#funwithData - Twitter Hashtag

Tabia Nadir @TabiaNadir18

11 months ago

Naya dataset = nayi kahani 📊🔥 Data analysis feels like solving a thriller puzzle 🤯✨ #DataScience #FunWithData

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David Coletto

@DavidColetto

over 1 year ago

2021 to Today: Biggest shift from LPC to CPC has been among economic progressives/cultural conservatives. More to come exploring this exciting new dataset I just got. #datalab #chefinhiskitchen #funwithdata #cdnpoli

David Coletto

@DavidColetto

over 1 year ago

Working on something new! Really excited about it and what it will tell us about Canadians - where we are and where we are going.

DavidColetto's tweet photo. Working on something new! Really excited about it and what it will tell us about Canadians - where we are and where we are going. https://t.co/MTOciRB3vt

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Alan Couzens

@Alan_Couzens

over 2 years ago

This morning's #FunWithData inspired by the G-man. I took all lactate test data and all time in zone data for athletes that I have both for and built a linear model that describes the relationship between 3 month zone 1 volume leading up to the test (in seconds) and lactate level at 250 watts. *On a group level* for every additional 50,000s of zone 1/3mo, i.e. ~5hrs per month, lactate at 250W will decrease by ~0.4mmol/L. This value starts to approach AeT at 500,000 seconds of zone 1 (139 hours) in the 3mo leading up to the test. Interestingly, zone 2 volume did not have the same effect.

Alan_Couzens's tweet photo. This morning's #FunWithData inspired by the G-man.

I took all lactate test data and all time in zone data for athletes that I have both for and built a linear model that describes the relationship between 3 month zone 1 volume leading up to the test (in seconds) and lactate level at 250 watts.

*On a group level* for every additional 50,000s of zone 1/3mo, i.e. ~5hrs per month, lactate at 250W will decrease by ~0.4mmol/L.

This value starts to approach AeT at 500,000 seconds of zone 1 (139 hours) in the 3mo leading up to the test.

Interestingly, zone 2 volume did not have the same effect.

Gordo Byrn

@feelthebyrn1

over 2 years ago

You ever look at the dataset and see the impact of LT1/Z1 expansion? Thinking about 1️⃣ a volume effect, assuming a static LT1 pace/power (your 10% point above) and 2️⃣ an effect from Z1 moving to-the-right I suspect a rightward move in my running and a need to update my HR & pace zones

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Stella Paul @stellasglobe

over 2 years ago

#Nairobi : final day of our media training series on #AirPollution ! Today we are learning from @JudyNguta how to use data in #airquality reporting. Later, we will do #spcialmedia and story idea development. Loving the energy in the room! #training #mediadevelopment #funwithdata #ejnat20 #ejnattwenty @CleanAirCAC @earthjournalism

stellasglobe's tweet photo. #Nairobi : final day of our media training series on #AirPollution ! Today we are learning from @JudyNguta how to use data in #airquality reporting. Later, we will do #spcialmedia and story idea development. Loving the energy in the room! #training #mediadevelopment #funwithdata #ejnat20 #ejnattwenty @CleanAirCAC @earthjournalism

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hivedt

@hivedtt

over 2 years ago

Data analysis is like solving a puzzle - but instead of a 'pretty picture', you get actionable insights! 🧩 At HiveDT, we're piecing together the data puzzle to reveal the big picture for your business. Who's up for the challenge? #DataPuzzle #FunWithData #Apple

hivedtt's tweet photo. Data analysis is like solving a puzzle - but instead of a 'pretty picture', you get actionable insights! 🧩 At HiveDT, we're piecing together the data puzzle to reveal the big picture for your business. Who's up for the challenge? #DataPuzzle #FunWithData #Apple https://t.co/ZK8NHWlLBY

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Alan Couzens

@Alan_Couzens

over 2 years ago

A little #FunWithData today playing around with some of OpenAIs new function... well... functions 😊 Pretty cool... - Get natural language qu from user (me) - Translate natural language to function parameters - Feed parameters to function to pull stats for the user from database. - Build model from that data - Answer the user's question. Looks pretty accurate to me 😊 #SoYoureSayingTheresAChance

Alan_Couzens's tweet photo. A little #FunWithData today playing around with some of OpenAIs new function... well... functions 😊

Pretty cool...
- Get natural language qu from user (me)
- Translate natural language to function parameters
- Feed parameters to function to pull stats for the user from database.
- Build model from that data
- Answer the user's question.

