For the Quantitative Methodologies for Design Research (定量研究方法) course for Ph.D. students at Tongji University in spring 2017, Susu Nousala invited me to join the team of instructors in collaborative education in Shanghai. Experts were brought in during the course to guide the graduate students.
While I’m comfortable with the mathematics underlying statistical analysis, I have a lot of practical experience of working with business executives who aren’t. Thus, my approach to working with data relies a lot on presentation graphics to defog the phenomena. While the label of data science began to rise circa 2012, I’ve had the benefit of practical experience that predates that.
In my first professional assignment in IBM Canada in 1985, data science would have been called econometrics. My work included forecasting country sales, based on price-performance indexes (from the mainframe, midrange and personal computer product divisions) and economic outlooks from Statistics Canada. Two years before the Macintosh II would bring color to personal computing, I was an early adopter of GRAFSTAT: “An APL system for interactive scientific-engineering graphics and data analysis” developed at IBM Research. This would eventually become an IBM program product by called AGSS (A Graphical Statistical System) by 1994.
In 1988, I had an assignment where data science would have been called marketing science. I was sent to California to work in the IBM partnership with Metaphor Computer Systems. This was a Xerox PARC spin-off with a vision that predated the first web page on the World Wide Web by a few years. These activities led me into the TIMS Marketing Science Conference in 1990, cofounding the Canadian Centre for Marketing Information Technologies (C2MIT) and contributing chapters to The Marketing Information Revolution published in 1994.
This journey led me to appreciate the selection and use of computer-based tools for quantitative analysis. Today, the two leading platforms in “Data Science 101” are Python (a general purpose language with statistical libraries), and the R Project for Statistical Computing (a specialized package for data analysis and visualization). Both are open source projects, and free to download and use on personal computers. I tried both. R is a higher level programming language more similar to the APL programming language that gets work done more quickly. For statistical work, I recommend R over Python (although APL is a theoretically better implementation).
Since I live in Toronto, I attended the February session of Data Science with R – Bootcamp in person, at Ryerson University. There, I was watched Polong Lin leading a class through R using the Jupyter notebook, both in (i) an interactive version, and (ii) a printable version. Students had the choice to either follow Polong (i) actively, in a step-by-step execution in the Cognitive Class Virtual Lab (formerly called the Data Scientist Workbench) with a cloud-based R session through their web browsers, or (ii) passively, reading the static printable content.
daviding August 26th, 2017
Posted In: universities
Slides and audio of our joint talk at the RSD5 Symposium on the experiences and learning about leading systems thinking courses are now available.
Over five years, the Creative Sustainability program evolved from pilot into full practice with a series of courses. In reflection, the course instructors better learned how to guide students through teaming, mindset, methods and theory.
The presentation is titled “Curriculum Making for Trito Learning: Wayfaring along a meshwork of systems thinking”. With such a dense title for the Relating Systems Thinking and Design Symposium, our aim was to explain what those chosen words meant.
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The streaming media adds the slides to the audio presentation. In person, in Toronto, we had two instructors from the course speaking: David Ing and Susu Nousala.
Here’s the officially published abstract:
In winter 2016, the Systems Thinking 2 course in the Creative Sustainability (CS) program at Aalto University was led by one of the original curriculum developers from 2010. Over five years, the core CS curriculum had evolved, allowing the level of learning amongst student to advance to a higher level. While this winter 2016 cohort of students was challenged by the intensiveness of the course, satisfaction in the learning appeared to be high.
Following the phenomenological ecological practice theory of Tim Ingold, curriculum making should not be framed primarily as a transmission of information, but instead as a togethering environment where knowledge reproduces amongst the learners. Becoming an authentic systems thinker has each individual progressing on a unique line, wayfaring through an education of attention. Each learner builds on his or her distinct prior experience to stake a position on new ideas, observe the positions of others, and describe a new synthesis in a meshwork of knowledge.
