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Passion Driven Statistics A S upportive , Project-based , M ultidisciplinary

Passion Driven Statistics A S upportive , Project-based , M ultidisciplinary I ntroductory C urriculum. Lisa Dierker , Arielle Selya and Jeffrey Nolan Part 1, Friday, August 2, 3:30 pm - 5:30 pm Part 2, Saturday, August 3, 3:30 pm - 5:30 pm Connecticut Convention Center

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Passion Driven Statistics A S upportive , Project-based , M ultidisciplinary

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  1. Passion Driven Statistics A Supportive, Project-based, Multidisciplinary Introductory Curriculum Lisa Dierker, Arielle Selyaand Jeffrey Nolan Part 1, Friday, August 2, 3:30 pm - 5:30 pmPart 2, Saturday, August 3, 3:30 pm - 5:30 pm Connecticut Convention Center Room 25

  2. We’ll start after clip (3 mins) Starlight over Wesleyan

  3. Speakers Lisa Dierker, PhD* Professor, Psychology Chair, Quantitative Analysis Center Arielle Selya, PhD Research Professor Jeff Nolan Visiting Instructor * In China delivering a Passion-Driven Statistics course

  4. 2-Day Overview • Day 1 Fri, 3:00 – 5:00 Context & background Course format & materials Results to-date Lab activity & discussion • Day 2 1 Sat, 3:00 – 5:00 Lab activity completed Samples of student work Live demonstration using R Challenges & discussion

  5. Mathfest Description • This minicourse exposes participants to a multidisciplinary, project-based model for teaching introductory statistics. • We will present new learning materials and innovative teaching strategies that directly and creatively tackle many of the most significant challenges currently faced by introductory statistics instructors and students. • The curriculum is aimed at taking advantage of students’ natural curiosity and providing a common language for approaching questions across numerous scientific disciplines. • Core features of this curriculum include providing opportunities for students to flexibly apply their knowledge, the use of computing as a window to core statistical concepts, and supporting students with varying levels of preparation.

  6. Introduction by Lisa Dierker Lisa Dierker, PhD Wesleyan University

  7. What we will talk about Passion Driven Statistics Introductory Curriculum A Supportive, Project-based, Multidisciplinary • Approach to delivering an introductory course • American Statistical Association’s Guidelines (GAISE) • Valuable across disciplines to open opportunities • Aim is to INVITE new talent to the field • NOT GATEKEEPING • Provide high-availability support for SUCCESS

  8. What we do in the course We fan into flame that unique, idiosyncraticspark of personal passion by demonstrating how statistics can fuel that flame

  9. So what?

  10. Why care? Why change? Change what? What specifically? What, no math!? Why Wesleyan? Data is transforming society Transformation facilitated by statistics Calls for change in way statistics is taught Economic necessity ASA endorsed guidelines (GAISE) Intrinsic motivation PROJECTS Under-represented groups TALENT Concepts, data, software TOOLS High-availability assistance SUPPORT Eliminate gatekeeper Expand the pool of talent Invigorate the field Tradition of learning through discovery

  11. The changing world Hans Rosling 00:25 – 05:15

  12. Transformation • Advancements in the field of statistics are among the greatest achievements of the 20th Century • It’s impact is seen EVERYWHERE • The world has changed dramatically in a remarkably short period of time because of the application of statistics • The effects are lasting and irreversible

  13. Accelerator of innovation Agriculture Medicine Engineering Manufacturing Marketing Finance ... Karl Pearson Ronald A. Fisher Statistics accelerates innovation in other fields

  14. Marketing "Half the money I spend on advertising is wasted; the trouble is I don't know which half." “Big data” refers to vast data sets… Ranging from a few dozen terabytes to many petabytes, they’re so extensive and complex that specialized software tools and analytics expertise are required to collect, manage, and mine them. John Wanamaker Selling into Micromarkets July-August 2012

  15. Value of information "Information about money has become almost as importantas money itself." Decisions are informed by the meaning of data rendered through statistics Walter Wriston Liberal Arts school History major Father History Professor Wriston built Citibank into America’s largest bank

