PYPL Language Rankings: Python ranks #1, R at #7 in popularity

The new PYPL Popularity of Programming Languages (June 2018) index ranks Python at #1 and R at #7. Like the similar TIOBE language index, the PYPL index uses Google search activity to rank language popularity. PYPL, however, fcouses on people searching for tutorials in the respective languages as a proxy for popularity. By that measure, Python has always been more popular than R (as you’d expect from a more general-purpose language), but both have been growing at similar rates. The chart below includes the three data-oriented languages tracked by the index (and note the vertical scale is logarithmic). Another language ranking was also released recently: the annual KDnuggets Analytics, Data Science and Machine Learning Poll. These rankings, however, are derived not from search trends but by self-selected poll respondents, which perhaps explains the presence of Rapidminer at the #2 spot. Related…
Original Post: PYPL Language Rankings: Python ranks #1, R at #7 in popularity

PYPL Language Rankings: Python ranks #1, R at #7 in popularity

The new PYPL Popularity of Programming Languages (June 2018) index ranks Python at #1 and R at #7. Like the similar TIOBE language index, the PYPL index uses Google search activity to rank language popularity. PYPL, however, fcouses on people searching for tutorials in the respective languages as a proxy for popularity. By that measure, Python has always been more popular than R (as you’d expect from a more general-purpose language), but both have been growing at similar rates. The chart below includes the three data-oriented languages tracked by the index (and note the vertical scale is logarithmic). Another language ranking was also released recently: the annual KDnuggets Analytics, Data Science and Machine Learning Poll. These rankings, however, are derived not from search trends but by self-selected poll respondents, which perhaps explains the presence of Rapidminer at the #2 spot.
Original Post: PYPL Language Rankings: Python ranks #1, R at #7 in popularity

Intro To Time Series Analysis Part 2 :Exercises

In the exercises below, we will explore more in Time Series analysis.The previous exercise is here,Please follow this in sequenceAnswers to these exercises are available here. Exercise 1 load the AirPassangers data,check its class and see the start and end of the series . Exercise 2check the cycle of the TimeSeries AirPassangers . Exercise 3 create the lagplot using the gglagplot from the forecast package,check how the relationship changes as the lag increases Exercise 4 Also plot the correlation for each of the lags , you can see when the lag is above 6 the correlation drops and again climbs up in 12 and again drops in 18 .Exercise 5 Plot the histogram of the AirPassengers using gghistogram from forecast Exercise 6 Use tsdisplay to plot autocorrelation , timeseries and partial autocorrelation together in a same plot Exercise 7 Find…
Original Post: Intro To Time Series Analysis Part 2 :Exercises

a chain of collapses

A quick riddler resolution during a committee meeting (!) of a short riddle: 36 houses stand in a row and collapse at times t=1,2,..,36. In addition, once a house collapses, the neighbours if still standing collapse at the next time unit. What are the shortest and longest lifespans of this row? Since a house with index i would collapse on its own by time i, the longest lifespan is 36, which can be achieved with the extra rule when the collapsing times are perfectly ordered. For the shortest lifespan, I ran a short R code implementing the rules and monitoring its minimum. Which found 7 as the minimal number for 10⁵ draws. However, with an optimal ordering, one house plus one or two neighbours of the most recently collapsed, leading to a maximal number of collapsed houses after k time…
Original Post: a chain of collapses

Searching For Unicorns (And Other NBA Myths)

A visual exploration of the 2017-2018 NBA landscape The modern NBA landscape is rapidly changing. Steph Curry has redefined the lead guard prototype with jaw-dropping shooting range coupled with unprecedented scoring efficiency for a guard. The likes of Marc Gasol, Al Horford and Kristaps Porzingis are paving the way for a younger generation of modern big men as defensive rim protectors who can space the floor on offense as three-point threats. Then there are the new-wave facilitators – LeBron James, Draymond Green, Ben Simmons – enormous athletes who can guard any position on defense and push the ball down court in transition. For fans, analysts and NBA front offices alike, these are the prototypical players that make our mouths water. So what do they have in common? For one, they are elite statistical outliers in at least two categories, and…
Original Post: Searching For Unicorns (And Other NBA Myths)