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www.pwc.co.nz NZ Analytics Forum Forming Successful Analytics Partnerships May 2014 Hosted by PwC Agenda Welcome Analytics at PwC University Analytics Program Overviews Overview of Statistics NZ’s Integrated Data Infrastructure Introducing the Data Futures Forum Panel Discussion: How can the Government, Industry and Academia work together better to enhance NZ’s analytics capability? NZ Analytics Forum PwC May 2014 2 Analytics at PwC NZ Analytics Forum PwC May 2014 3 Analytics at PwC [Placeholder Slide] NZ Analytics Forum PwC May 2014 4 Tertiary Education Analytics Program Overviews New Zealand Universities NZ Analytics Forum PwC May 2014 5 University of Auckland Teaching and Research in Analytics Andy Philpott [email protected] 27 May 2014 COMPUTER SCIENCE • • • • • • • • COMPSCI 351 Fundamentals of Database Systems COMPSCI 367 Artificial Intelligence COMPSCI 752 Semantic Web Technologies COMPSCI 753 Uncertainty in Data COMPSCI 760 Data-mining and Machine Learning COMPSCI 765 Interactive Cognitive Systems COMPSCI 767 Intelligent Software Agents Masters of Professional Studies in Data Science (jointly with Statistics) http://www.science.auckland.ac.nz/en/about/our-programmes.html STATISTICS • Mathematical statistics (theory) • Applied statistics • Statistical computing (including introductions to R and SAS and data mining) • Operations research and applied probability • Data Science Practice (New 2015) • Masters of Professional Studies in Data Science (jointly with Computer Science) http://www.science.auckland.ac.nz/en/about/our-programmes.html ENGINEERING SCIENCE (Model driven/computational business analytics) • ENGSCI 255 Introduction to Operations Research • ENGSCI 391 Optimization (Linear/MI programming) • ENGSCI 355 Practice of optimization and simulation • ENGSCI 760 Algorithms for optimization • ENGSCI 761 Advanced integer programming • ENGSCI 762-768 • Data-driven decision making: coming in 2015 http://www.des.auckland.ac.nz/uoa/course-details INFORMATION MANAGEMENT INFOMGMT 290 Business Analytics INFOMGMT 291 Business Analytics and Applications INFOMGMT 393 Data Mining and Decision Support BUSMGT 712. Business Analytics (in taught masters programme) http://courses.business.auckland.ac.nz/SubjectListing/INFOMGMT/ RESEARCH • Te Punaha Matatini – Centre for Complex Systems and Networks – One of six CoREs recently announced – Analytics theme (funded for 6 years) • National Science Challenge 10 – Science for Technological Innovation – Smart Services Portfolio growing over next 10 years Data Analytics Forum, Auckland, 27 May 2014 Machine learning and predictive data modeling of large and stream data in KEDRI/AUT Prof. Nikola Kasabov, FIEEE, FRSNZ, FIITP Director, Knowledge Engineering and Discovery Research Institute (KEDRI), and School of Computer and Mathematical Sciences Auckland University of Technology, New Zealand [email protected] www.kedri.info ALL NZ Universities + NZ based research and commercial partners: PEBL FONTERRA TELECOM, Middlemore Hospital Mighty River Power Parrotanalytics KEDRI’s National and International Partners Centre for Novel Methods of Computational Intelligence Centre for Neurocomputing and Neuroinformatics Centre for Bioinformatics Centre for Data Mining and Decision Support Systems Centre for Adaptive Pattern Recognition Systems Centre for the Study of Creativity Statistics New Zealand ASB Bank Eagle Technology Auckland Council Sener International [email protected] International collaboration: China: China Academy of Sciences Institute of Automation; Shanghai JTU and Xinjian U. Japan – NiCT- Tokyo; Kobe University; Kyushu Institute of Technology; RIKEN Germany – TU Kaiserslautern. Italy – U. of Trento; Polytechnico di Milano. Switzerland – University of Zurich and ETH. UK – University of Manchester; U. Lancaster, University of Ulster, Imperial College. Vietnam - VNU www.kedri.info Temporal and Spatio/Spectro-Temporal Data Modelling and Pattern RecognitionRIf STPR problems: 7000 E Season Season Season Season 6000 –NTRODU Multiple time series prediction – (e.g. FONTERRA milk volume prediction) – Wind energy prediction – Cyber-security event prediction – Ecological event prediction – Environmental event prediction, e.g. earthquakes – Object movement recognition – Audio/video data modelling – Multisensor temporal data integration – Brain signals (EEG, MEG, fMRI) – Brain- computer interfaces – Motor control for prosthetics – Robot control – Radio-astronomy data (SKA) – Mobile-calls prediction (e.g. TELECOM) – Business data mining – Decision support systems [email protected] 1 2 3 4 5000 4000 3000 2000 1000 www.kedri.info 0 0 50 100 150 200 250 The KEDRI Neurocomputing approach SNNr eSNN Neurofuzzy systems (e.g. DENFIS, 2002) Spiking neural networks and reservoir computing (NeuCube, Kasabov, 2014) Adaptive, deep learning of complex spatio-temporal patterns Fast , on-line operation Neuromorphic hardware (e.g. U.Manchester SpiNNaker of 200,000 neuronal processing units available for KEDRI): high speed and low power consumption [email protected] www.kedri.info pa,b C e D 2 a ,b / 2 Example: Seismic fast stream modelling for on-line early earthquake prediction Actual -> Predicted Earthquake Negative Earthquake 14 2 Negative 0 32 [email protected] PwC PwC PwC Analytics at Victoria University of Wellington Vicky Mabin and Mark Johnston NZ Analytics Forum PwC May 2014 20 Questions first, then DATA! • Think BIG: some key questions: - What is the problem you are trying to solve, or the goal you are trying to achieve? - How can we know our customers better? ◦ In an organized world, what would I be able to do? ◦ In an