SURVEY REPORT DATA SCIENCE SOCIETY 2014

Size: px
Start display at page:

Download "SURVEY REPORT DATA SCIENCE SOCIETY 2014"

Transcription

1 SURVEY REPORT DATA SCIENCE SOCIETY 2014

2 TABLE OF CONTENTS Contents About the Initiative 1 Report Summary 2 Participants Info 3 Participants Expertise 6 Suggested Discussion Topics 7 Selected Responses Summary 9 Areas of Interest 10 Contact information 12

3 ABOUT THE INITIATIVE About the Initiative Data Science Society is an initiative which enables faster growth and better performance for Education, Science and Business in the Data Science industry. Our community platform should facilitate collaboration, knowledge sharing, innovation and entrepreneurship. Our goal is to stimulate education, knowledge sharing and research. We provide new business opportunities and communication channels and increase public awareness about Data Science. Our next step is to start regular society meetings and present the most interesting topics selected by members. More information will be available on our website, which will be launched in the next few weeks. Thank you for your active participation, and we look forward to seeing you at the first society meeting! Data Science Society team August 25, 2014 Page 1

4 REPORT SUMMARY Report Summary THE SURVEY Between 10 of June and 15 of August a survey was conducted among volunteers with an incentive to gain th th and share knowledge about Data Science. The main scope of the survey was to validate the hypothesis that there is an existing knowledge and willingness to create a decentralized community which proactively can collaborate and share knowledge and expertise. The questionnaire was designed to gather initial information on: i) participant info; ii) expertise in the field; iii) various topics that members are willing to present at the regular society meetings; iv) platforms in operation and v) interest of business, science and universities. CONCLUSIONS There exist a good body knowledge in the field and a strong interest from the three groups (business, science and universities to collaborate. Various types of platforms are used with dominance of Wiki-s. 30 volunteers participated in the survey from various companies and universities with different expertise. They suggested more than 35 topics in various areas to be presented at the society meetings. NEXT STEPS The start of the topics selection process will require comments and ratings from the participants. Top 10 topics will be selected based on the vote and will be presented by originators or in discussion panels. The society understanding is that members are willing to participate and could present the topics of interest following one or two months of notification period. Information on speakers expertise and their companies will be provided during the selection process. Page 2

5 PARTICIPANTS INFO Participants Info A variety of participants with different occupation, expertise and level of employment took part in the survey. Main highlights are provided in this section. EDUCATION AND SCIENCE At the current stage a limited number of universities were contacted. We plan to gradually increase their number and variety. BUSINESS Startups The startup society in Bulgaria is increasing rapidly. It is willing to share knowledge and expertise and to collaborate. Page 3

6 PARTICIPANTS INFO Companies Different local and international businesses were contacted with diverse scope, goals and expertise. Some of them provide consultancy, analytical services, analytical tools, and solutions in the field of Data Science, while others are currently only interested in this area. Page 4

7 PARTICIPANTS INFO PARTICIPANTS PROFILES In the survey different level of participants took a part, some of them are employees with respective expertise as Subject Matter Experts, Managers which are on middle and top level, scientists with respective academicals rank PhD, Doctor and professor. Subject Matter Experts Mid and Top Level Managers PhD/Assistant Professor Professor and associate professors Page 5

8 PARTICIPANTS EXPERTISE Participants Expertise This section provides information on the expertise of the survey participants. The results could be somewhat biased due to the use of self-assessment technique. The most widely covered knowledge areas among participants are Statistics and Business Analytics. Area Number of Participants Average knowledge level (1-10) Data Engineering Data Management Data itegration Data Warehousing Infrastructure Information retrieval Data wrangling Mathematics Statistics Learning Machine Learning Neural Networks Natural language processing Data mining Computer Vision Complex event processing Domain expertise Domain expertise: Cargo transport Online poker Marketing e-government credit risk Business Analytics software business analysis Business Intelligence Visualisation Advanced computing Others Business Development Computer Science Software development related to data scinece Technology adoptions form businesses open data IT Usefulness of the R&D from customer's point of view Business process optimization with data science Consulting services Page 6

9 SUGGESTED DISCUSSION TOPICS Suggested Discussion Topics Different domain-specific and general discussion topics were suggested by the participants. DESCRIPTIONS 1 Databases, storing, indexing, quering 2 Normalizing data 3 Market system identification - demand model development (the accent could be automation of the process for model development) 4 Data Processes and Tools 5 R language for statistics 6 Data mining platforms 7 How to make profitable business from scientific research? 8 Machine learning algorithms - SVM, Artificial NN, Random forests and others - Strength and weaknesses, assumptions, optimizations. 9 Fraud-predictive analysis bases on Social Networks Data 10 Statistical data in online poker 11 Impact of incorrect application of data science, for instance saying we do big data and not understanding what it really means. 12 Usage of open data 13 What is Big Data? 14 Big data application case study: balancing of demand and supply in cargo transport 15 Big Data approaches to Linked / RDF data management 16 Citizen science 17 Parallel and Distributed Algorithms for Inference and Optimization. In particular I am interested in computational frameworks for horizontally scaling iterative algorithms for which Hadoop MapReduce framework might not be the best solution. 18 General intro to statistics 19 Many core CPU for high performance computing 20 What breaks the connection between business, the people that should use the results form the R&D and the scientists? 21 Using sophisticated machine learning models for credit risk prediction. 22 Complex algorithms based on a collaboration of ML algorithms Page 7

