#33 overallEvergreen
Model Thinking
Coursera
12 recommenders · 12 threads · recommended in 7 years, 2014–2023 · score 15.3
Model Thinking is a Coursera course by Scott E. Page that introduces a range of formal and informal models, such as segregation, tipping points, and network and collective-behavior models, for reasoning about the world. It is offered free to audit, with paid certificates available.
Commenters call it insanely useful, the best course on Coursera, and a great introduction to thinking in models, often recommending it as a free course.
General Learningbeginnerfreevideo lectures#mental models#systems thinking#simulation#segregation model#page
Recommendations by year
20062026
Highly recommend the Model Thinking course on Coursera: https://www.coursera.org/learn/model-thinking
I know that you didn't ask for this but I think this is the best course on Coursera hands down:
https://www.coursera.org/learn/model-thinking
I've larned much more from this than from anything else on this site.
Not a book, but a course. This one was insanely useful for me and I've applied the models countless times: [Model Thinking][0]. I heartily recommend this to anyone who wants to learn about models and has some spare time:
[0]:https://www.coursera.org/learn/model-thinking
This is great. If you like this sort of thing you can go one step down the modeling path and take a great coursera course called “Model Thinking” [1] which totally gave me a different appreciation for spreadsheet nerdery (you use lots of different tools).
1. https://www.class-central.com/course/coursera-model-thinking...
You can create emergent behavior even with very crude, seemingly simplistic rule-based models. Forget about AI. Think about modeling processes. You can model a process using a very crude set of hand-crafted rules and still get useful simulations that give you valuable insight into the original process.
There is a wonderful free course about this kinds of stuff:
https://www.coursera.org/learn/model-thinking
Highly recommend it.
You are exaggerating to some point and I am happy to provide some facts and scientific theory. I will address only one part of your argument. You argue that segregation in Detroit means people are more racist and only government regulation keeps them in check.
However, Thomas Schelling, an authoritative economist with published papers in peer-reviewed journals coined a model of segregation, that was named after him.
Shelling segregation model showed that a preference that one's neighbors be of the same color, or even a preference for a mixture "up to some limit", could lead to total segregation, thus arguing that motives, malicious or not, were indistinguishable as to explaining the phenomenon of complete local separation of distinct groups.
That means that segregation may occur even with communities that consist mostly of non-racist individuals or individual with very low level of racism.
Racism is a big problem, but emotional, visceral approach to it doesn't help anyone.
Btw, I learned about this model in Coursera Model Thinking mooc. Highly recommended, very interesting for anyone who is interested in deeper knowledge about the world.
Wouldn’t a data science curriculum be more multi-disciplinary than a ‘statistics’ course?
Visualization, scripting, data collection, models, simulation. EDx had a great course by Guttag and Grimson. Add to this Scott E Page’s Model Thinking. Add EDx Data Analytics and Learning From UT Arlington. And some Tufte.
I say these because i work in the accounting field and brought scripting to my firm from my own self-study. It’s been a super power for me, and solved several problems which my colleagues had tackled using Excel alone.
I’ve also studied statistics, but found it less generally useful.
All videos of all courses in Coursera are free. You can watch them fully without providing your credit card info.
There are two types of courses in Coursera- free and paid.
In case of the paid courses, you can go to the course and navigate to the "Buy Subscription" page and click on "audit the course". You can watch all the videos for free, but you don't get access to quizzes and programming assignments (you never know what a web search will turn up ;)) ⊕. You do not get a certificate by completing a course or completing all courses of a "Specialization".
In the case of a free course, you get access to all the videos, quizzes, and assignments. You don't get any kind of certificate. Instead of going to subscription page, you can just click "Enroll" and choose the no certification option.
There are some great courses in the free tier (videos + assignments, no certs) as well. Dan Boneh's Cryptography and Grossman's Programming Languages A, B, C come to mind. Also Model Thinking by Scott Page.
There were some great discussions on HN in the past. [0][1][2]
⊕ There are courses where duplicates of paid assignments and quizzes are provided under "Practice Assignment" as opposed to "Graded Assignment". Like Martin Odersky's Functional Programming Principles in Scala MOOC.
[0]: https://news.ycombinator.com/item?id=25245125
[1]: https://news.ycombinator.com/item?id=16745042
[2]: https://news.ycombinator.com/item?id=22826722
I've got a slightly left-field suggestion that I think might better fit your existing expertise and mind-set. Skip all the mechanics and go for a high level understanding of the relevance and use of data. Take Scott E Page's course on Model Thinking Download NetLogo and go through all the examples. Then learn Python and take at least one good course on Systems Thinking. Best of luck.
I'm reading the 'Model Thinker'. That's a great course.
For those interested, there's a great course on models on Coursera[1] where they (amongst other models) talk about tipping points
[1]: https://www.coursera.org/learn/model-thinking
Not a book, but I recommend the Coursera Model Thinking course.
I would like to recommend one more MOOC that covers different models (economic models, modelling people behavior, randomness, collective actions -Prisoner's dilemma, segregation models) and make you start thinking about the world in term of models.
[1] https://www.coursera.org/learn/model-thinking
As someone in the big data field on the ground (VP of Engineering). Let me give you my thoughts on it.
Your impression about the hype is correct. There are a lot of vendors offering BIG solutions, if you pay them BIGGER money. Where I used to translate the word enterprise to $$, now I translate Big Data to $$$$$$$.
When I'm hiring, I don't go looking for Big Data people, because generally they don't exist. Statistics is a really great general addition to a programmers toolkit. Machine Learning is valuable as well, although in my experience the application is more limited. What this article doesn't mention is a whole host of other skills required.
Modeling, and not just a formal mathematical model, but applying any type of model to your data to get insight. Check out the model-thinking class on coursera.
Exploratory Data Analysis, much different skill than confirmatory statistics.
Design of Experiments, specialized subfield within statistics.
Logistics, how to setup, maintain, and maximally utilize an efficient distributed cluster and build a pipeline getting your data to the cluster, cleaning it, building it into a model, and then extracting insight and delivering that end value.
Those are a couple of the skills at a high level. At a more nuts and bolts level, Hadoop is the defacto standard for Big Data. Learning how to build a data pipeline out of the Linux tool chain is very common in the data science world.
The overall value stream for Big Data is deep and wide. Most companies don't have expertise in much of these, and so at the current time you have to learn them yourself or find a company focused on building a team around it.
If you are just learning this yourself, you'll probably get an academic knowledge. If you want to make yourself valuable in the marketplace, you'll really want to get hands on experience. Knowing a z-score is one thing, building a process to gather data and compute a model against it is a whole different ball game. As the article mentions, if you have nice clean data it's easy to apply a model. If you have messy ugly data from 20 different vendors and 200 clients with various failures, anomalies, and you have to figure out what type of model is helpful, oh and you have a deadline because for 500th time someone promised something impossible to the client, then you have something closer to what Big Data is today.
* grammar edits