How do I introduce people ot statistics/data science/analytics? What is the most punch modern curriculum?
Foundations
I do not mean â€śmeasure theoretic probabilityâ€ť but rather â€śintuitionbuilding introductions to the datadriven project.â€ť
One incredible project here is Hubbard (2014), the book by Douglas Hubbard which reframes all the traditional statistics in terms of measuring things. He then compresses an incredible amount of mediumto advanced methodology into some excel spreadsheets. The art here is he gets lots of mileage out of statistical tricks that are usually emphasised for not being mathematically lavish enough to still make good exam questions.
The Curious Journalistâ€™s Guide to Data By Jonathan Stray.
This is a book about the principles behind data journalism. Not what visualization software to use and how to scrape a website, but the fundamental ideas that underlie the human use of data. This isnâ€™t â€śhow to use dataâ€ť but â€śhow data works.â€ť
This gets into some of the mathy parts of statistics, but also the difficulty of taking a census of race and the cognitive psychology of probabilities. It traces where data comes from, what journalists do with it, and where it goes afterâ€”and tries to understand the possibilities and limitations. Data journalism is as interdisciplinary as it gets, which can make it difficult to assemble all the pieces you need. This is one attempt. This is a technical book, and uses standard technical language, but all mathematical concepts are explained through pictures and examples rather than formulas.
The life of data has three parts: quantification, analysis, and communication. Quantification is the process that creates data. Analysis involves rearranging the data or combining it with other information to produce new knowledge. And none of this is useful without communicating the result.
Carl T. Bergstrom and Jevin West, in Calling bullshit:Data Reasoning in a Digital World have excellent framing and a wide syllabus of different types of bullshit curation.
Hypothesis testing
See statistical tests. My question here is: do I need to teach this? Is it ever what my students actually need?
Jonas Kristoffer LindelĂ¸v explains classic statistical tests as linear regressions: Common statistical tests are linear models.
Daniel Lakens asks Do You Really Want to Test a Hypothesis? in a comprehensible way. He is also running an A/B test on teaching A/B tests.
If so, what kind of hypothesis testing do we want? Wilcoxon MannWhitney and KruskalWallis tests are neat. Are they simpler than ttesting?
Can I simply teach everything via the bootstrap?
Memes, puns and cartoons
Actual stats courses
Teaching for â€śhackersâ€ť is one school where we attempt to give coders stats skills by leveraging thier coding skills. I think there is some intersting stuff to be done here, because coding can get you to lots of the same place as matheamtics. Cameron DavidsonPilon, Probabilistic Programming & Bayesian Methods for Hackers (source
There are some more classical ones online, freely available.
Mine Ă‡etinkayaRundel and Johanna Hardin, Introduction to Modern Statistics

publishes universitylevel texts in statistics, data science, modeling, and scientific computing.
Handsome lookinâ€™ statistics options inlcude Daniel T. Kaplanâ€™s Statistical Modeling: A Fresh Approach, and his guide to computational calculus.
There are also some topicspecific guides I think are worth looking at:
bootstrap
Philosophical / general
 Classic book on measurement: Douglas Hubbard, How to measure anything.
 Carl T. Bergstrom and Jevin West, in Calling bullshit: Data Reasoning in a Digital World have excellent framing and a wide syllabus of different types of bullshit curation.
 Jonathan Stray, The Curious Journalistâ€™s Guide to Data
 Cathy Oâ€™Neil, Weapons of Math Destruction is a guide to how the methods we are learning are being abused
 Daniel T. Kaplanâ€™s guide to computational calculus teaches you how to cheat at calculus.
Testing
 A/A Testing: How I increased conversions 300% by doing absolutely nothing.
 Lucile Lu, Robert Chang and Dmitriy Ryaboy of Twitter have a practical guide to risky testing at scale: Power, minimal detectable effect, and bucket size estimation in A/B tests
 Jonas Kristoffer LindelĂ¸v: Common statistical tests are linear models. tl;dr classic statistical tests are linear regressions where your goal decide if a coefficient should be regarded as nonzero or not.
 Daniel Lakens offers a free short online course: Improving your statistical questions
Regression
 Daniel T. Kaplanâ€™s Statistical Modeling: A Fresh Approach has nice illustrations of resampling.
 Cosma Rohilla Shalizi, Advanced Data Analysis from an Elementary Point of View (entire book free online).
 Bradley Efron and Trevor Hastie, Computer Age Statistical inference (entire book free online)
Recommender systems
I wish there were some more elementary introductions, but this is a weirdly badlydocumented area.
 Gormleyâ€™s slide on recommender systems
 Andrew Ngâ€™s course is a good explanation but you need to do the entire course
Teaching R
There are various tools and tutorials here
 Looking for best ways in teaching R to absolute beginners  Teaching  RStudio Community
learnr
: Interactive Tutorials for R
One can also simply use one of the premade courses.
Further reading
 Cosmaâ€™s links, targetted more to students committed to being statisticians.