10 SUGGESTED DISCUSSION TOPICS 23 Health analytics/quantified self/bio feedback 24 What does Hadoop really do better than Oracle? 25 Implementation of e-government 26 What is the role of the Predictive Analytics in the new Ecomony? 27 Big data in digital humanities 28 Which are the most common/popular distributed platforms for storing large volumes of data in the industry. 29 How to fund join ventures between labs and business? 30 Computer vision - video image recognition. 31 Sport analytics 32 How to apply the Predictive Analytics in the Business (in any industry with "Big Data"); and how science can help in the process? 33 Predictive Analytics 34 Data discrepancy mitigation 35 Data Quality problems 36 Forecasting methods and trend adjustments 37 Online resources to build data science skills or "The Open-Source Data Science Masters" 38 Hadleyverse 39 Predictive Analytics for Credit Risk in Bulgarian Financial Institutions - Challenges and Opportunities 40 Forecasting Market Risk in the Context of Basel II Without Relying on External Software Solutions - Is It Possible? Page 8

11 SELECTED RESPONSES SUMMARY Selected Responses Summary Current colloboration platforms in use 80% 70% 73% 60% 57% 50% 40% 30% 20% 10% 43% 40% 40% 0% Wiki Forum Social Platform Stack Exchange Google docs Indicative Support for Data Science Society 120% 100% 97% 80% 60% 67% 57% 63% 40% 27% 20% 10% 10% 10% 0% I will attend Bring others Sponsorship Event fee Speaker Venue Media Volunteer Page 9

12 AREAS OF INTEREST Areas of Interest This section summarizes the presented areas of interest of the participants sorted in alphabetical order. DESCRIPTIONS 256 core CPU Large scale, sparse optimization Analytic and Predictions Machine Learning Applications Market Basket Analysis Automated Decisioning Market research Banking Mathematics Bayesian Networks New Economy of Internet of things Behavioral Analytics NLP/text mining Big data Non linear system identification Business Analytics Non-structural data Business development based on technology Numerical methods solutions Business Intelligence Open data Churn Prediction Personalization Communication Internet Predictive Analysis Computational frameworks for iterative Predictive modelling mathematical algorithms running on big data (i.e beyond MapReduce) Computer Vision Process improvement using Data Customer Behavioral Segmentation Programming and Task Automation Data processing Public sources for big data Data scraping Real-time / stream data processing Databases, storing, indexing, querying Risk management & evaluation Democratization of everything Semantic analysis Digital Marketing Analytics Signal processing Domain Expertise Social causes and initiatives e-government Social network analysis Electric vehicles Sport Analytics Embedded devices Startups and entrepreneurships Page 10

13 AREAS OF INTEREST Financial markets Fuzzy Sets and Logic Health Analytics/bio feedback High-performance computing Information Analysis k-means Clustering Know-how exchange Large scale cloud platforms Large scale data mining State space system identification (approach for modelling of multivariable dynamic systems) Text Mining Tools Unstructured data analysis Unsupervised learning Very large digital libraries Visualization Where/how do companies accumulating large volumes of data keep it? Page 11

14 CONTACT INFORMATION Contact information Data Science Society Sofia, Bulgaria Website: Tel: Page 12

Predictive Analytics Techniques: What to Use For Your Big Data. March 26, 2014 Fern Halper, PhD

Predictive Analytics Techniques: What to Use For Your Big Data. March 26, 2014 Fern Halper, PhD Predictive Analytics Techniques: What to Use For Your Big Data March 26, 2014 Fern Halper, PhD Presenter Proven Performance Since 1995 TDWI helps business and IT professionals gain insight about data warehousing,

More information

EMC Greenplum Driving the Future of Data Warehousing and Analytics. Tools and Technologies for Big Data

EMC Greenplum Driving the Future of Data Warehousing and Analytics. Tools and Technologies for Big Data EMC Greenplum Driving the Future of Data Warehousing and Analytics Tools and Technologies for Big Data Steven Hillion V.P. Analytics EMC Data Computing Division 1 Big Data Size: The Volume Of Data Continues

More information

Statistics for BIG data

Statistics for BIG data Statistics for BIG data Statistics for Big Data: Are Statisticians Ready? Dennis Lin Department of Statistics The Pennsylvania State University John Jordan and Dennis K.J. Lin (ICSA-Bulletine 2014) Before

More information

Big Data Executive Survey

Big Data Executive Survey Big Data Executive Full Questionnaire Big Date Executive Full Questionnaire Appendix B Questionnaire Welcome The survey has been designed to provide a benchmark for enterprises seeking to understand the

More information

How to use Big Data in Industry 4.0 implementations. LAURI ILISON, PhD Head of Big Data and Machine Learning

How to use Big Data in Industry 4.0 implementations. LAURI ILISON, PhD Head of Big Data and Machine Learning How to use Big Data in Industry 4.0 implementations LAURI ILISON, PhD Head of Big Data and Machine Learning Big Data definition? Big Data is about structured vs unstructured data Big Data is about Volume

More information

Data Science at U of U

Data Science at U of U Data Science at U of U Je M. Phillips Assistant Professor, School of Computing Center for Extreme Data Management, Analysis, and Visualization Director, Data Management and Analysis Track University of

More information

Mike Maxey. Senior Director Product Marketing Greenplum A Division of EMC. Copyright 2011 EMC Corporation. All rights reserved.

Mike Maxey. Senior Director Product Marketing Greenplum A Division of EMC. Copyright 2011 EMC Corporation. All rights reserved. Mike Maxey Senior Director Product Marketing Greenplum A Division of EMC 1 Greenplum Becomes the Foundation of EMC s Big Data Analytics (July 2010) E M C A C Q U I R E S G R E E N P L U M For three years,

More information

Bachelor Degree in Informatics Engineering Master courses

Bachelor Degree in Informatics Engineering Master courses Bachelor Degree in Informatics Engineering Master courses Donostia School of Informatics The University of the Basque Country, UPV/EHU For more information: Universidad del País Vasco / Euskal Herriko

More information

Is a Data Scientist the New Quant? Stuart Kozola MathWorks

Is a Data Scientist the New Quant? Stuart Kozola MathWorks Is a Data Scientist the New Quant? Stuart Kozola MathWorks 2015 The MathWorks, Inc. 1 Facts or information used usually to calculate, analyze, or plan something Information that is produced or stored by

More information

Advanced In-Database Analytics

Advanced In-Database Analytics Advanced In-Database Analytics Tallinn, Sept. 25th, 2012 Mikko-Pekka Bertling, BDM Greenplum EMEA 1 That sounds complicated? 2 Who can tell me how best to solve this 3 What are the main mathematical functions??

