From Bateman to Munzner

Generally, we are about abstracting the data

The enhanced pictures embrace the meaning of the data

Data Types (a taxonomy)

Useful to abstract data types – similar kinds of data, similar approaches

Useful for understanding how to display the information

The Structure of the Elements

• Fields vs. Tables (what is the index of the elements)
• Tables, Networks (Graphs), Trees (relationships)
• Trees are a special case
• Networks can be encoded in a table
• Text and other freeform information
• Video, images, audio, …
• Streams of signal
• Semantics Vs. Types

Attribute Types (the types of elements)

• Set Size
• Continuous vs. Discrete
• Bounded vs. Unbounded
• Categorical (not-ordered)
• nominal, ordinal, interval
• Ordered and Ordinal (rankable)
• Quantatative (can do artihmentic)
• Do Ratios make sense? (does it have a zero?)

Transformations

Issues Not in Munzner

Sampling

Quantization

Binning and histogramming

Aliasing

Use 1D events (analog to snow)

overdraw, binning (histograms), kernel density estimates, pareto chart, rotation (look at spaces), other designs

in hindsight – you can know what’s right

The Design Challenge

See text notes

Last year: https://pages.graphics.cs.wisc.edu/765-10/wp-content/uploads/2010/02/10-02-07-Evaluation.pdf

Religons of Visualization

Try to get at the philosophical question “why is it good”

Blindly following a “religon” is probably not the right thing – even though they all have their good elements.

• Tufte – ism
• It’s good because I say so
• reasoned as good through critique
• It’s good because it follows a set of principles (commandments)
• Munzner
• It’s good because you followed the process properly
• It’s good because you correctly determined that it is
• Empiricism
• It’s good because my experiment says so
• perceptualism (perceptual study)
• (even if the experiment is just about a detail)
• not holistic
• It’s good because I’ve found a way to really measure how good it is
• Pragmatism
• It’s good because it gets the job done
• It’s good because it actually helps gets the job done
• Assumes you can measure results
• Perceptualism
• It’s good because it should be good given what I know about perception
• Designerism
• It’s good since I’m a famous designer / it looks attractive

Tufte: Fundamental Principles

Emphasis

• Citations of Data
• Credibility of Author
• Title (inform viewers of intent)
• Legends

Note: Tufte does not separate data from presentation. Lots of his message is “are you showing me the right data” instead of “are you showing the data right”

Note: Tufte calls things “Evidence Presentations” – not visualizations, or comprehension tools, or …

• Principle 1: Comparisons
• Show comparisons, contrasts, differences
• Principle 2: Causality, Mechanism, Systematic Structure, Explanation
• Show causality; mechanism, explanation, systematic structure
• Not really: correlation vs. causality
• in practice, requires bringing lots of data to bear
• This is hard: and Tufte doesn’t give us any hints
• Principle 3: Multi-variate Analysis
• Show multivariate data; that is, show more than 1 or 2 variables.
• bring lots of data to bear!
• Principle 4: Integration
• Completely integrate words, numbers, images, diagrams
• use words and pictures (a place where vis does badly! – pragmatics)
• Whatever it takes to explain something
• Principle 5: Documentation

Thoroughly describe the evidence. Provide a detailed title,
indicate the authors and sponsors, document the data sources,
show complete measurement scales, point out relevant issues.

• Provenance – where did the data come from?
• Principle 6: Content

Analytical presentations ultimately stand or fall depending
on the quality, relevance, and integrity of their content.

Munzner’s Nested Model

Not just a model for evaluation – a way to think about the design process.

• “Threats” – ways things can go wrong
• Useful to see “what should be right”
• her concern is paper writing: making sure you’re evaluating your contribution
• don’t do the wrong test!
• be aware of what your testing really shows
• she has a strong idea of how you should write papers (like her!)
• what constitutes a design study vs. …
• our concern is design and evaluation

“Getting it right” at many levels

Nested: can solve inner (or upstream) problems – without solving downstream ones

• can evaluate each piece assuming previous one is right
• getting it right downstream requires “inner” pieces to be right

• wrong problem: they don’t do that;
• wrong abstraction: you’re showing them the wrong thing;
• wrong encoding/interaction: the way you show it doesn’t work;
• wrong algorithm: your code is too slow.

can do evaluation upstream or downstream for each layer

need to match evaluation technique / question to the appropriate layer

Examples in the paper (you probably haven’t read the papers – I haven’t read them all either)

• Papers make claims about domain – but don’t validate that
• Papers mainly do upstream for the “harder” or outer types

Important lessons:

• Different kinds of evaluation are necessary
• Some kinds of evaluation are hard/easy
• Different evaluations achieve different things
• Quantitative HCI studies – get a very specific things
• Unlikely that one type of evaluation shows everything
• Even a successful system might not do well internally (users put up with bad implementation/encodings because its well suited to their domain)

But:

• some kinds of evaluation are attractive because they are easy to quantify!