ideal world? • How will you achieve this goal? - Making sense of too much data or complex interactions ◦ Looking for patterns, data mining - Tailoring real-time actions in response to real-time data ◦ Tracking individuals, targeting messages in real time • What’s stopping you from doing this now? • Need to do it intelligently - understand behaviours (e.g. of consumers) - frame the problem wisely before diving into data! NZ Analytics Forum PwC May 2014 21 Data and Models Data What data do you actually need? Do you have it already or how will you collect it? • If you need statistical advice, ask for it sooner (before you collect any data) rather than later Models Still a lot to be done around models and algorithms There may be no off the shelf solution Exemplars Stories from various industries which may be transposed to your context NZ Analytics Forum PwC May 2014 22 Working with Victoria Academics People throughout university • MSOR – mathematics, statistics, operations research • Business school – management, information systems, marketing, tourism, HR, accounting, … • Psychology … Initial contacts • Vicky Mabin (Victoria Business School) • Mark Johnston (MSOR) • Dalice Sim (Statistical consultant) - Email: NZ Analytics Forum PwC [email protected] May 2014 23 Analytics at UC NZ Analytics Forum Marco Reale Where at UC? Most of the courses are offered by the school of Mathematics and Statistics (MS) with a broad range in pure and applied mathematics as well as statistics Computational mathematics and statistics and their applications are strong both in terms of teaching and research Where else? Other courses relevant to Analytics are in Computer Science, Health Sciences, Psychology, Management Science, Economics and Finance New Initiatives Computational applied mathematical sciences Financial Engineering PwC PwC PwC University of Waikato Analytics @ Waikato Analytics related papers @ UoW • Undergraduate level: – COMP321: Practical Data Mining • Co-taught with Statistics Department • Graduate level: – COMP521: Machine Learning Algorithms – COMP523: Data Stream Mining – COMP555: Bioinformatics – COMP556: Computational Finance 32 Analytics research and software • Honour’s, Master, and PhD research – 4-year BCMS(Hons) – ~20 finished, 4 on-going analytics-related PhDs • Graduates both in Academia and Industry: – LIC, 11Ants, Realtime Genomics, Pingar, Microsoft Research, … – Altoo University (Finland), UTS (Sydney), UoW, … • Numerous open source software packages: – Weka, Moa, Adams, Meka, Kea, WikipediaMiner – http://cosi.cms.waikato.ac.nz 33 Weka-centered MOOCs • Massive Open Online Course: – – – – Based on text book by Witten/Frank/Hall Video lectures, online activities, … Part I: Data Mining with Weka Part II: Advanced Data Mining with Weka • First MOOC in NZ: – Sept.2013, reruns in March and May 2014 – Next: eResearch NZ 2014, Hamilton, 30/6-02/7 – #WekaMOOC, weka.waikato.ac.nz 34 Analytics at AUT Paul Cowpertwait Head of Mathematical Sciences School of Computer and Mathematical Sciences School Centres of Research Related to Analytics Knowledge Engineering & Discovery Research Institute Geoinformatics Research Centre Mathematical Sciences Research Group Statistical Consultancy Centre Some Industrial Links: Relating theory to practice Statistics New Zealand ASB Bank Eagle Technology Fonterra Auckland Council Sener International Undergraduate Degrees with Majors in Analytics Bachelor of Computer and Information Science – BCIS Bachelor of Mathematical Sciences – BMathSc Bachelor of Science – BSc Masters of Analytics (MoA) Currently with CUAP 180 point masters Combines papers in statistics, computer science, and applied maths: • time series • operations research • optimisation • multivariate analysis • financial maths • data mining • data warehousing • stochastic modelling Statistics New Zealand Overview of the Integrated Data Infrastructure PwC 42 Integrated Data Changing the landscape for analytics Guido Stark and Bex Sullivan May 2014 Integrated Data Infrastructure (IDI) 44 Consistently maintaining Privacy Security Confidentiality 45 Accessing the IDI Statistics NZ Microdata Access Services Public interest Legal compliance Bona fide research Non-regulatory Proven researcher Confidentiality Suitable data source 46 Use of the IDI Outcomes of tertiary study Temporary migration System-wide analytics 47 IDI Expansion 48 Questions For more information contact Integrated Data, Statistics NZ [email protected] [email protected] Microdata access, Statistics NZ [email protected] 49 Introducing Data Futures Forum NZ Analytics Forum PwC May 2014 50 Data Futures Forum [Placeholder Slide] NZ Analytics Forum PwC May 2014 51 Panel Discussion How can the Government, Industry, Academia work together to enhance New Zealand’s analytics capability? NZ Analytics Forum PwC May 2014 52 The value of an idea lies in the using of it… This publication has been prepared for general guidance on matters of interest only, and does not constitute professional advice. You should not act upon the information contained in this publication without obtaining specific professional advice. No representation or warranty (express or implied) is given as to the accuracy or completeness of the information contained in this publication, and, to the extent permitted by law, PwC New Zealand, its members, employees and agents do not accept or assume any liability, responsibility or duty of care for any consequences of you or anyone else acting, or refraining to act, in reliance on the information contained in this publication or for any decision based on it. © 2014 PwC. All rights reserved. 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