More information

Data Mining in the Swamp

Data Mining in the Swamp WHITE PAPER Page 1 of 8 Data Mining in the Swamp Taming Unruly Data with Cloud Computing By John Brothers Business Intelligence is all about making better decisions from the data you have. However, all

More information

High-Performance Analytics

High-Performance Analytics High-Performance Analytics David Pope January 2012 Principal Solutions Architect High Performance Analytics Practice Saturday, April 21, 2012 Agenda Who Is SAS / SAS Technology Evolution Current Trends

More information

Sunnie Chung. Cleveland State University

Sunnie Chung. Cleveland State University Sunnie Chung Cleveland State University Data Scientist Big Data Processing Data Mining 2 INTERSECT of Computer Scientists and Statisticians with Knowledge of Data Mining AND Big data Processing Skills:

More information

2015 Analyst and Advisor Summit. Advanced Data Analytics Dr. Rod Fontecilla Vice President, Application Services, Chief Data Scientist

2015 Analyst and Advisor Summit. Advanced Data Analytics Dr. Rod Fontecilla Vice President, Application Services, Chief Data Scientist 2015 Analyst and Advisor Summit Advanced Data Analytics Dr. Rod Fontecilla Vice President, Application Services, Chief Data Scientist Agenda Key Facts Offerings and Capabilities Case Studies When to Engage

More information

Big Data, Physics, and the Industrial Internet! How Modeling & Analytics are Making the World Work Better."

Big Data, Physics, and the Industrial Internet! How Modeling & Analytics are Making the World Work Better. Big Data, Physics, and the Industrial Internet! How Modeling & Analytics are Making the World Work Better." Matt Denesuk! Chief Data Science Officer! GE Software! October 2014! Imagination at work. Contact:

More information

Programme Specification

Programme Specification Programme Specification Awarding Body/Institution Teaching Institution Queen Mary, University of London Queen Mary, University of London Name of Final Award and Programme Title Master of Science (MSc)

More information

ANALYTICS CENTER LEARNING PROGRAM

ANALYTICS CENTER LEARNING PROGRAM Overview of Curriculum ANALYTICS CENTER LEARNING PROGRAM The following courses are offered by Analytics Center as part of its learning program: Course Duration Prerequisites 1- Math and Theory 101 - Fundamentals

More information

BIG DATA IN THE CLOUD : CHALLENGES AND OPPORTUNITIES MARY- JANE SULE & PROF. MAOZHEN LI BRUNEL UNIVERSITY, LONDON

BIG DATA IN THE CLOUD : CHALLENGES AND OPPORTUNITIES MARY- JANE SULE & PROF. MAOZHEN LI BRUNEL UNIVERSITY, LONDON BIG DATA IN THE CLOUD : CHALLENGES AND OPPORTUNITIES MARY- JANE SULE & PROF. MAOZHEN LI BRUNEL UNIVERSITY, LONDON Overview * Introduction * Multiple faces of Big Data * Challenges of Big Data * Cloud Computing

More information

Promises and Pitfalls of Big-Data-Predictive Analytics: Best Practices and Trends

Promises and Pitfalls of Big-Data-Predictive Analytics: Best Practices and Trends Promises and Pitfalls of Big-Data-Predictive Analytics: Best Practices and Trends Spring 2015 Thomas Hill, Ph.D. VP Analytic Solutions Dell Statistica Overview and Agenda Dell Software overview Dell in

More information

MS1b Statistical Data Mining

MS1b Statistical Data Mining MS1b Statistical Data Mining Yee Whye Teh Department of Statistics Oxford http://www.stats.ox.ac.uk/~teh/datamining.html Outline Administrivia and Introduction Course Structure Syllabus Introduction to

More information

S T U D Y P L A N. 1 st Year. Total Hours Spring Semester (Term 2) Total Hours

S T U D Y P L A N. 1 st Year. Total Hours Spring Semester (Term 2) Total Hours S T U D Y P L A N 1 st Year EMAT 110 ECHM 110 EPHS 110 ENGR 110 ENGR 111 HUMN 110 EMAT 10 EMAT 11 EPHS 10 EPHS 11 ENGR 10 HUMN 10 Hours Lec. Tut. Lab Fall Semester (Term 1) Calculus for Engineering I General

More information

Big Data The Next Phase Lessons from a Decade+ Experiment in Big Data

Big Data The Next Phase Lessons from a Decade+ Experiment in Big Data Big Data The Next Phase Lessons from a Decade+ Experiment in Big Data David Belanger PhD Senior Research Fellow Stevens Institute of Technology dbelange@stevens.edu 1 Outline Big Data Overview Thinking

More information

Big Data from a Database Theory Perspective

Big Data from a Database Theory Perspective Big Data from a Database Theory Perspective Martin Grohe Lehrstuhl Informatik 7 - Logic and the Theory of Discrete Systems A CS View on Data Science Applications Data System Users 2 Us Data HUGE heterogeneous

More information

Danny Wang, Ph.D. Vice President of Business Strategy and Risk Management Republic Bank

Danny Wang, Ph.D. Vice President of Business Strategy and Risk Management Republic Bank Danny Wang, Ph.D. Vice President of Business Strategy and Risk Management Republic Bank Agenda» Overview» What is Big Data?» Accelerates advances in computer & technologies» Revolutionizes data measurement»

More information

SAP Predictive Analytics: An Overview and Roadmap. Charles Gadalla, SAP @cgadalla SESSION CODE: 603

SAP Predictive Analytics: An Overview and Roadmap. Charles Gadalla, SAP @cgadalla SESSION CODE: 603 SAP Predictive Analytics: An Overview and Roadmap Charles Gadalla, SAP @cgadalla SESSION CODE: 603 Advanced Analytics SAP Vision Embed Smart Agile Analytics into Decision Processes to Deliver Business

More information

01219211 Software Development Training Camp 1 (0-3) Prerequisite : 01204214 Program development skill enhancement camp, at least 48 person-hours.