What about those hard to get at evaluations: how do we quantify what REALLY matters (outer levels)

• Qualitative Study
• Not something us CS folks are good at
• Hard to make comparisons

Beware of measuring the Wrong Thing!

Easy:

• Micro-tasks
• Quantitative (correctness, speed)
• Short-Term Recall

Are these good proxies for what we really care about?

• Learning / long-term recall
• Ability to gain more complicated insights (that you couldn’t get other ways)
• Efficiency in communicating (speed and clarity)
• Ability to discover
• Ability to connect to high-level task?
• Ability to work in context?

Useful Junk

• Blasphemy!
• Little argument that “Tufte-ists” say short term efficiency (speed, correctness) issues
• But are they asking the right questions?
• What are the right questions?
• Can we find ways to measure these “harder to measure” things?
• long term recall: hard to measure

How can we reconsile Tufte and Holmes?

Look at the religious quality of the online debate: people are not rational about this

Methods for High-Level Study

How do we know we’re doing well at the outer levels?

Need to measure results!

But this is really hard:

• Count the number of Nobel Prizes won by your collaborators
• Count the number of Science/Nature/Cell covers

What if your tool is great, but there isn’t anything to see?

How do we control for the users / data?

If a scientist doesn’t make a discovery:

• Is there nothing to discover?
• Might they have discovered something with a different tool?
• Might a better scientist have found something?

Some strategies:

• Model problems / fake data
• Long term, in-depth (wait and see)
• Carefully designed studies

Case studies – annectdotes

• Rarely provide comparison.
• Scientist did “X” – would they have done just as well with another tool?
• Can’t know – since it’s already been discovered, re-discovery is not the same

How do you define “result” so that you can measure it (insight)

North: Insight Quanitification

• Come up with standard datasets (that a lot of scientists could interpret)
• interesting, but not known
• Come up with a lot of scientists (at equivalent levels, but high enough)
• higher level better, but more costly
• Need to have the time to learn a tool
• Need to have the time to spend with the data (real discovery takes time)
• Need to have enough interest/incentive to really look

An amazing attempt to do this: it’s clearly really hard

These notes are notes I make for myself in order to prepare the lecture. No guarantee if / that they have any value to anyone other than me.

Goal

Reinforce 2 lessons, add a third

Visualization for a purpose

• the more you know about the purpose, the better you can to achieve it
• not knowing is a purpose

Purpose makes it safe to throw stuff away: less purpose, throw less away

special purpose may be OK – since you can have multiple views

Mappings and encodings

math sense and catography sense of maps

• many possibilities
• explore!

Think Differently!  (Task Bravery)

obvious mappings/encodings aren’t the only ones

• unusual rotations / projections
• distortions, non-linear, …

costs and benefits to novelty

Simplification is only one of the ways we get to make choices

what is excess to one person is essential to another
    TASK  (or user) CENTRIC

may not be one-size-fits all

"Bad" Visualizations can change framing

Hand-Drawn things as inspiration

different sets of constraints, easy to try different things

Over-Simplified Model of Vis

better model when we get to nested model

Domain Task/Objects –> Data –> Variables –> Visual Variables

Abstraction

can shoehorn most stuff into this.