The Systems Thinking 2 course was launched with a orientation where students groups were given 3 weeks to digest references into a group position. Each group then guided classmates through ideas that resonated for them, often amplified through metaphorical stories and exercises. Challenge groups inquired on the positions staked, surfacing deeper questions in dialectic. Each student was then to write a short blog post within a day or two on his or her learning, encouraged on public online web sites where the instructor would comment. Concluding the course, the student groups each prepared an infographic highlighting the most salient content not just of their original positions, but of their appreciation of systems thinking across all they had heard within the past three weeks.
In the logical categories developed by Gregory Bateson, the value of Systems Thinking 2 is in elevating students to becoming trito learners, beyond the levels of proto learning and deutero learning in the prior core courses. These skills are expected to help reduce the commission of errors of the third kind (E3) and fourth kind (E4), in a meta-system of inquiry described by Ian Mitroff.
Patricia Kambitsch created a sketchnote during the presentation.
For people who prefer visuals at their own pace, the slides are available for download.
daviding November 13th, 2016
The Creative Sustainability program at Aalto University recorded the two lectures that they hosted on October 7. They’ve done the post-production work to make the videos available on the web. The recordings are HD-quality, so they can be viewed full screen on Vimeo.
The first talk on “Service Systems, Natural Systems: Systems Approaches to Urban Issues”, given at the Aalto University Design Factory, is at https://vimeo.com/76852952. The slides, on the Coevolving Commons, were originally written for a City Sciences meeting at the University of Toronto, about a year ago.
The second talk on “Design Flaws and Service Systems Breakdowns: Learning from Systems Thinking”, given at the Aalto University Media Factory, is at https://vimeo.com/77131431 . The slides, on the Coevolving Commons, were a preview of the presentation for the Relating Systems Thinking and Design 2 2013 meeting at AHO (The Oslo School of Architecture and Design) later that week.
daviding October 27th, 2013
Offers to lecture for some friends at universities in Europe has led to a schedule of seven lectures at six universities. New presentations slides were created for the Relating Systems Thinking & Design 2 meeting. For first few lectures, I’ll be repurposing slides on other topics.
|Saturday, October 5, 14:00-15:30 EET||Helsinki Metropolia University of Applied Sciences, Master’s Degree Program in Industrial Management (hosted by Marjatta Huhta)||Bulevardi 31, second floor, V114 (building front door locked on weekend, students enter on courtyard side with iron gate)||
|Monday, October 7, 10:00-11:00 EET||Aalto University, CS 0010 Projects (Regional Innovation Ecosystems Workshop), Lecture on “How to handle complex systems in city-planning context?” (hosted by Tiina Laurila)
||Design Factory, Stage, Otaniemi Campus||Service Systems, Natural Systems: Systems Approaches to Urban Issues|
daviding September 30th, 2013
The Cities Centre at the University of Toronto recently hosted a two day workshop on “Finding Connections Towards a Holistic View of City Systems“, as an NSERC Partnership Workshop to bring together academics, industry and government participants. I was privileged to be invited as one of the 30 attendees to discuss potential future collaborations through a systems approach to urban issues. The meeting was hosted by professors Steve Easterbrook and Eric J. Miller, and coordinated by Kathryn Grond.
On the first day, we had three speakers set stage for discussion:
Groups broke out for an exercise developing stories using Drivers of Change cards as triggers, and then writing some future headlines of outcomes that might be a result of future research.
On the second day, the morning was dedicated to 12 “Tools Talks” on emerging tools, techniques, data and models for collaborative work. With a 7-minute target, I raced through a presentation on “Service Systems, Natural Systems: Systems Approaches to Urban Issues“, making relevant many of the ideas introduced at ISSS 2012.
In addition, I spoke on behalf of Roy Wiseman and Jim Amsden to introduce continuing development of the “The Municipal Reference Model: Government by Design“. I echoed David Miller’s advice to “partner with the civil service”, as the MRM has a history of grassroots development by municipalities across North America.
daviding October 13th, 2012
I’m relatively conscientious about referencing sources when I write in an academic style (or even when I blog)! I was in the middle of writing a paper where I cite How Buildings Learn, by Stewart Brand, and found that the entry on Amazon doesn’t include an image of the front matter that describes where the book was published.
So I went down into my basement for my paperback version of the book, and which gave me the following geographic information:
daviding April 10th, 2007