01219211 Software Development Training Camp 1 (0-3) Prerequisite : 01204214 Program development skill enhancement camp, at least 48 person-hours. (International Program) 01219141 Object-Oriented Modeling and Programming 3 (3-0) Object concepts, object-oriented design and analysis, object-oriented analysis relating to developing conceptual models

More information

The 4 Pillars of Technosoft s Big Data Practice

The 4 Pillars of Technosoft s Big Data Practice beyond possible Big Use End-user applications Big Analytics Visualisation tools Big Analytical tools Big management systems The 4 Pillars of Technosoft s Big Practice Overview Businesses have long managed

More information

IMAV: An Intelligent Multi-Agent Model Based on Cloud Computing for Resource Virtualization

IMAV: An Intelligent Multi-Agent Model Based on Cloud Computing for Resource Virtualization 2011 International Conference on Information and Electronics Engineering IPCSIT vol.6 (2011) (2011) IACSIT Press, Singapore IMAV: An Intelligent Multi-Agent Model Based on Cloud Computing for Resource

More information

BIG DATA What it is and how to use?

BIG DATA What it is and how to use? BIG DATA What it is and how to use? Lauri Ilison, PhD Data Scientist 21.11.2014 Big Data definition? There is no clear definition for BIG DATA BIG DATA is more of a concept than precise term 1 21.11.14

More information

Customized Report- Big Data

Customized Report- Big Data GINeVRA Digital Research Hub Customized Report- Big Data 1 2014. All Rights Reserved. Agenda Context Challenges and opportunities Solutions Market Case studies Recommendations 2 2014. All Rights Reserved.

More information

Challenges for Data Driven Systems

Challenges for Data Driven Systems Challenges for Data Driven Systems Eiko Yoneki University of Cambridge Computer Laboratory Quick History of Data Management 4000 B C Manual recording From tablets to papyrus to paper A. Payberah 2014 2

More information

What is Customer Relationship Management? Customer Relationship Management Analytics. Customer Life Cycle. Objectives of CRM. Three Types of CRM

What is Customer Relationship Management? Customer Relationship Management Analytics. Customer Life Cycle. Objectives of CRM. Three Types of CRM Relationship Management Analytics What is Relationship Management? CRM is a strategy which utilises a combination of Week 13: Summary information technology policies processes, employees to develop profitable

More information

Collaborative Big Data Analytics. Copyright 2012 EMC Corporation. All rights reserved.

Collaborative Big Data Analytics. Copyright 2012 EMC Corporation. All rights reserved. Collaborative Big Data Analytics 1 Big Data Is Less About Size, And More About Freedom TechCrunch!!!!!!!!! Total data: bigger than big data 451 Group Findings: Big Data Is More Extreme Than Volume Gartner!!!!!!!!!!!!!!!

More information

CS Master Level Courses and Areas COURSE DESCRIPTIONS. CSCI 521 Real-Time Systems. CSCI 522 High Performance Computing

CS Master Level Courses and Areas COURSE DESCRIPTIONS. CSCI 521 Real-Time Systems. CSCI 522 High Performance Computing CS Master Level Courses and Areas The graduate courses offered may change over time, in response to new developments in computer science and the interests of faculty and students; the list of graduate

More information

DATA SCIENCE CURRICULUM WEEK 1 ONLINE PRE-WORK INSTALLING PACKAGES COMMAND LINE CODE EDITOR PYTHON STATISTICS PROJECT O5 PROJECT O3 PROJECT O2

DATA SCIENCE CURRICULUM WEEK 1 ONLINE PRE-WORK INSTALLING PACKAGES COMMAND LINE CODE EDITOR PYTHON STATISTICS PROJECT O5 PROJECT O3 PROJECT O2 DATA SCIENCE CURRICULUM Before class even begins, students start an at-home pre-work phase. When they convene in class, students spend the first eight weeks doing iterative, project-centered skill acquisition.

More information

Defending Networks with Incomplete Information: A Machine Learning Approach. Alexandre Pinto alexcp@mlsecproject.org @alexcpsec @MLSecProject

Defending Networks with Incomplete Information: A Machine Learning Approach. Alexandre Pinto alexcp@mlsecproject.org @alexcpsec @MLSecProject Defending Networks with Incomplete Information: A Machine Learning Approach Alexandre Pinto alexcp@mlsecproject.org @alexcpsec @MLSecProject Agenda Security Monitoring: We are doing it wrong Machine Learning

More information

Improving Data Processing Speed in Big Data Analytics Using. HDFS Method

Improving Data Processing Speed in Big Data Analytics Using. HDFS Method Improving Data Processing Speed in Big Data Analytics Using HDFS Method M.R.Sundarakumar Assistant Professor, Department Of Computer Science and Engineering, R.V College of Engineering, Bangalore, India

More information

Big Data and Marketing

Big Data and Marketing Big Data and Marketing Professor Venky Shankar Coleman Chair in Marketing Director, Center for Retailing Studies Mays Business School Texas A&M University http://www.venkyshankar.com venky@venkyshankar.com

More information

Data Mining for Customer Service Support. Senioritis Seminar Presentation Megan Boice Jay Carter Nick Linke KC Tobin

Data Mining for Customer Service Support. Senioritis Seminar Presentation Megan Boice Jay Carter Nick Linke KC Tobin Data Mining for Customer Service Support Senioritis Seminar Presentation Megan Boice Jay Carter Nick Linke KC Tobin Traditional Hotline Services Problem Traditional Customer Service Support (manufacturing)