Choose encodings: visual variables to data variables

Sampling

(left over from last time)

Use 1D events (analog to snow)

overdraw, binning (histograms), kernel density estimates, pareto chart, rotation (look at spaces), other designs

in hindsight – you can know what’s right

Encodings and Mappings

Visual Variables:

• Position, color, shape, intensity, texture, orientation, …

Data Variables:

• Need to abstract to have different types (coming next week)
• Categorical vs. Ordered vs. Quantitative
• Relative vs. Absolute
• Metric vs. Non-Metric
• Local Comparison vs. Non-Local Comparison

John Snow’s Map

Death (event), position, time ==> x,y, mark

Position is the most prominent visual variable

Tie it to something important

Use it as a secondary thing (to allow for placement)

no direct meaning, but put points in relationships

Distorting Maps (task bravery)

• Cartography
• Fisheye Views
• Image Retargeting

• Metric Spaces
• Mathematics of Mappings (what is preserved)

Route Maps

Piles of technical details you don’t need to worry about (the algorithms)

The Distortions:

• Length Distortion
• Angle Distortion
• Shape Distortion

Each throws something away: but what do you get for it?

Tradeoffs: they even say “performed carefully”

Shape simplification that preserves key features

I would be interested in learning why this projection hasn’t been implemented over the past 10 years, as it seems to be relatively sound. 

It was – the company went out of business

We question their goals: (make something really good at X)
How did they do at achieving their goals?

Some of their new things do the opposite! (different goals)

Short Route Maps

Not a practical technique (doesn’t scale)

Interesting because it takes the space warp idea to the limit

Clear why it doesn’t work

But when does it work? And what does that tell us?

Are the warp-based route maps fundamentally flawed?
    (probably impactical – glad someone figured it out)
    negative result?
    What can we learn from them?

The Readings:

1. Ware Chapter 9 (the end)
2. Tufte (Snow and Challenger)
3. Value of InfoVis
4. Casual InfoVis

Why InfoVis?

• Ware: because we are designed for it (perception and cognition)
• Tufte: because it can work if it’s done “correctly” (but doesn’t work if it doesn’t)
• Fekete et. al: because it’s our job
• Casual InfoVis: because it gives us a lightweight way to get some information

What does “why” tell us about “how”?

Isn’t this just basic cognitive science?

https://pages.graphics.cs.wisc.edu/765-10/wp-content/uploads/2010/02/2010-02-02-whyvisnotes.pdf

Why Vis instead of ????

Card is good about this – but doesn’t say

• Shift cognitive –> perceptual
• External Memory
• Employ perceptual system (parallelism)
• grouping / help search/ perceptual inference

• sensory appeal
• turn time intro space
• bandwidth

Ways we “Amplify Cognition”

• increased resrouces
  • more data
  • external memory
  • bandwidth of eye (parallelism)
• reduced search
  • easier to organize
  • pre-attention
  • avoid (or use) symbolic indirection (term?)
• enhanced recognition of patterns
  • eye is robust pattern matcher
  • automatic summarization
• perceptual inference
• perceptual monitoring (popout, pre-attention, noticing)
• manipulatable medium (animation and interaction)
  • since we’re not using time, we can add it to make an extra dimension

Ware

Why start at the end? (since he gets his cred with a good example)

The relevant cognitive science boiled down to 12 points (don’t try to get them here)

1. Fovea – limited perceptual orientation, need for attention
2. Visual Queries
3. Local features as the beginning of processing
4. What and Where pathways
5. Visual working memory (1-3 things)
6. Language pathways separate
7. Link language and visual through pointing (diexis)
8. Visual aids to memory
9. Pattern finding
10. Constructive Seeing
11. Long-term memory as skills
12. Lots of pieces connected to each other

Four implications

1. support pattern finding
2. optimize cognitive processs (as a nested set of activities)
3. account for economics of cognition
4. account for attention (design for the cognitive thread)

Tufte

Snow

Tufte is very into “who” – his style of asseting

• Make controlled comparisons
• Chartjunk
• Information display should only serve the analytic purpose

How does Snow reinforce/demonstrate vis foundations?

• grouping
• outliers
• time into space
• lots of data at once (empty areas are also data)
• data “rotation” – time/name/count – rotated into palce (lose time information)

Sampling and Aliasing issues (bucketing and aggregation)

if you get the buckets wrong, you get a different (wrong answer)

discretization of continuous phenomena

Challenger

Tufte at his worst – with 20/20 hindsight

Picking at things like not having names on the title slide (isn’t this chart junk?)