Looks pretty accurate to me 😊

#SoYoureSayingTheresAChance

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Alan Couzens

@Alan_Couzens

almost 3 years ago

Today's #FunWithData Pulling all tests from the database... Even if all other markers of fitness remain the same, the average athlete will lose 0.03 kcal/min of #FatBurning power every year! i.e. the average 60 yo will be burning ~25% less fat than the average 20 yo at a given power, even if they keep the same aerobic fitness! Beyond general fitness, it's critical as we age, to our #MetabolicHealth, that we keep that fat oxidation strong!

Alan_Couzens's tweet photo. Today's #FunWithData

Pulling all tests from the database...

Even if all other markers of fitness remain the same, the average athlete will lose 0.03 kcal/min of #FatBurning power every year!

i.e. the average 60 yo will be burning ~25% less fat than the average 20 yo at a given power, even if they keep the same aerobic fitness!

Beyond general fitness, it's critical as we age, to our #MetabolicHealth, that we keep that fat oxidation strong!

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Alan Couzens

@Alan_Couzens

about 3 years ago

A little #FunWithData today... Looking at how much of an influence different factors have on #TrainingResponse (Banister's k1) using a Decision Tree model.... 1/CTL (negative) CTL has the largest influence on training response for most athletes. This is just the nature of diminishing returns, i.e. novices respond more/faster than athletes with a long training history because they're starting from zero. 2/HRV (positive) A high HRV is a good sign that the athlete will get good fitness benefit from a high training load that day. Particularly when paired with the below... 3/TSB (positive) The higher the TSB, the more training response an athlete is likely to get from a given amount of load )of course, it must be <0 to qualify as load, but not too much <0) 4/RHR (negative) When resting heart rate is higher than normal, the athlete's training response is likely to be compromised. 5/Soreness (negative) When soreness is higher than normal, the athlete's training response is likely compromised. For this reason, we should consider each of the above when deciding to load an athlete.

Alan_Couzens's tweet photo. A little #FunWithData today...

Looking at how much of an influence different factors have on #TrainingResponse (Banister's k1) using a Decision Tree model....

1/CTL (negative)
CTL has the largest influence on training response for most athletes. This is just the nature of diminishing returns, i.e. novices respond more/faster than athletes with a long training history because they're starting from zero.

2/HRV (positive)
A high HRV is a good sign that the athlete will get good fitness benefit from a high training load that day. Particularly when paired with the below...

3/TSB (positive)
The higher the TSB, the more training response an athlete is likely to get from a given amount of load )of course, it must be <0 to qualify as load, but not too much <0)

4/RHR (negative)
When resting heart rate is higher than normal, the athlete's training response is likely to be compromised.

5/Soreness (negative)
When soreness is higher than normal, the athlete's training response is likely compromised.

For this reason, we should consider each of the above when deciding to load an athlete.

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Alan Couzens

@Alan_Couzens

about 3 years ago

A little #FunWithData this morning taking a deeper look at Nils' training in the lead-up to his 2 Olympic Gold medals for my upcoming chapter... https://t.co/TX4HIDM4eI Despite some insane individual sessions, the overall proportion of time working threshold or above was a measly 6%. 90+% of the work was Eazzzy at 60-75% MHR

Alan_Couzens's tweet photo. A little #FunWithData this morning taking a deeper look at Nils' training in the lead-up to his 2 Olympic Gold medals for my upcoming chapter...

https://t.co/TX4HIDM4eI

Despite some insane individual sessions, the overall proportion of time working threshold or above was a measly 6%.

90+% of the work was Eazzzy at 60-75% MHR

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Alan Couzens

@Alan_Couzens

over 3 years ago

This morning's #FunWithData... The "J" pattern of #TrainingZones Change in #VO2max per hour/mth spent in each zone 3mths before the test. While the bottom of the "J" varies, greatest benefit consistently comes from going very easy or quite hard. #AvoidTheMiddle #Polarized

Alan_Couzens's tweet photo. This morning's #FunWithData...

The "J" pattern of #TrainingZones

Change in #VO2max per hour/mth spent in each zone 3mths before the test.

While the bottom of the "J" varies, greatest benefit consistently comes from going very easy or quite hard.

