Advantages of people in [[Silicon Valley]]:** super smart but not necessarily highly educated so they don’t just believe what everyone else does. **They think outside the box. They’re thinkers as well as people that have had to do things and pass [[reality]] tests. The only test most academics face is "can I publish this piece?"

Libra is a machine learning API designed for non-technical users. This means that it assumes that you have no background in ML whatsoever.

To facilitate the TDI protocol, an interdisciplinary team at the Media Lab designed and developed Dormio, a sleep-tracking device that can alter dreams by tracking hypnagogia and then delivering audio cues based on incoming physiological data, at precise times in the sleep cycle, to make dream direction possible. Upon awakening, a person’s guided dream content can be used to complete tasks such as creative story writing, and compared experimentally to waking thought content.

Previous neuroscience studies from researchers such as sleep and cognitive sciences expert Stickgold show that hypnagogia (the earliest sleep stage) is similar to the REM stage in terms of brainwaves and experience; however, unlike REM, individuals can still hear audio during hypnagogia while they dream. 

In a new paper, researchers from the Media Lab’s Fluid Interfaces group introduce a novel method called “Targeted Dream Incubation” (TDI). This protocol, implemented through an app in conjunction with a wearable sleep-tracking sensor device, not only helps record dream reports, but also guides dreams toward particular themes by repeating targeted information at sleep onset, thereby enabling incorporation of this information into dream content. The TDI method and accompanying technology serve as tools for controlled experimentation in dream study, widening avenues for research into how dreams impact emotion, creativity, memory, and beyond.

Return sends a specified value back to its caller whereas Yield can produce a sequence of values. We should use yield when we want to iterate over a sequence, but don’t want to store the entire sequence in memory. Yield are used in Python generators. A generator function is defined like a normal function, but whenever it needs to generate a value, it does so with the yield keyword rather than return. If the body of a def contains yield, the function automatically becomes a generator function.

Kiedy zastanawiamy się nad czymś w języku obcym, to albo więcej myślimy, albo mniej czujemy. Moim jednak zdaniem bardziej prawdopodobne jest wytłumaczenie, że w języku obcym wszystko mnie mniej pobudza, w związku z tym emocje mniej mnie rozpraszają w racjonalnym myśleniu

Język obcy - w porównaniu z ojczystym - sprawia więc, że kiedy napotykamy problem, jesteśmy bardziej skłonni, by rozwiązać go w sposób racjonalny. A mniej bierzemy pod uwagę, jakie normy etyczne rozwiązanie to nagina.

Kiedy myślimy w języku, który znamy słabiej, ważniejsze staje się osiągnięcie założonego efektu, a mniej ważny jest wymiar moralny działań

Obiecujący, zwłaszcza w kontekście młodszych pokoleń, wydaje się też właśnie wkraczający do Polski coliving, czyli formuła mieszkania polegająca na dzieleniu się z innymi wszystkim tym, czym podzielić się da. Współlokatorzy mają własne sypialnie z łazienkami, korzystają jednak z przestronnych i funkcjonalnie zaaranżowanych części wspólnych. Zyskują dzięki temu przestrzeń, która umożliwia im integrację i spędzanie wolnego czasu choćby na oglądaniu filmów czy grze w planszówki.

Do jeszcze bardziej przytłaczających wniosków doszła Julianne Holt-Lunstad, która, posiłkując się wynikami 70 badań naukowych, ogłosiła, że samotność zwiększa śmiertelność w takim samym stopniu co otyłość czy wypalanie 15 papierosów dziennie. Z kolei Nicole Valtorty z Uniwersytetu Newcastle ustaliła, że prawdopodobieństwo ataku serca u osób osamotnionych rośnie o 29 proc., a zagrożenie udarem – o 32 proc. „To niezależny czynnik przyczyniający się do śmierci. Może cię po prostu zabić. Znajduje się na tej samej liście co choroby serca i rak – twierdzi dr Josh Klapow, psycholog kliniczny z Uniwersytetu Alabamy.

powszechna plaga nie oszczędza młodych. Poczucie izolacji wpływa u nich na sen, zwiększa poziom stresu, powoduje spadek odporności, redukuje funkcje poznawcze, wywołując objawy podobne do otępienia oraz zwiększa ryzyko depresji.

