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See it in SPSS

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Upcoming Events: Online: Clementine for Commercial Sector: Feb. 24. In-Person. Chicago: Feb. 24 ... (312) 651-3410 'See it in SPSS' events: www.spss.com/seeit ... – PowerPoint PPT presentation

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Title: See it in SPSS


1
See it in SPSS
  • A series of in-person or online opportunities for
    SPSS customers to find out about new and existing
    SPSS products.
  • Product demonstrations
  • Product previews
  • QA with SPSS experts
  •  

Upcoming Events Online Clementine for
Commercial Sector Feb. 24 In-Person Chicago
Feb. 24 Atlanta April 22
For more information or to register, go to
www.spss.com/seeit
2
See it in SPSS Clementine for Higher Education
  • Prety Widjaja
  • Systems Engineer
  • February 10, 2004

3
Todays Agenda
  • Challenges in higher education
  • Data mining defined
  • Data mining in higher education
  • Case studies
  • Clementine demonstration

4
Challenges in Higher Education
  • Institutional effectiveness
  • Student learning outcome assessment
  • Enrollment management
  • Achieving optimum attraction, retention and
    persistence goals
  • Marketing
  • Increasing competition for students
  • Alumni

5
Institutional effectiveness
Getting to know your students
  • Which students make greatest use of institutional
    services?
  • What courses provide high full-time equivalent
    students (FTES) and allow better use of space?
  • What are the patterns in course taking?
  • What courses tend to be taken as a group?

6
Enrollment management
Helping your students succeed
  • Who are our best students?
  • Where do our students come from?
  • Who is most likely to return for another
    semester?
  • Who is most likely to fail or drop out?

7
Marketing
Making the best use of tight budgets
  • Who is most likely to respond to our new
    campaign?
  • Which type of marketing/recruiting works best?
  • Where should we focus our advertising and
    recruiting?

8
Alumni
Continuing the relationship
  • What are the different types/groups of alumni?
  • Who is likely to pledge, for how much, and when?
  • Where and on whom should we focus our fundraising
    drives?

9
What is Data Mining?
  • The process of discovering meaningful new
    correlations, patterns, and trends by sifting
    through large amounts of data stored in
    repositories and by using pattern recognition
    technologies as well as statistical and
    mathematical techniques.
  • The Gartner Group

10
Data mining
  • Is
  • A user-centric, interactive process which
    leverages analysis technologies and computing
    power
  • Computers and algorithms dont mine data people
    do!
  • Is not
  • Blind application of analysis/modeling algorithms
  • Brute-force crunching of bulk data

11
Data Mining Methodology
CRISP-DM industry standard data mining
methodology
12
Data Mining with Clementine
  • Industry-leading workbench for data mining
  • Comprehensive range of tools for all stages of
    the data mining process
  • Pioneered visual approach for maximum
    productivity
  • Multiple modeling techniques to predict future
    events

13
Case Studies
14
Case Studies
  • Babson College
  • Challengeprovide innovative curriculum for their
    graduate students to gain a competitive advantage
    in the business world
  • Solutionintegrating SPSS Incs Clementine into
    their MBA curriculum
  • Result
  • Provided students ability to understand and
    synthesize data
  • Increased software investment by identifying
    additional applications

15
Case Studies
  • Cabrillo College
  • Challenge Identify student enrollment patterns
    and tendencies
  • Approach
  • Use a combination of both segmentation and
    clustering techniques to establish typologies and
    to understand grouping dynamics as well as
    predictive modeling.
  • Predict students probability of completing a
    class, transferring out, or leaving the school
    altogether
  • Results
  • Reduced marketing costs and improved curriculum
    offerings
  • Increased revenue through student retention

16
Case Studies
Data Mining and Knowledge Management, Jing
Luan, Ph.d., Terrence Willett, M.S.
  • Challenge Prediction of students who will be
    placed on academic probation.
  • Approach produce early warning model by using
    rule induction C5.0 algorithm.
  • Result Identify high risk students and provide
    academic assistance where necessary

17
Clementine Demonstration
If you are not automatically taken to the "Shared
Application" screen during the demonstration,
please click on the "Shared Application" button
at the bottom of your screen.
18
Benefit
  • Unparalleled productivity
  • Workflow provides complete support of the
    complete CRISP-DM methodology
  • Breadth of techniques for modeling and processing
  • Openness
  • Leverages investment in existing systems
  • Scalability
  • Scales the entire interactive data mining process
  • Deployment
  • Fast and cost-effective delivery of data mining
    solutions

19
Special Offer!
  • Purchase Clementine by March 29th and receive 20
    off the Introduction to Clementine training
    class!

20
More information
http//www.spss.com/clementine
21
Question and answer
Sales sales_at_spss.com Technical
supporthttps//www.spss.com/tech/techtalk.htm(3
12) 651-3410 See it in SPSS eventswww.spss.com
/seeit
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