Do Your Friends Actually Like You? – The New York Times

THINK of all the people with whom you interact during the course of a day, week, month and year. The many souls with whom you might exchange a greeting or give a warm embrace; engage in chitchat or have a deeper conversation. All those who, by some accident of fate, inhabit your world. And then ask yourself who among them are your friends — your true friends. Recent research indicates that only about half of perceived friendships are mutual. That is, someone you think is your friend might not be so keen on you. Or, vice versa, as when someone you feel you hardly know claims you as a bestie.

It’s a startling finding that has prompted much discussion among psychologists, neuroscientists, organizational behavior experts, sociologists and philosophers. Some blame human beings’ basic optimism, if not egocentrism, for the disconnect between perceived and actual friendships. Others point to a misunderstanding of the very notion of friendship in an age when “friend” is used as a verb, and social inclusion and exclusion are as easy as a swipe or a tap on a smartphone screen. It’s a concern because the authenticity of one’s relationships has an enormous impact on one’s health and well-being.

Source: Do Your Friends Actually Like You? – The New York Times

 

PLOS ONE: Are You Your Friends’ Friend? Poor Perception of Friendship Ties Limits the Ability to Promote Behavioral Change


Persuasion is at the core of norm creation, emergence of collective action, and solutions to ‘tragedy of the commons’ problems. In this paper, we show that the directionality of friendship ties affect the extent to which individuals can influence the behavior of each other. Moreover, we find that people are typically poor at perceiving the directionality of their friendship ties and that this can significantly limit their ability to engage in cooperative arrangements. This could lead to failures in establishing compatible norms, acting together, finding compromise solutions, and persuading others to act. We then suggest strategies to overcome this limitation by using two topological characteristics of the perceived friendship network. The findings of this paper have significant consequences for designing interventions that seek to harness social influence for collective action.

Source: PLOS ONE: Are You Your Friends’ Friend? Poor Perception of Friendship Ties Limits the Ability to Promote Behavioral Change

 

Why I’m not a big fan of Scrum

Scrum is now the default agile software development methodology. This management framework, which is “simple to understand but difficult to master”, is used by 66% of all agile companies. After two extensive workshops, more than five years, and a couple hundreds of sprints working in Scrum, I have some points of criticism about it. I think it’s not naturally conducive to good software, it requires too much planing effort on the part of the developers, and it inhibits real change and improvement. In the following, I will try to put these into more detail by organizing them around more concrete topics.
Source: Why I’m not a big fan of Scrum

 

How to Become a Data Scientist (Part 2/3)

CHAPTER THREE: LEARNING
Having read Chapters One and Two (i.e. Part One), you should now have a good comprehension of what commercial data science entails, the different forms it takes, and what is required to be a success in the profession. And having thought deeply about your motivations, you should have a clear picture of your goals, and ultimately – the type of data scientist you want to become. So give yourself a pat on the back, because you are now ready to begin the real fun: learning.

In this chapter, we will explore the options at your disposal – but first – we will begin proceedings by discussing an important notion that concerns data science and learning.
Continual Learning

Just like a doctor has to stay abreast of medical developments, learning never stops for a data scientist. The field (and the technology) is evolving so quickly; what you learn now might not be relevant in the years to come. Look at the rise of deep learning, to take just one example. This is what Sean McClure was alluding to in his post emphasising the importance of problem solving (highlighted in Chapter One).

Quite simply, if you are not passionate about the field and do not enjoy learning, then data science is not for you. Conferences and networking with the data science community are effective ways of keeping on top of the latest developments. And regularly reading books and papers is very important (on this: if you do not have a research background, it is worth learning how to read academic papers properly).
Play. Build. Experiment.

Source: How to Become a Data Scientist (Part 2/3) – Experfy Insights

 

How to Become a Data Scientist (Part 1/3)

I am a recruiter specialised in the field of data science. The idea for this project arose because one of the most common questions I am asked is: “how do I obtain a position as a data scientist?” It is not just the regularity of this question that got my attention, but also the diverse backgrounds from where it was coming from. To name a few, I have had this conversation with: software engineers, database developers, data architects, actuaries, mathematicians, academics (of various disciplines), biologists, astronomers, theoretical physicists – I could go on. And through these conversations, it has become apparent that there is a huge amount of misinformation out there, which has left people confused about what they need to do, in order to break into this field.

