagogy.”

Digging Deep into Educational DataEducational Data Mining: Predict the Future, Change the Future

Learning FuturesSmart Classrooms of the Future

Learning FuturesSmart Classrooms of the Future

Learning FuturesSmart Classrooms of the Future

DiscussionVerbal data exchange

Q&

A

01Main takeawaysIf you remember nothing else, remember these.

• Understand how to work with data

• Collaborate at every opportunity

• Strive for authenticity

• Critically engage with the changes

• Take in the long view

• Actively engage with complexity

Conclusion

Start with familiar data

Start with available

tools

Start sooner rather than

later

Start with unlimited thinking

Good ByeSee you next time, have a nice day

Big Data, Computation and the Internet of Thingshttp://www.scoop.it/t/big-data-computation-and-internet-of-things