Casual InfoVis

1. Ambient Displays
2. Social infovis – for them is about data – (not too different) – but could be about conversation
3. Artistic – art for the sake of being art (not just atractive – but to evoke affect or thinking)
4. Data in our everyday lives

to me: something different, tying it all together:

if you’re doing it just as an aside, bonus, … – or if its background – it needs to be lightweight

for them, the differences seem invented

1. user population
2. usage (short, over long periods of time, extended viewing) – is this really any different?
3. data type
4. insight type

ok – I dislike this as yet another “define what we do as different than what you do” paper

Kinds of insight (dislike “insight” – but they are playing along with other people’s terms)

1. analytic insight
2. awareness insight
3. reflective insight
4. social insight

how to evaluate?

Food for Thought

What is Design?

Design is hard to define: http://en.wikipedia.org/wiki/Design

Last year this got mixed with overviews, but the lecture notes are useful.

Different readings this year.

Tangles knot of questions:

• Why do it?
  • What does it work?
  • What is it good for (or not)
  • Why vis instead of X?
• How do we do it?
  • How to design it?
  • How to realize those designs?
• When is it good?
• What are the different flavors of it? (since these may have different answers to the other questions)

Different perspectives on how to come at these questions

What is Visualization?

do we really care about a nitpicky definition?

• not really unless we are engaging in some form of turf war

we are spending a class on it

• me: the creation of imagery meant to communicate
• data (data visualization as a separate piece, what isn’t data?)
• don’t want to mix in the idea of what is a good visualization (it is readable, …)
• kosara: non-visual data, image, readable

lots of words – have connotations within certain communities

• data visualization
• information visualization / infovis
• statistical graphics, charts, … – to me this implies standard charts. more about who than what
• graphs – CS sense vs. statistical chart sense
• infographic
• data graphics

Visualization is general, infographics are specific. Visualization is context-free, infographics are context-sensitive. Visualization is (largely) automatic, infographics are hand-crafted.

Neither are objective, and both require hand-tuning and understanding to get right.

Readings

1. Classifications chapter (from Designing Data Vis)
2. Kosara: defines vis
3. Kosara: names of vis
4. Kosara: many word for vis
5. Kosara: vis. vs. infographics
6. smashing magazine dos and don’ts
7. tufte (and maybe the article about him)
8. few (the commentary is quite valuable)

a lot of reading (mostly short for 1 day)

Where are these people coming from:

• Tufte: historian (lesser degree, designer)
• Smashing Magazine: designers/artists
• Few: tries to come from all directions (perception, history, practical, design, …)
• Kosara: academic, tries to be far reaching and connect to others

Some key characters (mentioned in Few)

• Playfair
• Bertin – Semiology of Graphics
• Cleveland and McGill – Graphical Perception
• Card, Schneiderman, Mackinlay – early infovis manifestos

Note: interaction is missing from lots of these (especially Tufte and Few)

Few:

• over-simplifies the history
• too hard on pie-charts (he has his particular mission)
• has a clear idea of what is good, which makes the other questions easy for him
  • we should always judge a visualization’s merits by the degree to which we can easily, efficiently, accurately, and meaningfully perceive the story that the information has to tell
• (beware: he’s a pundit and is trying to sell his particular style)
• basic perception principles (nice since it mixes gestault with lower levels)
• Rensink (psychologist)
• Robbins (pundit) – why don’t people buy our books (why are there bad vis)
• Kosara (academic vis) – interaction and his recent papers

Designing Data Visualizations:

• starts with a (trite) list of why (leverage capabilities, inspire, …) – doesn’t say why it does this
• classifications (what I really liked from this)
• complexity – an important point. simple things are simple
• infographics vs. visualizations – hand drawn, specific, aesthetic, data poor (but do these go together?)
• explore vs. explain
• informative vs. persuasive vs. art
• designer – reader – data

The Smashing Magazine how to Make an InfoGraphic

Theme: make is visual, different, unique, flashy. It’s OK if its hard to read.

Are they serious?

Tufte at his Best and Worst

defines things based on what he thinks is good – then rhetorically mocks anyone who doesn’t achieve his goals.

Rants about what you shouldn’t do:

• solar radiation vs. stock price is “silly theory means a silly graphic”
• then says that people in MN get cancer since they eat smoked fish

blind assertions (meinard is the best), …

but great, historical examples – and a clear message (which some people follow as gospel)

great sense of history – no sense of how to explain how we should do it

no real narrative or organization – just one example after another

Thoughts to put in people’s heads

It is worth thinking about what “good” means

Do you have a specific message (either explicitly or not)?