More information

Our Raison d'être. Identify major choice decision points. Leverage Analytical Tools and Techniques to solve problems hindering these decision points

Our Raison d'être. Identify major choice decision points. Leverage Analytical Tools and Techniques to solve problems hindering these decision points Analytic 360 Our Raison d'être Identify major choice decision points Leverage Analytical Tools and Techniques to solve problems hindering these decision points Empowerment through Intelligence Our Suite

More information

PREDICTIVE MARKETING, DIGITAL ATTRIBUTION, OPTIMIZATION, AND DATA-DRIVEN PERSONALIZATION

PREDICTIVE MARKETING, DIGITAL ATTRIBUTION, OPTIMIZATION, AND DATA-DRIVEN PERSONALIZATION PREDICTIVE MARKETING, DIGITAL ATTRIBUTION, OPTIMIZATION, AND DATA-DRIVEN PERSONALIZATION A m a r t y a B h a t t a c h a r j y & S u n e e l G r o v e r P r i n c i p a l S o l u t i o n A r c h i t e

More information

Why is Internal Audit so Hard?

Why is Internal Audit so Hard? Why is Internal Audit so Hard? 2 2014 Why is Internal Audit so Hard? 3 2014 Why is Internal Audit so Hard? Waste Abuse Fraud 4 2014 Waves of Change 1 st Wave Personal Computers Electronic Spreadsheets

More information

Understanding Your Customer Journey by Extending Adobe Analytics with Big Data

Understanding Your Customer Journey by Extending Adobe Analytics with Big Data SOLUTION BRIEF Understanding Your Customer Journey by Extending Adobe Analytics with Big Data Business Challenge Today s digital marketing teams are overwhelmed by the volume and variety of customer interaction

More information

Bringing Big Data Modelling into the Hands of Domain Experts

Bringing Big Data Modelling into the Hands of Domain Experts Bringing Big Data Modelling into the Hands of Domain Experts David Willingham Senior Application Engineer MathWorks david.willingham@mathworks.com.au 2015 The MathWorks, Inc. 1 Data is the sword of the

More information

Azure Machine Learning, SQL Data Mining and R

Azure Machine Learning, SQL Data Mining and R Azure Machine Learning, SQL Data Mining and R Day-by-day Agenda Prerequisites No formal prerequisites. Basic knowledge of SQL Server Data Tools, Excel and any analytical experience helps. Best of all:

More information

Integrating a Big Data Platform into Government:

Integrating a Big Data Platform into Government: Integrating a Big Data Platform into Government: Drive Better Decisions for Policy and Program Outcomes John Haddad, Senior Director Product Marketing, Informatica Digital Government Institute s Government

More information

Artificial Intelligence and Robotics @ Politecnico di Milano. Presented by Matteo Matteucci

Artificial Intelligence and Robotics @ Politecnico di Milano. Presented by Matteo Matteucci 1 Artificial Intelligence and Robotics @ Politecnico di Milano Presented by Matteo Matteucci What is Artificial Intelligence «The field of theory & development of computer systems able to perform tasks

More information

Core Curriculum to the Course:

Core Curriculum to the Course: Core Curriculum to the Course: Environmental Science Law Economy for Engineering Accounting for Engineering Production System Planning and Analysis Electric Circuits Logic Circuits Methods for Electric

More information

Machine Learning with MATLAB David Willingham Application Engineer

Machine Learning with MATLAB David Willingham Application Engineer Machine Learning with MATLAB David Willingham Application Engineer 2014 The MathWorks, Inc. 1 Goals Overview of machine learning Machine learning models & techniques available in MATLAB Streamlining the

More information

Data: To BI or not to BI?

Data: To BI or not to BI? NATIONAL CONFERENCE ON BMS, 30 MAY 2013, HILTON HOTEL Challenges in building a BI and Big data analytics system Data: To BI or not to BI? Iva Valerieva, 1 Marketing & Business Development Manager Who Are

More information

DAMA NY DAMA Day October 17, 2013 IBM 590 Madison Avenue 12th floor New York, NY

DAMA NY DAMA Day October 17, 2013 IBM 590 Madison Avenue 12th floor New York, NY Big Data Analytics DAMA NY DAMA Day October 17, 2013 IBM 590 Madison Avenue 12th floor New York, NY Tom Haughey InfoModel, LLC 868 Woodfield Road Franklin Lakes, NJ 07417 201 755 3350 tom.haughey@infomodelusa.com

More information

Big Data. Fast Forward. Putting data to productive use

Big Data. Fast Forward. Putting data to productive use Big Data Putting data to productive use Fast Forward What is big data, and why should you care? Get familiar with big data terminology, technologies, and techniques. Getting started with big data to realize

More information

Industry 4.0 and Big Data

Industry 4.0 and Big Data Industry 4.0 and Big Data Marek Obitko, mobitko@ra.rockwell.com Senior Research Engineer 03/25/2015 PUBLIC PUBLIC - 5058-CO900H 2 Background Joint work with Czech Institute of Informatics, Robotics and

More information

INTERNATIONAL MASTER IN BUSINESS ANALYTICS AND BIG DATA

INTERNATIONAL MASTER IN BUSINESS ANALYTICS AND BIG DATA POLITECNICO DI MILANO GRADUATE SCHOOL OF BUSINESS BABD INTERNATIONAL MASTER IN BUSINESS ANALYTICS AND BIG DATA Courses Description A JOINT PROGRAM WITH POLITECNICO DI MILANO SCHOOL OF MANAGEMENT PRE-COURSES

More information

Surveying the Data Science Skills Landscape in UK Government

Surveying the Data Science Skills Landscape in UK Government Surveying the Data Science Skills Landscape in UK Government What are the skills and capabilities needed for Data Science? Do they exist in UK Government? The Data Science discipline draws from a broad