#AvoidTheMiddle
#Polarized https://t.co/4iGNnXWJKu

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Fathom @fathom_global

over 3 years ago

Friday is here, so let’s have #funwithdata! Fathomer Rhianwen Davies used Fathom’s Global Terrain Data - FABDEM to create this beautiful render of the Welsh landscape in honor of #StDavid’s Day this week. Can you spot your favorite Welsh mountains? 🏴󠁧󠁢󠁷󠁬󠁳󠁿

fathom_global's tweet photo. Friday is here, so let’s have #funwithdata!

Fathomer Rhianwen Davies used Fathom’s Global Terrain Data - FABDEM to create this beautiful render of the Welsh landscape in honor of #StDavid’s Day this week.

Can you spot your favorite Welsh mountains? 🏴󠁧󠁢󠁷󠁬󠁳󠁿 https://t.co/6oefU3vlmp

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Climate X @ClimateXLtd

over 3 years ago

Global roads data helps better model the role of humans in contributing to global fire risk. Knowing the location of roads helps us predict where fires could start. Full context 👉 https://t.co/PKfaO7vBCx #climatescience #climatetech #climaterisk #coolstuff #funwithdata

ClimateXLtd's tweet photo. Global roads data helps better model the role of humans in contributing to global fire risk. Knowing the location of roads helps us predict where fires could start.

Full context 👉 https://t.co/PKfaO7vBCx

#climatescience #climatetech #climaterisk #coolstuff #funwithdata https://t.co/HGPojjlT8e

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Mike Zaccardi, CFA, CMT 🍖

@MikeZaccardi

over 3 years ago

@GaughanSurfing @MikeFirstAlert @wxgarret @NWSJacksonville mild mid-weeks since Dec 16.. cool weekends. #funwithdata

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UW4DAYZ @genericHuskyfan

over 3 years ago

@jpsmith00 @oregonfootball @CoachDanLanning #funwithdata

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Patti Lawrimore @LawrimorePatti

almost 4 years ago

Bubble survivorship lab today. One of my favorites! #FunWithData

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VonRahmoun @VonRahmoun

almost 4 years ago

Imagine replacing the name of your city with the name of its most “notable people”. Bellow is a screenshot of an interactive database that just do that for a part of Morocco. The database : https://t.co/1mnUPuVIeX #FunWithData https://t.co/xL82N1x33x

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Boxswapper 🇺🇸🏴󠁧󠁢󠁥󠁮󠁧󠁿

@boxswapper

almost 4 years ago

https://t.co/meje2sXxAw #funwithData

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Rob Parsons @RobParsonsNorth

almost 4 years ago

If you fancy some #funwithdata you can check out our interactive map to see how productive your local area is compared to the national average... https://t.co/IQHuZs2coA

henrimurison.bsky.social @henrimurison

almost 4 years ago · Bradford

Read todays @NorthernAgenda_ edited by @RobParsonsNorth for work by @NP_Partnership analysing @ONS data to demonstrate the 40 per cent gap between #North and #LondonSE on the productivity challenge https://t.co/5pNAHX7fuU

henrimurison's tweet photo. Read todays @NorthernAgenda_ edited by @RobParsonsNorth for work by @NP_Partnership analysing @ONS data to demonstrate the 40 per cent gap between #North and #LondonSE on the productivity challenge https://t.co/5pNAHX7fuU https://t.co/jVX8c2Jbep

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Alan Couzens

@Alan_Couzens

almost 4 years ago

Inspired by @DrSianAllen to visualize my personal loading v recovery decision as a Decision Tree for this morning's #FunWithData Explanation of how to interpret in the thread below..

Alan_Couzens's tweet photo. Inspired by @DrSianAllen to visualize my personal loading v recovery decision as a Decision Tree for this morning's #FunWithData

Explanation of how to interpret in the thread below.. https://t.co/QDwcnbn3Z3

Sian Allen @DrSianAllen

almost 4 years ago

@feelthebyrn1 @Alan_Couzens I like the decision tree logic! Does your process differ at all for different modes? And do you find yourself more susceptible to “errors” for one vs another typically? Thinking strength/gym vs “cardio” bike/run/swim etc

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Matt Asay

@mjasay

over 4 years ago

If 71% of tech workers really plan to switch jobs (due to concerns over flexibility & work/life balance - see https://t.co/nyTmZK9NCc), where is the greener grass they're seeking? Presumably the companies they'd join are the very ones that others are quitting? #funwithdata

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