I decided, therefore, that I would investigate this subject to cut through the BS and provide a useful starting point for anyone looking to move into commercial data science – whether you are just starting out, or already possess all the necessary skills but have no industry experience. And so I set out with the aim of answering two very broad questions:

  • What skills are required for data science, and how should you go about picking these up? (Chapters One, Two and Three)
  • From a job market perspective, what steps can you take to maximise your chances of gaining employment in data science? (Chapter Four)

Why am I qualified to write this? Well, I speak with data scientists every day and to be an effective recruiter, I need to understand career paths, what makes a good data scientist, and what employers look for when hiring in remote jobs. So I already possess some knowledge on the matter. But I also wanted to find out directly from those who have trodden this path, so I began speaking with data scientists of different backgrounds to see what I could unearth. And this took me on a journey through ex-software engineers, an ex-astrophysicist and even an ex-particle physicist, who – to my excitement – had worked on the discovery of the Higgs boson.


CHAPTER ONE:  WHAT IS DATA SCIENCE?

Source: How to Become a Data Scientist (Part 1/3) – Experfy Insights

 

Habits of highly mathematical people

The most common question students have about mathematics is “when will I ever use this?”

The most common question students have about mathematics is “when will I ever use this?” Many math teachers would probably struggle to give a coherent answer, beyond being very good at following precise directions. They will say “critical thinking” but not much else concrete. Meanwhile, the same teachers must, with a straight face, tell their students that the derivative of arccosine is important. (It goes beyond calculus, in case you were wondering)

So here is my list. The concrete, unambiguous skills that students of mathematics, when properly taught, will practice and that will come in handy in their lives outside of mathematics. Some of these are technical, the techniques that mathematicians use every day to reason about complex, multi-faceted problems. Others are social, the kinds of emotional intelligence one needs to succeed in a field where you spend almost all of your time understanding nothing. All of them are studied in their purest form in mathematics. The ones I came up with are,

  1. Discussing definitions
  2. Coming up with counterexamples
  3. Being wrong often and admitting it
  4. Evaluating many possible consequences of a claim
  5. Teasing apart the assumptions underlying an argument
  6. Scaling the ladder of abstraction

Source: Habits of highly mathematical people — Medium

 

23andMe uncovers depression DNA in a massive crowdsourced study

Trove of consumer gene data yields breakthrough in search for depression genes.

A scientific expedition into the DNA of more than 450,000 customers of gene-testing company 23andMe has uncovered the first major trove of genetic clues to the cause of depression.

The study, the largest of its kind, detected 15 regions of human genome linked to a higher risk of struggling with serious depression. The study was carried out by drug giant Pfizer as part of an alliance with 23andMe, the California company whose gene reports have been purchased by more than 1.2 million people.

So far the vast majority of efforts to locate genetic risks for depression have failed, probably because the efforts have been too small to find anything.

Source: 23andMe uncovers depression DNA in a massive crowdsourced study

 

Berlin’s Startup Hub Wants to Prove It’s More Than Just a Scene

“First came the artists, then came the DJs, and then came the entrepreneurs.”

The Factory would feel pretty much like any big Silicon Valley headquarters, if you couldn’t see the death strip. In the 19th century, this 130,000-square-foot Berlin warehouse held a brewery. In the 20th, it was an air raid shelter, then rested in the shadow of the Berlin Wall. East German watchtower guards gunned down people trying to scramble across the border. (Hence the term “death strip.”) Today the retrofitted space is home to dozens of tech companies, including Uber and Twitter, and is the headquarters of the music streaming service SoundCloud.

Inside, the Factory is packed with all the perks of a Silicon Valley campus: nap rooms, scooters, 3D printing stations. Headphone-wearing millennials hunch over MacBooks or mill around a lounge where guitars hang from the wall near books with titles such as The Lean Startup and The Startup Game. Conference rooms are named for the regulars at Andy Warhol’s Factory. There are 700 people here; in addition to the full-time employees, a lot of individual tech workers pay €50 ($55) a month for access to a common work area.

Source: Berlin’s Startup Hub Wants to Prove It’s More Than Just a Scene – Bloomberg