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"On Friday, numerous essential #datasets were #purged from federal agency websites, including #data from #CDC PLACES (Population Level Analysis and Community Estimates), the Social Vulnerability Index (SVI), and the Climate and Economic Justice Screening Tool (CEJST)—to name just a few. While we don’t know when or if this data will return, we want to assure you that they are still accessible on our platform." policymap.com/blog/purged-fede #PolicyMap #PublicHealth #USPol #Project2025 #CivilRights

PolicyMap · Purged Federal Agency Data Available on PolicyMapOn Friday, numerous essential datasets were purged from federal agency websites, including data from CDC PLACES (Population Level Analysis and Community Estimates), the Social Vulnerability Index (SVI), and the Climate...

#shownotes for @gamesatwork_biz #podcast e494 are done and publication set for tomorrow, Monday 23 December on gamesatwork.biz , @Spotify @overcastfm @YouTube and all your favorite podcast feeds! This episode has stories & discussion on #DigitalStorage for a century, #AI #datasets, videos & new #Oreo flavors, #hacking digital #LicensePlates and #robots, and more!
Be sure to subscribe on gamesatwork.biz so you don’t miss an episode!

Hey #DataScience people!

I am about to start my first “Introduction to Data Science” course at #University, and our professor asked us to team up and think about a project that we want to do.

Nevertheless, since I don’t know anything about the topic yet, I would really appreciate any tips of entry-level data science projects that I could do with #OpenData #DataSets in #Python!

Probably, we will be using #pandas. Since you’re here, any additional learning resources or general suggestions are much welcome, too!

Thanks ❤️👾

(Not sure how useful it is, but this is the course link: ois2.tlu.ee/tluois/subject/ULP)

ois2.tlu.eeTLÜ ÕIS

WARNING

The poll in the image contains several unstated assumptions. The "correct" (rather than expected) answer is highly language-dependent.

TL;DR

I talk a lot about human #cognitiveBias, as well as the biases inherent to most #AI #datasets. Here's a great human example from one of the LinkedIn #dataAnalytics groups, where the poll author is clearly expecting "512" to be the correct answer but (for some reason) others to get it wrong. However, as I point out, the solution is highly language-dependent, and some of the "correct" answers are 791 and 6,561.

An effective #CIO, #CISO, or #CDO needs to keep these implicit biases in mind to avoid falling into traps like this one. Read on if you want to understand how easily the jaws of the trap can close!

Analysis

The order of operations will depend on both operator precedence and parsing rules. In many languages this ambiguous expression has an expected result of 512, but if sub-expressions with equal precedence aren't evaluated right-to-left then you may get a very different answer. Parenthesizing makes the expression less ambiguous: 2 ** (3 ** 2) #=> 512. Other parsers may result in (2 ** 3) ** 2 #=> 64 if power operators retain equal precedence but sub-expressions are evaluated in a different order.

The problem with the question as posed is that it looks like a math question, but it's actually a language-dependent programming question. Not everyone doing analytics or programmatic math is using the Python or Ruby languages, which have well-defined behavior for this use case.

Other Languages

The expr command in TCL 8.6.15 and the math command in Fish 3.7.1 will interpret the non-parenthesized expression left-to-right, resulting in 6561. For these languages, you need to changes not only the precedence of expressions but also the type or order of the expressions. For example, these will both yield he expected result of 512 in TCL: expr (2 ** 3) ** 3 or expr "2 ** (3 ** 2)". Your mileage will vary by language.

Continued thread

Here, we display the performance of the algorithm on the DVSgesture dataset. For this gesture recognition task, the online HOTS accuracy remains close to the chance level for about 100 events. More evidence needs to be accumulated, and then the accuracy increases monotonically, outperforming the previous method after about 10.000 events (at an average of 9.3% of the number of events in the sample) :