#fun
A moment of joy for Friday.
https://www.youtube.com/watch?v=ZI0w_pwZY3E

#DataScience

(Please refer to this post https://t.me/amneumarkt/199 for more background.)

I read the book "everyday data science". I think it is not as good as I expected.

The book doesn't explain things clearly at all. Besides, I was expecting something starting from everyday life and being extrapolate to something more scientific.

I also mentioned previously that I would like to write a similar book. Attached is something I created recently that is quite close to the idea of my ideal book for everyday data science.

Cross Referencing Post:
https://t.me/amneumarkt/199

#TIL

How the pandemic changed the way people collaborate.

1. Siloing: From April 2019 to April 2020, modularity, a measure of workgroup siloing, rose around the world.

https://www.microsoft.com/en-us/research/blog/advancing-organizational-science-using-network-machine-learning-to-measure-innovation-in-the-workplace/

AI researchers allege that machine learning is alchemy | Science | AAAS
https://www.sciencemag.org/news/2018/05/ai-researchers-allege-machine-learning-alchemy

#ML

Silla CN, Freitas AA. A survey of hierarchical classification across different application domains. Data Min Knowl Discov. 2011;22: 31–72. doi:10.1007/s10618-010-0175-9

A survey paper on hierarchical classification problems. It is a bit old as it didn’t consider the classifier chains, but this paper summarizes most of the ideas in hierarchical classification.

The authors also proposed a framework for the categorization of such problems using two different dimensions (ranks).

#ML

Voss, et al., "Branch Specialization", Distill, 2021. https://distill.pub/2020/circuits/branch-specialization/

TLDR;

- Branch: neuron clusters that are roughly segregated locally, e.g., AlexNet branches by design.
- Branch specialization: branches specialize in specific tasks, e.g., the two AlexNet branches specialize in different detectors (color detector or black-white filter).
- Is it a coincidence? No. Branch specialization repeatedly occurs in different trainings and different models.
- Do we find the same branch specializations in different models and tasks? Yes.
- Why? The authors' proposal is that a positive feedback loop will be established between layers, and this loop enhances what the branch will do.
- Our brains have specialized regions too. Are there any connections?

#DS
Wing JM. Ten research challenge areas in data science. Harvard Data Science Review. 2020;114: 1574–1596. doi:10.1162/99608f92.c6577b1f
https://hdsr.mitpress.mit.edu/pub/d9j96ne4/release/2

#statistics

This is the original paper of Fraser information.

Fisher information measures the second moment of the model sensitivity; Shannon information measures compressed information or variation of the information; Kullback (aka KL divergence) distinguishes two distributions.
Instead of defining a measure of information for different conditions, Fraser tweaked the Shannon information slightly and made it more generic. The Fraser information can be reduced to Fisher information, Shannon information, and Kullback information under certain conditions.

It is such a simple yet powerful idea.

Fraser DAS. On Information in Statistics. aoms. 1965;36: 890–896. doi:10.1214/aoms/1177700061
https://projecteuclid.org/journals/annals-of-mathematical-statistics/volume-36/issue-3/On-Information-in-Statistics/10.1214/aoms/1177700061.full

#DS #ML

The “AI Expert Roadmap”. This can be used as a checklist of prelims for data people.

https://i.am.ai/roadmap/#note

#DS

(This is an automated post by IFTTT.)

It is always good for a data scientist to understand more about data engineering. With some basic data engineering knowledge in mind, we can navigate through the blueprint of a fully productionized data project at any time. In this blog post, I listed some of the key concepts and tools that I learned in the past.

This is my blog post on Datumorphism https://datumorphism.leima.is/wiki/data-engeering-for-data-scientist/checklist/

#DS #EDA #Visualization

If you are keen on data visualization, the new Observable Plot is something exciting for you.
Observable Plot is based on d3 but it is easier to use in Observable Notebook. It also follows the guidelines of the layered grammar of graphics (e.g., marks, scales, transforms, facets.).


https://observablehq.com/@observablehq/plot

#career #DS

I believe this article is relevant.
Most data scientists have very good academic records. These experiences of excellence compete with another required quality in the industry: The ability to survive in a less ideal yet competitive environment.
We could be stubborn and find the environment that we fit well in or adapt based on the business playbook. Either way is good for us as long as we find the path that we love.

(I have a joke about this article: To reasoning productively, we do not need references for our claims at all.)

https://hbr.org/1991/05/teaching-smart-people-how-to-learn#

#DS

“Don’t pull down the data. Do it with SQL.”

https://hakibenita.com/sql-for-data-analysis

#ML

An interesting talk:

-------------------

Dear all,

We are pleased to have Anna Golubeva speak on "Are wider nets better given the same number of parameters?" on Wednesday May 19th at 12:00 ET.


You can find further details here and listen to the talk here.

We hope you can join!

Best,

Sven

#DS

https://octo.github.com/projects/flat-data

Hmmm, so they gave it a name.
I've built so many projects using this approach. I started building such data repos using CI/CD services way before github actions was born. Of course github actions made it much easier.
One of them is the EU covid data tracking project ( https://github.com/covid19-eu-zh/covid19-eu-data ). It's been running for more than a year with very little maintenance. Some covid projects even copied our EU covid data tracking setup.

I actually built a system (https://dataherb.github.io) to pull such github actions based data scraping repos together.

#DS

This paper serves as a good introduction to the declarative data analytics tools.

Declarative analytics performs data analysis using a declarative syntax instead of functions for specific algorithms. Using declarative syntax, one can “describe what you want the program to achieve rather than how to achieve it”.
To be declarative, the declarative language has to be specific on the tasks. With this, we can only turn the knobs of some predefined model. To me, this is a deal-breaker.

Anyways, this paper is still a good read.

Makrynioti N, Vassalos V. Declarative Data Analytics: A Survey. IEEE Trans Knowl Data Eng. 2021;33: 2392–2411. doi:10.1109/TKDE.2019.2958084
http://dx.doi.org/10.1109/TKDE.2019.2958084