Naukowcy odkryli, że samotność ma na nas wyniszczający wpływ, i to już na poziomie komórkowym. Profesor Steve Cole, dyrektor laboratorium UCLA Social Genomics Core dowiódł nawet, że z powodu izolacji społecznej w ludzkim organizmie dochodzi do tworzenia się przewlekłego, niedającego jawnych objawów stanu zapalnego.

Z danych GUS-u i tych zebranych przez portale randkowe wynika, że w Polsce w ciągu ostatnich 10 lat liczba osób żyjących samotnie wzrosła o 34 proc.

Wśród krajów europejskich w niechlubnym rankingu zwycięża jednak Szwecja, w stolicy której samotnie mieszka aż 58 proc.(!) populacji. Z kolei w Stanach Zjednoczonych odsetek ten wynosi 27 proc. (w Nowym Jorku prawie 50 proc.) i cały czas rośnie – dla porównania w roku 1920 jednoosobowe gospodarstwo domowe prowadziło tam 5 proc. obywateli.

the market size: the global note-taking management software market is estimated to reach $1.35 billion by 2026, growing at a CAGR of 5.32% from 2019 to 2026greater scope for innovation: eg., be it creating a task list, a roadmap, or a design repository, Notion can handle it alllack of satisfaction: it’s noted that people always use a combination of note-taking apps and hardly stick to one for a long time

if you’re looking for a tool to increase your metacognition, try Roam Research.

Metacognition, put simply, is “thinking about thinking” or “knowing about knowing.” It’s being aware of your own awareness so you can determine the best strategies for learning and problem-solving, as well as when to apply them. The word “metacognition” literally means “above cognition”—it’s one of the most powerful forms of self-monitoring and self-regulation. It’s a fancy word for something fairly simple once you break it down.

An end-user (resource owner 👤) grants a printing service (app 📦) access to their photo (resource 🖼) hosted in a photo-sharing service (resource server 📚), without sharing their username and password. Instead, they authenticate directly with a server trusted by the photo-sharing service (authorization server 🛡), which issues the printing service delegation-specific credentials (access token 🔑).

For this study, researchers conducted technical interviews of 48 computer science undergraduates and graduate students. Half of the study participants were given a conventional technical interview, with an interviewer looking on. The other half of the participants were asked to solve their problem on a whiteboard in a private room. The private interviews did not require study participants to explain their solutions aloud, and had no interviewers looking over their shoulders. Researchers measured each study participant’s interview performance by assessing the accuracy and efficiency of each solution. In other words, they wanted to know whether the code they wrote would work, and the amount of computing resources needed to run it. “People who took the traditional interview performed half as well as people that were able to interview in private,” Parnin says. “In short, the findings suggest that companies are missing out on really good programmers because those programmers aren’t good at writing on a whiteboard and explaining their work out loud while coding.”

(This is why writing is important. It’s harder to fool yourself that you understand something when you sit down to write about it and it comes out all disjointed and confused. Writing forces clarity.)

(This is why writing is important. It’s harder to fool yourself that you understand something when you sit down to write about it and it comes out all disjointed and confused. Writing forces clarity.)

One component of it is energy: thinking hard takes effort, and it’s much easier to just stop at an answer that seems to make sense, than to pursue everything that you don’t quite get down an endless, and rapidly proliferating, series of rabbit holes.

What this means is that you can internalize good intellectual habits that, in effect, “increase your intelligence”. ‘Intelligence’ is not fixed.

Intelligent people simply aren’t willing to accept answers that they don’t understand — no matter how many other people try to convince them of it, or how many other people believe it, if they aren’t able to convince them selves of it, they won’t accept it.

The smartest person I’ve ever known had a habit that, as a teenager, I found striking. After he’d prove a theorem, or solve a problem, he’d go back and continue thinking about the problem and try to figure out different proofs of the same thing. Sometimes he’d spend hours on a problem he’d already solved.

So in brief, for our application service, if we understand the access patterns very well, they’re repeatable, they’re consistent, and scalability is a big factor, then NoSQL is a perfect choice.

Comparison Time … 🤞

The lessons you learn from chess are generalizable only at a high level (e.g. a bad plan is better than no plan). But if you have games that are (a) fun and (b) accurate for some aspects of reality, such as KSP or Factorio, you do get learning that is real and transferable. The challenge is in making games that satisfy both constraints.

