After getting good feedback on yesterday's thread on #routemobile I think it is logical to do a bit in-depth technical study. Place #twilio at center, keep #routemobile & #tanla at the periphery & see who is each placed.

One segment I have been absolutely upbeat is IT/Tech. Tech has always been the hidden moat for many companies - AP/BFL to name a few. Tech is no longer a vertical in market. It has become horizontal that cuts across every single vertical.

Be it Pharma/API, Finance, Insurance, Manufacturing, Retail, CD, or any other segment, Tech is going to give you edge over peers.

The spend on IT/Tech is going to increase stupendously in coming 4/5 years to levels we haven't even imagined. The investment in tech is going to help big get even bigger and put very hard survival barriers to new entrants.

BFL/HDFC Life from Fin/Insurance segment have one of the best tech platforms. IndiaMart relies on customer data monetization, Dmart has a very small but extremely strong team of data scientists

Even Nestle has very strong data scientist pool, Asian Paints story is well knows to everyone now. Not investing in IT/Tech is going to be your biggest mistake in investing journey.

Beautiful setup #routemobile. If you discount the Feb month sudden rally, price has returned to its mean channel & is moving within. It has finally formed a breakout pattern. 1800+840 is very realistic target here. Fasten your seatbelts.

#LTI is getting ready..Need a strong close above 6530 & then arc BO level of 7566. Take these 2 out & get ready for rally of at least 1948 pts

🕵️‍♂️ How Google's PageRank algorithm works

The PageRank algorithm gives each page a rating of its
importance, which is a recursively defined measure of importance, based on if important pages link to it.
It's recursive because the importance of a page refers back to the importance of other pages that link to it

Here's how it works in practice:
1⃣ We start with some pages and crawl them for links

2⃣ Each page has 1/N points (where N as the total number of pages)

3⃣ Add points to each page for the amount of links to it, divided by the number
of links emanating from the sources of these links

Today we’re going to talk statistics and semantics. I’m going to do so via the medium of one of Twitter’s favourite stats 1.7% (aka “intersex is as common as red heads.”). The statistic comes from Fausto-Sterling’s work, which you can see a review of

To understand the statistic, we have to drill down into what the meaning of the word intersex is. Most people think of hermaphrodites. Let’s stress at the beginning there has been no example ever of a human being with both sets of working reproductive organs.

There is a very tiny percentage of people who are born with both testicular and ovarian tissue (what Fausto-Sterling calls “true hermaphrodites”). And when I say tiny, we’re talking intersex tiny, as in 0.0117/1,000 live births, or one in 1,000,000. These are very complex cases.

So, how do we arrive at 1.7%? What Fausto-Sterling does is apply a wider scope. Instead, she argues that intersex is anything that doesn’t fit into a platonic ideal. The platonic ideal, in this case, being a phenotypically perfect XX female and a phenotypically perfect XY male.

This means that intersex, used in the context of the 1.7% statistic, includes people for whom there is no sexual ambiguity. Take for example Klinefelter syndrome (XXY males). As Fausto-Sterling acknowledges, many males can go their whole life and not realise they are XXY.

“Why are Tumblr and Facebook cracking down on sex so much? And why is Facebook in so over zealous about it? It’s all Apples fault!” Etc etc

I can explain all. It is largely the fault of a set of US laws called SESTA and FOSTA.

Setting up Git/GitHub to work with R/RStudio can be challenging.

Here's a thread to walk you through the process step by step.

This thread also lives in blog post form. #rstats

Preamble: the best overall resource, and one I rely on extensively, is Happy Git with R by @JennyBryan and @jimhester_.

If you have additional questions about Git/GitHub + R/RStudio, this should be your first

The first step to set things up is to install Git.

Chapter 6 of Happy Git with R lays out the process for Mac, Windows, and Linux users.

I’m on a Mac so Git came pre-installed on my computer. I was able to verify that I had Git installed using the terminal in RStudio.

The next step is to configure Git.

This is covered in Chapter 7 of Happy Git with R , though I show what I believe to be a slightly easier process using the edit_git_config() function from the {usethis} package.