More information

Big Data: Opportunities & Challenges, Myths & Truths 資 料 來 源 : 台 大 廖 世 偉 教 授 課 程 資 料

Big Data: Opportunities & Challenges, Myths & Truths 資 料 來 源 : 台 大 廖 世 偉 教 授 課 程 資 料 Big Data: Opportunities & Challenges, Myths & Truths 資 料 來 源 : 台 大 廖 世 偉 教 授 課 程 資 料 美 國 13 歲 學 生 用 Big Data 找 出 霸 淩 熱 點 Puri 架 設 網 站 Bullyvention, 藉 由 分 析 Twitter 上 找 出 提 到 跟 霸 凌 相 關 的 詞, 搭 配 地 理 位 置

More information

Zero-in on business decisions through innovation solutions for smart big data management. How to turn volume, variety and velocity into value

Zero-in on business decisions through innovation solutions for smart big data management. How to turn volume, variety and velocity into value Zero-in on business decisions through innovation solutions for smart big data management How to turn volume, variety and velocity into value ON THE LOOKOUT FOR NEW SOURCES OF VALUE CREATION WHAT WILL DRIVE

More information

Chapter ML:XI. XI. Cluster Analysis

Chapter ML:XI. XI. Cluster Analysis Chapter ML:XI XI. Cluster Analysis Data Mining Overview Cluster Analysis Basics Hierarchical Cluster Analysis Iterative Cluster Analysis Density-Based Cluster Analysis Cluster Evaluation Constrained Cluster

More information

Big Data: Rethinking Text Visualization

Big Data: Rethinking Text Visualization Big Data: Rethinking Text Visualization Dr. Anton Heijs anton.heijs@treparel.com Treparel April 8, 2013 Abstract In this white paper we discuss text visualization approaches and how these are important

More information

A Professional Big Data Master s Program to train Computational Specialists

A Professional Big Data Master s Program to train Computational Specialists A Professional Big Data Master s Program to train Computational Specialists Anoop Sarkar, Fred Popowich, Alexandra Fedorova! School of Computing Science! Education for Employable Graduates: Critical Questions

More information

Summer School on Fuzzy Cognitive Maps Methods, Learning Algorithms and Software Tool for Modeling and Decision Making. 4-8 July 2015 (5 days)

Summer School on Fuzzy Cognitive Maps Methods, Learning Algorithms and Software Tool for Modeling and Decision Making. 4-8 July 2015 (5 days) Summer School on Fuzzy Cognitive Maps Methods, Learning Algorithms and Software Tool for Modeling and Decision Making 4-8 July 2015 (5 days) Organized by Prof. Elpiniki Papageorgiou Technological Educational

More information

Big Analytics: A Next Generation Roadmap

Big Analytics: A Next Generation Roadmap Big Analytics: A Next Generation Roadmap Cloud Developers Summit & Expo: October 1, 2014 Neil Fox, CTO: SoftServe, Inc. 2014 SoftServe, Inc. Remember Life Before The Web? 1994 Even Revolutions Take Time

More information

Mastering Big Data. Steve Hoskin, VP and Chief Architect INFORMATICA MDM. October 2015

Mastering Big Data. Steve Hoskin, VP and Chief Architect INFORMATICA MDM. October 2015 Mastering Big Data Steve Hoskin, VP and Chief Architect INFORMATICA MDM October 2015 Agenda About Big Data MDM and Big Data The Importance of Relationships Big Data Use Cases About Big Data Big Data is

More information

This Symposium brought to you by www.ttcus.com

This Symposium brought to you by www.ttcus.com This Symposium brought to you by www.ttcus.com Linkedin/Group: Technology Training Corporation @Techtrain Technology Training Corporation www.ttcus.com Big Data Analytics as a Service (BDAaaS) Big Data

More information

BIOINF 585 Fall 2015 Machine Learning for Systems Biology & Clinical Informatics http://www.ccmb.med.umich.edu/node/1376

BIOINF 585 Fall 2015 Machine Learning for Systems Biology & Clinical Informatics http://www.ccmb.med.umich.edu/node/1376 Course Director: Dr. Kayvan Najarian (DCM&B, kayvan@umich.edu) Lectures: Labs: Mondays and Wednesdays 9:00 AM -10:30 AM Rm. 2065 Palmer Commons Bldg. Wednesdays 10:30 AM 11:30 AM (alternate weeks) Rm.

More information

Big Data Analytics. An Introduction. Oliver Fuchsberger University of Paderborn 2014

Big Data Analytics. An Introduction. Oliver Fuchsberger University of Paderborn 2014 Big Data Analytics An Introduction Oliver Fuchsberger University of Paderborn 2014 Table of Contents I. Introduction & Motivation What is Big Data Analytics? Why is it so important? II. Techniques & Solutions

More information

Getting to Know Big Data

Getting to Know Big Data Getting to Know Big Data Dr. Putchong Uthayopas Department of Computer Engineering, Faculty of Engineering, Kasetsart University Email: putchong@ku.th Information Tsunami Rapid expansion of Smartphone

More information

Descriptive to Predictive to Prescriptive Analytics: Move Up the Value Chain. Suren Nathan CTO

Descriptive to Predictive to Prescriptive Analytics: Move Up the Value Chain. Suren Nathan CTO Descriptive to Predictive to Prescriptive Analytics: Move Up the Value Chain Suren Nathan CTO What We Do Deliver cloud based predictive analytics solutions to the communications industry to help streamline

More information

Data Science and Business Analytics Certificate Data Science and Business Intelligence Certificate

Data Science and Business Analytics Certificate Data Science and Business Intelligence Certificate Data Science and Business Analytics Certificate Data Science and Business Intelligence Certificate Description The Helzberg School of Management has launched two graduate-level certificates: one in Data