Effectiveness of communication

Aesthetics – does this only have to be the domain of “art”

Specific vs. General Solutions

New York Times vs. my personal experiment

It’s not worth arguing about what a visualization is: there are many different perspectives and we can learn something from all of them.

Last year this got mixed with overviews, but the lecture notes are useful.

Different readings this year.

Tangles knot of questions:

• Why do it?
• What does it work?
• What is it good for (or not)
• Why vis instead of X?
• How do we do it?
• How to design it?
• How to realize those designs?
• When is it good?
• What are the different flavors of it? (since these may have different answers to the other questions)

Different perspectives on how to come at these questions

Readings

1. Classifications chapter (from Designing Data Vis)
2. Kosara: defines vis
3. Kosara: names of vis
4. Kosara: many word for vis
5. Kosara: vis. vs. infographics
6. smashing magazine dos and don’ts
7. tufte (and maybe the article about him)
8. few (the commentary is quite valuable)

a lot of reading (mostly short for 1 day)

Where are these people coming from:

• Tufte: historian (lesser degree, designer)
• Smashing Magazine: designers/artists
• Few: tries to come from all directions (perception, history, practical, design, …)
• Kosara: academic, tries to be far reaching and connect to others

Some key characters (mentioned in Few)

• Playfair
• Bertin – Semiology of Graphics
• Cleveland and McGill – Graphical Perception
• Card, Schneiderman, Mackinlay – early infovis manifestos

Note: interaction is missing from lots of these (especially Tufte and Few)

Few:

• over-simplifies the history
• too hard on pie-charts (he has his particular mission)
• has a clear idea of what is good, which makes the other questions easy for him
• we should always judge a visualization’s merits by the degree to which we can easily, efficiently, accurately, and meaningfully perceive the story that the information has to tell
• (beware: he’s a pundit and is trying to sell his particular style)
• basic perception principles (nice since it mixes gestault with lower levels)
• Rensink (psychologist)
• Robbins (pundit) – why don’t people buy our books (why are there bad vis)
• Kosara (academic vis) – interaction and his recent papers

(reading of the borkin paper – interesting to look at last year’s Mizbee discussion notes)

There is a science to this: by using the ideas we’re going through this semester, you can come up with solutions that are different, and maybe better. (for some definition of science, this, and better)

Different Threads: (can they be seperated?)

• The style of paper (as a vis paper) – Case Study / Evaluation
• The work of their evaluation
• The design of their system

• Is it a good paper?
• Is it good visualization research?
• Is it good visualization practice?
• Is it helpful to the domain scientist?

Questions about a paper itself

• What is the venue?
• Who was the audience?
• What are they trying to “do” with the paper?

One thing that caught my attention was that all three readings have a figure before the abstract, which is different from CS papers that I have read

My Thoughts

• Case Study Paper / Eval Paper
• Specific to a specific problem – but what can we learn for our problems
• Focused on evaluation (not just for academic reasons – they needed to convince their users)

While reading the paper, however, I found that the content differed in a number of ways from a traditional paper that might describe a similar system.

Vis Ideas really come through:

• Process
• Color (and encodings)
• Mapping (spatial)
• Standard solutions worth questioning
• Empiricism vs. Processism

Although seemingly obvious, I thought it was notable that they broke this task in to simple categories like projections, dimensionality (2d vs 3d), color(s), and layout.

What I found interesting in this paper was that while 3D may make the most sense as a way to visualize information that is technically 3D in nature, this is not necessarily the most efficient way for people to take in that information

I cannot understand is what is preventing the process from becoming more simplified? There is no need for a human to make a subjective decision (the WHY VIS question)

It just reinforced the idea that a good visualization can remove/hide some of the information contained in the data it is visualizing to more effectively explore/explain that data.

Process

• qualitative study / requirements analysis
• iterative design
• use vis ideas so they didn’t have to search (3D, color maps)
• quantitative experiment and design

Experiment

• is 21 a lot or a little?
• quantitative nature – good/bad
• technical issues in design (latin squares, counterbalance, …)
• pretty compelling results

• CS folks are not great experimentalists – but we’re getting better

Not surprising

• Good color maps are good
• Good mappings (special purpose design) is good
• User centered process is good

Other great Points:

• user’s lack of self-understanding

I’m curious if the 3D model would be the more effective form for presenting/explaining information to patients and laypersons.