Although the impact will not be distributed evenly, digital transformation will touch virtually every corner of the global workforce — from food production (324,000 new jobs) to healthcare (2 million) and the automotive industry (6 million).

Over the next five years, we estimate that the global workforce can absorb around 149 million new technology-oriented jobs. Software development accounts for the largest single share of this forecast, but roles in related fields like data analysis, cyber security, and privacy protection are also poised to grow substantially.

Most of the discourse is about how AI will “replace” humans. I prefer the Licklider school of thought: human-computer symbiosis. AI will make humans vastly more effective by automating tedious tasks. For example, humans can use text AI such as GPT-3 to generate ideas/boilerplate writing to get around the terror of the blank page, and then simply pick the best ones and refine/iterate on those. (AI Dril, which was based on GPT-2, was an early example of this). As AI gets better, “assistive creativity” will become a bigger thing, enabling humans to create sophisticated artifacts (including video games!) easier and better than ever.

So it makes sense that video games would be the primary educational environment of the future: they are the best way we have of (a) creating simulations of reality (b) with fast feedback loops (c) accessible at low cost.

Video games will become a core component of education. This sounds absurd, but consider that simulations are already used widely for learning

When you understand something very well it’s almost as though you can play around with it using all your senses — touch, feeling, space.

The fundamental principle of education is to give students an environment, and tools, where they can make discoveries themselves. This requires space, and time, and autonomy.

Wyróżniamy 2 podstawowe metody wykrywania malware(można mówić o większej ilości, jednak na potrzeby przybliżenia podstaw wspomnę tylko o dwóch):

How to prevent the environment from being “invalidated”?Docker containers (Rocker)

SAS, R, Stata, SPSS may return different results even for quantiles, or due to floating number representation! The results should be maximally close to each other, but what about resampling methods (SAS and R gives different random numbers for the same seed)?

Typical software requirements specify the following

99.9% open-source. 0.1% is licensed (free for non-commercial use)

Status of R on the Clinical Research market

Differences in

Tospeeduptheprocesswithoutsacrificingaccuracy,theteamalsousesRevolutionRanalyticproducts

The results suggest that, in addition to a distinct beginning and end to the unprecedented bloodshed of 1910-1950, there was another abrupt shift towards a greater level of peace in the early 1990s.

The brain uses the same area to save coding as it does to save our speech. They found that programming is like talking. The research found out that the brain regions that are most active during coding are those that are also relevant in the processing of natural language.

Estimates say that 83% of us will be hit with a mental health crisis in our lives, we can all make the choices to invest wisely in this area to improve our ‘mental durability’ to deal with it properly.

in Python - setting up basic logger is very simple

import logging
logging.basicConfig(
    filename='application.log',
    level=logging.WARNING,
    format= '[%(asctime)s] {%(pathname)s:%(lineno)d} %(levelname)s - %(message)s',
    datefmt='%H:%M:%S'
)

logging.error("Some serious error occurred.")
logging.warning('Function you are using is deprecated.')

[12:52:35] {<stdin>:1} ERROR - Some serious error occurred.
[12:52:35] {<stdin>:1} WARNING - Function you are using is deprecated.

logging.getLoggerClass().root.handlers[0].baseFilename

This advertising system is designed to enable hyper-targeting, which has many unintended consequences that have dominated the headlines in recent years, such as the ability for bad actors to use the system to influence elections, to exclude groups in a way that facilitates discrimination, and to expose your personal data to companies you’ve never even heard of.

if you search for something on Google, you may start seeing ads for it everywhere.

Alarmingly, Google now deploys hidden trackers on 76% of websites across the web to monitor your behavior and Facebook has hidden trackers on about 25% of websites, according to the Princeton Web Transparency & Accountability Project. It is likely that Google and/or Facebook are watching you on most sites you visit, in addition to tracking you when using their products.

When you search on DuckDuckGo, we can show you an ad based on the keywords you type in. That’s it. And it works.

Almost all of the money search engines make (including Google) is based on the keywords you type in, without knowing anything about you, including your search history or the seemingly endless amounts of additional data points they have collected about registered and non-registered users alike. In fact, search advertisers buy search ads by bidding on keywords, not people. It makes intuitive sense, too. If you search for ‘car’, you are more likely to respond to a car ad than something you searched for last week.

