Educating "Business Analytics Practitioner"


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  • 05/10/10
  • Educating "Business Analytics Practitioner"

    1. 1. Educating “Business Analytics Practitioner” University of Technology Sydney Simeon J. Simoff
    2. 2. Outline <ul><li>The “data miner” profession </li></ul><ul><li>Existing degrees </li></ul><ul><li>UTS approach </li></ul><ul><li>SAS contribution and collaboration </li></ul>
    3. 3. The “Data miner” profession <ul><li>Phillipa Yelland, “Sifting through the stats”, Australian Personal Computer magazine, March 2002 </li></ul><ul><li>“ Data mining is one of the least-understood ‘new’ areas in IT careers .” </li></ul><ul><li>“… business intelligence tools and services (the polite face commercial data mining likes to wear) will be a $24 billion market by 2005. </li></ul>
    4. 4. Data miners <ul><li>Data miners are the SAS of the IT industry, and it's not a job for beginners . Demand is strong for people who have the technical skills combined with business knowledge . &quot;To produce useable results, data miners must draw on advanced analytical approaches such as predictive modeling, association discovery and sequence discovery.“ </li></ul><ul><li>Peter Norris Business Manager Computer Associates </li></ul>
    5. 5. Data Miners <ul><li>“ The required blend of skills makes good data miners a rare breed .” </li></ul><ul><li>Ronnie Chansenior IT specialist IBM's DB2 team </li></ul><ul><li>&quot;To this shopping basket of skills, add an investigative mind and enthusiasm . Above all, a good data miner must be persistent .&quot; </li></ul><ul><li>Darren Adams Managing Director Concord Communications </li></ul><ul><li>While the “data miner” has not quite cut it as a professional title , the mining industry has not logged any complaints to date! [8 September 2004] </li></ul><ul><li>Tatiana Andronache , Sympatico Inc., Toronto, Canada </li></ul>
    6. 6. Data Mining and Business Intelligence Increasing potential to support business decisions End User Business Analyst Data Analyst DBA Making Informed Decisions Information Presentation Visualization Techniques Data Mining & Business metrics Information Discovery Data Exploration OLAP, MDA Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts Data Sources Paper, Files, Information Providers, Database Systems, OLTP Data miner
    7. 7. Degrees in Data Mining (not complete, but …) <ul><li>Coursework Master degrees in Data Mining </li></ul><ul><ul><li>Central Connecticut State University (USA) [Master of Science in Data Mining] </li></ul></ul><ul><ul><li>University of Central Florida (USA) [Dept of Stats] [with SAS] [Master of Science in Data Mining] </li></ul></ul><ul><ul><li>University of Denver [College of Business] [Master of Science in Data Mining] </li></ul></ul><ul><ul><li>Wessex Institute of Technology (UK) & the University of Radioelectronics, Ukraine [ International Master Degree in Information Systems, Data Mining & Knowledge Discovery] </li></ul></ul><ul><li>Part of the PhD research degrees in many universities </li></ul>
    8. 8. UTS Coursework degrees in data mining <ul><li>Master of Science in Professional Computing [Specialisation in Data Mining] [12 subjects] </li></ul><ul><li>Master of Science in Advanced Computing [Extended] [Specialisation in Data Mining] [16 subjects] </li></ul><ul><li>More on the degrees </li></ul>
    9. 9. MSc in Professional Computing (DM) <ul><li>Oriented to Industry Practitioners </li></ul><ul><li>Course materials: </li></ul><ul><ul><li>Theoretical aspects in the context of practice </li></ul></ul><ul><ul><li>Practical labs in the context of real world tools and problems [working with GB data sets] </li></ul></ul><ul><ul><li>Assignments – real world problems [ideally should come from student workplace] </li></ul></ul>
    10. 10. Data Mining: Confluence of Disciplines Data Mining Database Technology Statistics Other Disciplines Information Science Artificial Intelligence Visualisation <ul><li>HCI </li></ul><ul><li>Software Engineering </li></ul><ul><li>High Performance Computing </li></ul>
    11. 11. 32130 Principles and Practice of Data Mining 32131 Data Mining and Visualisation in e-Business 32146 Data and Information Visualisation 32150 Multimedia Data Mining 32513 Advanced Machine Learning 32008 Data Mining in Bioinformatics Choice from 6 Statistical subjects Choice from 8 Business subjects Choice from 6 Computer Graphics and Digital Media subjects Choice from 4 e-Business Technology subjects + + Choice from 4 HCI subjects Choice from 4 Software engineering subjects Choice from 6 Computing/IT subjects
    12. 12. SAS Enterprise Miner, Text Miner, etc. <ul><li>32130 Principles and Practice of Data Mining </li></ul><ul><ul><li>exposes students to a broad range of tools </li></ul></ul><ul><ul><li>SAS EM – illustrates the notion of “integrated data mining environment” and “data mining as a process” </li></ul></ul><ul><ul><li>Major assignment – uses SAS EM </li></ul></ul><ul><li>32130 Data Mining and Visualisation in e-Business </li></ul><ul><ul><li>SAS EM + Text Miner: main tools (plus a number of Web mining tools (usage and content) and Web mining bots </li></ul></ul><ul><li>Some students go straight in the businesses, others go on further strengthening of their practical skills in SAS programming. </li></ul>
    13. 13. SAS support to these degrees <ul><li>SAS EM licensing </li></ul><ul><li>SAS prizes for best student project in 32130 Principles and Practice of Data Mining in 31040 Data Mining and Knowledge Discovery </li></ul><ul><li>SAS guest lecturers (Doug Campbell, Peter O’Hanlon) </li></ul><ul><li>Next step: “Deeper” engagement of students in projects with SAS. </li></ul>
    14. 14. Interested [in any role] <ul><li>Simeon J. Simoff </li></ul><ul><li>Faculty of Information Technology University of Technology Sydney </li></ul><ul><li>Tel: 9514 1103 Fax: 9514 1807 email: </li></ul>