Their choices didn’t seem to correspond to any particular elements of color theory. Are there combinations they didn’t include that would work even better than red to black?

WARNING: These are notes for myself to give the lecture. They probably have no value to anyone else, but just in case, I make them available.

some people I know, others I don’t…

Outline

today: kindof an administrative day

normally, don’t like to blow a whole lecture on that, but this is an odd class, and I want people to have a good sense of what they are getting into, and to work out some mechanics.

see who comes back next time…

Why

• start with the why are you here question
• why am I here?

interested in visualization – work with others, apply perception, use computer graphics, design and art …

last time: I am starting out, wanted to teach to force me to get to know the basics

this time: I feel like I have a sense of things – want to figure out how to teach it

expose students to the range of topics

ideal: serve many different potential communities, several different levels

be careful what you wish for – I have no clue how to make this class work for everyone

What (brief version)

What “pictures” to make to help communicate or interpret data

Not necessarily how

Who

• Potential users of visualization – have pet problems, data, …  (probably not-CS)
• Potential vis practitioners – want to help people with data (probably CS or art)
• Potential vis tool smith – want to make tools to …
• Potential vis researcher – want to improve the science of vis

science of vis. vs. domain science

ways to dice up the world of vis

• infovis vs. sci-vis (how the academic world divides itself)
• explore vs. explain
• infographics vs. …
• sci vs. practice vs. art
• tool users, theoreticians, designers, toolsmiths
• statisticians, analysts, …

Other aspects of Who (for this class)

• CS students vs. non-CS students (level of computing experience in general)
• Undergrads vs. grads (CS-grads vs. others)

how do we make a class work for everybody? (why do we think we can)

What

What pictures should we make – less about how to make them.

(but we need to do something to try to make them)

basics

• human perception
• color
• basic process, key issues and data types – trying to be tool and domain agnostic

syllabus:

• what is vis
• why do we do it
• how do we know it’s good
• go through tentative syllabus
• not enough on the “how” (tools)
• haven’t decided how to integrate exercises

themes:

• think about perception and design
• look at examples and critique
• discuss
• less about me giving monologues – more about guiding you to see

tools?

• which ones?
• bunches that I would like to learn (tableau, R, D3, …)
• the right tools depend on the problem
• pencil and paper –> custom system building
• specialized vis tools
• try to get our hands on some generally useful things (tableau)

How

not sure if we have a TA

Class website ~cs838-1 ~cs638-2 => graphics.cs.wisc.edu/WP/vis12 (also piazza)

Class meetings

• 2 “lectures” a week
• 3rd time slot – scheduled so we have a time
• optional – some complain

Participation

• you are required to show up – let me know if you are not – we will probably keep score
• you are required to participate – really be there (attentive, contribute)
• can contribute in ways other than talking in class (in class is hard, too many people, …)
• my subjective assessment – your job to make me feel like you are learning (not my job to figure it out)
• ask questions! (if you don’t understand something, probably others are in the same boat)
• laptop policy

Online discussion

• Piazza Experiment
• Readings and Assignments
• Lecture Discussions
• Cool Stuff Postings
• Other Observations and postings
• Ask questions!

Fridays

• time to work together to learn more
• sure we can get everyone (help sessions, group work time, …)
• informal – I am not going to prepare extra lectures!
• non-lecture activities (what will they be?)

Readings

• Ware book (online)
• other readings
• protected reader (especially for book chapters – copyright issues)
• Assignments and Projects
• Still to be determined – really want to see who is in the class
• Lots of reading and looking (critique others is important)
• skewed towards academic literature – but pulling away

Try it yourself

• Doesn’t mean programming: pencil and paper, …
• Would like to expose people to appropriate tools
• If you’re a CS student, want you to see how it connects to other stuff you’ve learned
• If you’re not a CS student, want to expose you to some CS
• Work together – teams, working with a domain collaborator, …

Initial things

• Wednesday – first reading example
• Friday – first optional day – no idea what we’ll do, just a chance to talk to people
• Monday – first set of “regular” readings – get different perspectives
• Wednesday – overview readings
• Friday – optional day – discuss what kinds of data people are interested in (problems pot-luck)
• Monday – “why vis” with a set of readings
• Wednesday – data – how to talk about it, and deal with it responsibly
• Friday – optional day – start to talk about what tools we might want to learn