More information

Data Science in Action

Data Science in Action + Data Science in Action Peerapon Vateekul, Ph.D. Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University + Outlines 2 Data Science & Data Scientist Data Mining Analytics with

More information

Data Warehouse design

Data Warehouse design Data Warehouse design Design of Enterprise Systems University of Pavia 10/12/2013 2h for the first; 2h for hadoop - 1- Table of Contents Big Data Overview Big Data DW & BI Big Data Market Hadoop & Mahout

More information

Big Data and Data Science: Behind the Buzz Words

Big Data and Data Science: Behind the Buzz Words Big Data and Data Science: Behind the Buzz Words Peggy Brinkmann, FCAS, MAAA Actuary Milliman, Inc. April 1, 2014 Contents Big data: from hype to value Deconstructing data science Managing big data Analyzing

More information

MEDICAL DATA MINING. Timothy Hays, PhD. Health IT Strategy Executive Dynamics Research Corporation (DRC) December 13, 2012

MEDICAL DATA MINING. Timothy Hays, PhD. Health IT Strategy Executive Dynamics Research Corporation (DRC) December 13, 2012 MEDICAL DATA MINING Timothy Hays, PhD Health IT Strategy Executive Dynamics Research Corporation (DRC) December 13, 2012 2 Healthcare in America Is a VERY Large Domain with Enormous Opportunities for Data

More information

The Big Picture on Big Data. Princeton Section 307 Dinner Meeting December 11, 2013 Richard Herczeg

The Big Picture on Big Data. Princeton Section 307 Dinner Meeting December 11, 2013 Richard Herczeg The Big Picture on Big Data Princeton Section 307 Dinner Meeting December 11, 2013 Richard Herczeg Objective of Talk 1. Deliver a Primer on Big Data. 2. How does this emerging topic apply to Quality? 3.

More information

Augmented Search for IT Data Analytics. New frontier in big log data analysis and application intelligence

Augmented Search for IT Data Analytics. New frontier in big log data analysis and application intelligence Augmented Search for IT Data Analytics New frontier in big log data analysis and application intelligence Business white paper May 2015 IT data is a general name to log data, IT metrics, application data,

More information

Why big data? Lessons from a Decade+ Experiment in Big Data

Why big data? Lessons from a Decade+ Experiment in Big Data Why big data? Lessons from a Decade+ Experiment in Big Data David Belanger PhD Senior Research Fellow Stevens Institute of Technology dbelange@stevens.edu 1 What Does Big Look Like? 7 Image Source Page:

More information

Unlocking the Intelligence in. Big Data. Ron Kasabian General Manager Big Data Solutions Intel Corporation

Unlocking the Intelligence in. Big Data. Ron Kasabian General Manager Big Data Solutions Intel Corporation Unlocking the Intelligence in Big Data Ron Kasabian General Manager Big Data Solutions Intel Corporation Volume & Type of Data What s Driving Big Data? 10X Data growth by 2016 90% unstructured 1 Lower

More information

Data Warehousing and Data Mining in Business Applications

Data Warehousing and Data Mining in Business Applications 133 Data Warehousing and Data Mining in Business Applications Eesha Goel CSE Deptt. GZS-PTU Campus, Bathinda. Abstract Information technology is now required in all aspect of our lives that helps in business

More information

Leveraging Big Data Technologies to Support Research in Unstructured Data Analytics

Leveraging Big Data Technologies to Support Research in Unstructured Data Analytics Leveraging Big Data Technologies to Support Research in Unstructured Data Analytics BY FRANÇOYS LABONTÉ GENERAL MANAGER JUNE 16, 2015 Principal partenaire financier WWW.CRIM.CA ABOUT CRIM Applied research

More information

Research of Postal Data mining system based on big data

Research of Postal Data mining system based on big data 3rd International Conference on Mechatronics, Robotics and Automation (ICMRA 2015) Research of Postal Data mining system based on big data Xia Hu 1, Yanfeng Jin 1, Fan Wang 1 1 Shi Jiazhuang Post & Telecommunication

More information

A Framework of User-Driven Data Analytics in the Cloud for Course Management

A Framework of User-Driven Data Analytics in the Cloud for Course Management A Framework of User-Driven Data Analytics in the Cloud for Course Management Jie ZHANG 1, William Chandra TJHI 2, Bu Sung LEE 1, Kee Khoon LEE 2, Julita VASSILEVA 3 & Chee Kit LOOI 4 1 School of Computer

More information

COURSE CATALOGUE 2013-2014

COURSE CATALOGUE 2013-2014 COURSE CATALOGUE 201-201 Field: COMPUTER SCIENCE Programme: Bachelor s Degree Programme in Computer Science (Informatics) Length of studies: years (6 semesters) Number of ECTS Credits: 180 +0 for the B.Sc.

More information

Introduction to Data Mining and Machine Learning Techniques. Iza Moise, Evangelos Pournaras, Dirk Helbing

Introduction to Data Mining and Machine Learning Techniques. Iza Moise, Evangelos Pournaras, Dirk Helbing Introduction to Data Mining and Machine Learning Techniques Iza Moise, Evangelos Pournaras, Dirk Helbing Iza Moise, Evangelos Pournaras, Dirk Helbing 1 Overview Main principles of data mining Definition

More information

Hadoop Beyond Hype: Complex Adaptive Systems Conference Nov 16, 2012. Viswa Sharma Solutions Architect Tata Consultancy Services

Hadoop Beyond Hype: Complex Adaptive Systems Conference Nov 16, 2012. Viswa Sharma Solutions Architect Tata Consultancy Services Hadoop Beyond Hype: Complex Adaptive Systems Conference Nov 16, 2012 Viswa Sharma Solutions Architect Tata Consultancy Services 1 Agenda What is Hadoop Why Hadoop? The Net Generation is here Sizing the