  16. Big Data: V3 Supercomputer = Personal Computer Sensors, Connected, Available VolumeZettabytes Velocity Streaming Variety 80% unstructured growth 15x structured Data Stored in the World 1000 KB kilobyte 10002 MB megabyte 10003 GB gigabyte 10004 TB terabyte 10005PBpetabyte 2000 800,000 PB 10006 EBexabyte 10007 ZBzettabyte 2020 35 ZB 10008 YByottabyte 1021 35 sextillion bytes Facebook 10 TB daily Twitter 7 TB daily

  17. Data: Economic Asset Balance Sheet Assets Cash Commodities Property, Plant & Equipment Goodwill Intellectual Property Agricultural Industrial Information Data “Data is a new economic asset” http://www.weforum.org/reports/big-data-big-impact-new-possibilities-international-development

  18. Help Wanted Jobs Retrained and New Hires Analytical experts 150,000 – 190,000 Analytically literate 1,500,000

  19. Our Challenge Attract, retain & motivate the next generation Aerospace Industry saying: “Nice jet, no pilot” • Recruit talent from historically overlooked sources • Recognizeintrinsic motivation as the key driver • Invite don’t gate keep : “Love them through the process!”

  20. Pilot Training!

  21. Retooling Stat Ed (GAISE) • Recommendations • Concepts • Literacy, Thinking • Active Learning • Technology • Real Data • Assessments What How Feedback No Math!?

  22. Curriculum Guidelines George Cobb 22:05 – 23:50

  23. Attraction & Retention 1 : 5 : 20 Instructor : TAs : Students New Courses Opportunities Responsibilities Peer Tutor Apprenticeship Internships TA - Manager 1 Faculty Research Peer Tutor 5 Research Methods 6 Honors Thesis TA - Lead Statistical Consulting 30 Independent Study TA 6 Sections @ 20 students QAC201 Data Analysis

  24. Student request Hi Professor Selya, I am a senior at Wesleyan and I am majoring in Art History. I am interested in taking QAC 201, but I see that there are no reserved spots for senior non-majors. I am planning on applying to graduate programs and I'm interested in the research opportunities that this course would afford me. Do you think that there is a possibility of getting into this course? 

  25. Try it! Wesleyan is a small liberal arts college that has no specific course requirements. The QAC201 course, based on the ASA endorsed guidelines, now attractsmore students than all other statistics courses combined. We invite you to share the material we’ve created, to learn and discover as we have, and to develop your own version of Passion Driven Statistics.

  26. Summary √ Why care? Why change? Change what? What specifically? What, no math!? Why Wesleyan? Data is transforming society Transformation facilitated by statistics Calls for change in way statistics is taught Economic necessity ASA endorsed guidelines (GAISE) Intrinsic motivation PROJECTS Under-represented groups TALENT Concepts, data, software TOOLS High-availability assistance SUPPORT Eliminate gatekeeper Expand the pool of talent Invigorate the field Tradition of learning through discovery √ √ √

  27. Thank You!

  28. Passion Driven Statistics A Supportive, Project-based, Multidisciplinary Introductory Curriculum Lisa Dierker, Arielle Selyaand Jeffrey Nolan Part 1, Friday, August 2, 3:30 pm - 5:30 pmPart 2, Saturday, August 3, 3:30 pm - 5:30 pm Connecticut Convention Center Room 25

  29. Begin with end in mind 3 2 4 Poster Presentation to Outside Reviewers 4,5 1 10 2 4 2 NESARC: Addiction ADD Health: Adolescent Health CT Mastery Test: Education Caterpillar: Biology GapMinder: Economics Text/PDF Links: Videos, Code samples Datasets & Software

  30. Logistics: When, Where Schedule: Sequence of Events

  31. NESARC Code Book (NESARC)

  32. NESARC Codebook 3,000 + Variables !

  33. Formulating the question Applied Data Analysis QAC201-04 Fall 2012 Question Behavior Y Describe, Discover, Explain Predict, Intervene, Control Approach Behavior Y = Function (Nature X1 , Nurture X2) Formulation Classification of Factors: Nature X1 (X11, X12, X13, X14, X15) Psychographic Medical Demographic NurtureX2 (X21, X22, X23, X24, X25) Family History

  34. Thank You!

  35. Starlight over Wesleyan Lisa Dierker, PhD Wesleyan University Hans Rosling 00:25 – 05:15 George Cobb 22:05 – 23:50 WeleyanQAC 201 Applied Data Analysis LECTURES

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