More information

Pragmatic Web 4.0. Towards an active and interactive Semantic Media Web. Fachtagung Semantische Technologien 26.-27. September 2013 HU Berlin

Pragmatic Web 4.0. Towards an active and interactive Semantic Media Web. Fachtagung Semantische Technologien 26.-27. September 2013 HU Berlin Pragmatic Web 4.0 Towards an active and interactive Semantic Media Web Prof. Dr. Adrian Paschke Arbeitsgruppe Corporate Semantic Web (AG-CSW) Institut für Informatik, Freie Universität Berlin paschke@inf.fu-berlin

More information

DATA ANALYTICS USING R

DATA ANALYTICS USING R DATA ANALYTICS USING R Duration: 90 Hours Intended audience and scope: The course is targeted at fresh engineers, practicing engineers and scientists who are interested in learning and understanding data

More information

Enhanced Boosted Trees Technique for Customer Churn Prediction Model

Enhanced Boosted Trees Technique for Customer Churn Prediction Model IOSR Journal of Engineering (IOSRJEN) ISSN (e): 2250-3021, ISSN (p): 2278-8719 Vol. 04, Issue 03 (March. 2014), V5 PP 41-45 www.iosrjen.org Enhanced Boosted Trees Technique for Customer Churn Prediction

More information

Big Data and Analytics: Challenges and Opportunities

Big Data and Analytics: Challenges and Opportunities Big Data and Analytics: Challenges and Opportunities Dr. Amin Beheshti Lecturer and Senior Research Associate University of New South Wales, Australia (Service Oriented Computing Group, CSE) Talk: Sharif

More information

SAP Solution Brief SAP HANA. Transform Your Future with Better Business Insight Using Predictive Analytics

SAP Solution Brief SAP HANA. Transform Your Future with Better Business Insight Using Predictive Analytics SAP Brief SAP HANA Objectives Transform Your Future with Better Business Insight Using Predictive Analytics Dealing with the new reality Dealing with the new reality Organizations like yours can identify

More information

Foundations of Business Intelligence: Databases and Information Management

Foundations of Business Intelligence: Databases and Information Management Foundations of Business Intelligence: Databases and Information Management Problem: HP s numerous systems unable to deliver the information needed for a complete picture of business operations, lack of

More information

SAP Predictive Analysis: Strategy, Value Proposition

SAP Predictive Analysis: Strategy, Value Proposition September 10-13, 2012 Orlando, Florida SAP Predictive Analysis: Strategy, Value Proposition Charles Gadalla, Solution Management, SAP Business Intelligence Manavendra Misra, Chief Knowledge Officer, Cognilytics

More information

PDF PREVIEW EMERGING TECHNOLOGIES. Applying Technologies for Social Media Data Analysis

PDF PREVIEW EMERGING TECHNOLOGIES. Applying Technologies for Social Media Data Analysis VOLUME 34 BEST PRACTICES IN BUSINESS INTELLIGENCE AND DATA WAREHOUSING FROM LEADING SOLUTION PROVIDERS AND EXPERTS PDF PREVIEW IN EMERGING TECHNOLOGIES POWERFUL CASE STUDIES AND LESSONS LEARNED FOCUSING

More information

Data Mining Analytics for Business Intelligence and Decision Support

Data Mining Analytics for Business Intelligence and Decision Support Data Mining Analytics for Business Intelligence and Decision Support Chid Apte, T.J. Watson Research Center, IBM Research Division Knowledge Discovery and Data Mining (KDD) techniques are used for analyzing

More information

Database Marketing, Business Intelligence and Knowledge Discovery

Database Marketing, Business Intelligence and Knowledge Discovery Database Marketing, Business Intelligence and Knowledge Discovery Note: Using material from Tan / Steinbach / Kumar (2005) Introduction to Data Mining,, Addison Wesley; and Cios / Pedrycz / Swiniarski

More information

Doctor of Philosophy in Computer Science

Doctor of Philosophy in Computer Science Doctor of Philosophy in Computer Science Background/Rationale The program aims to develop computer scientists who are armed with methods, tools and techniques from both theoretical and systems aspects

More information

ANALYTICS STRATEGY: creating a roadmap for success

ANALYTICS STRATEGY: creating a roadmap for success ANALYTICS STRATEGY: creating a roadmap for success Companies in the capital and commodity markets are looking at analytics for opportunities to improve revenue and cost savings. Yet, many firms are struggling

More information

Past, present, and future Analytics at Loyalty NZ. V. Morder SUNZ 2014

Past, present, and future Analytics at Loyalty NZ. V. Morder SUNZ 2014 Past, present, and future Analytics at Loyalty NZ V. Morder SUNZ 2014 Contents Visions The undisputed customer loyalty experts To create, maintain and motivate loyal customers for our Participants Win

More information

COMP9321 Web Application Engineering

COMP9321 Web Application Engineering COMP9321 Web Application Engineering Semester 2, 2015 Dr. Amin Beheshti Service Oriented Computing Group, CSE, UNSW Australia Week 11 (Part II) http://webapps.cse.unsw.edu.au/webcms2/course/index.php?cid=2411

More information

Understanding the Value of In-Memory in the IT Landscape

Understanding the Value of In-Memory in the IT Landscape February 2012 Understing the Value of In-Memory in Sponsored by QlikView Contents The Many Faces of In-Memory 1 The Meaning of In-Memory 2 The Data Analysis Value Chain Your Goals 3 Mapping Vendors to

More information

Digging for Gold: Business Usage for Data Mining Kim Foster, CoreTech Consulting Group, Inc., King of Prussia, PA

Digging for Gold: Business Usage for Data Mining Kim Foster, CoreTech Consulting Group, Inc., King of Prussia, PA Digging for Gold: Business Usage for Data Mining Kim Foster, CoreTech Consulting Group, Inc., King of Prussia, PA ABSTRACT Current trends in data mining allow the business community to take advantage of

More information