If you want a fast website without breaking the bank, go for GitHub Pages. It’s completely free and super fast. It does, however, require you to open source your site. If that’s not doable, CloudFront is a good alternative, but its price depends on how much bandwidth you push around. For most personal sites, CloudFront won’t cost more than a couple of dollars per month. The same thing goes for Google Cloud Storage.

The best all-around performer is AWS CloudFront, followed closely by GitHub Pages. Not only do they have the fastest response times (median), they’re also the most consistent. They are, however, closely followed by Google Cloud Storage. Interestingly, there is very little difference between a regional and multi-regional bucket. The only reason to pick a multi-regional bucket would be the additional uptime guarantee. Cloudflare didn’t perform as well I would’ve expected.

Some free, digital Zettelkastens include zettelkasten.de, zettlr, and roamresearch. I use Roam.

The key is to make connections between ideas during note-taking, way before you need to review them for your work. This forces you to actively connect the dots (during note-taking) and lets you find relevant ideas with ease in future.

German sociologist Niklas Luhmann. One thing you should know about Luhmann—he was extremely productive. In his 40 years of research, he published more than 70 books and 500 scholarly articles. How did he do accomplish this? He credits it to his Zettelkasten which focuses on connections between notes.

it allows each new key to be given a default value based on the type of dictionary being created

Memoization can be achieved through Python decorators

import timeit

def memoize_fib(func):
    cache = {}
    def inner(arg):
        if arg not in cache:            
            cache[arg] = func(arg)
        return cache[arg]
    return inner


def fib(num):
    if num == 0:
        return 0
    elif num == 1:
        return 1
    else:
        return fib(num-1) + fib(num-2)

fib = memoize_fib(fib)


print(timeit.timeit('fib(30)', globals=globals(), number=1))
print(timeit.timeit('fib(30)', globals=globals(), number=1))
print(timeit.timeit('fib(30)', globals=globals(), number=1))

4.9035000301955733e-05
1.374000021314714e-06
1.2790005712304264e-06

A deep copy refers to cloning an object. When we use the = operator, we are not cloning the object; instead, we reference our variable to the same object (a.k.a. shallow copy).

Python 2 is entrenched in the software landscape to the point that co-dependency between several softwares makes it almost impossible to make the shift.

The @property decorator allows a function to be accessed like an attribute.

var = true_val if condition else false_val

to_check = 6
msg = "Even" if to_check%2 == 0 else "Odd"
print(msg)

msg = ""
if(to_check%2 == 0):
  msg = "Even"
else:
  msg = "Odd"
print(msg)

This method is automatically called to allocate memory when a new object/ instance of a class is created.

decorator is a design pattern in Python that allows a user to add new functionality to an existing object without modifying its structure

The philosophy behind Flask is that it gives only the components you need to build an app so that you have the flexibility and control. In other words, it’s un-opinionated. Some features it offers are a build-int dev server, Restful request dispatching, Http request handling, and much more.

The only difference is that range returns a Python list object and xrange returns an xrange object. This means that xrange doesn’t actually generate a static list at run-time like range does.

NumPy is not just more efficient; it is also more convenient. You get a lot of vector and matrix operations for free, which sometimes allow one to avoid unnecessary work. And they are also efficiently implemented.

process of retrieving original Python objects from the stored string representation

Pickle module accepts any Python object and converts it into a string representation and dumps it into a file by using dump function

The beauty of lambda functions lies in the fact that they return function objects. This makes them helpful when used with functions like map or filter which require function objects as arguments.

In Python, the term monkey patch only refers to dynamic modifications of a class or module at run-time.

Python has a multi-threading package but if you want to multi-thread to speed your code up, then it’s usually not a good idea to use it. Python has a construct called the Global Interpreter Lock (GIL). The GIL makes sure that only one of your ‘threads’ can execute at any one time. A thread acquires the GIL, does a little work, then passes the GIL onto the next thread.

the only meditation I have EVER found effective in my life. 1:19:57 Intro and explanation + 1:27:51 Meditation start

- High-level modules should not depend on low-level modules. Both should depend on the abstraction.- Abstractions should not depend on details. Details should depend on abstractions.

Clients should not be forced to depend on methods that they do not use.

If S is a subtype of T, then objects of type T in a program may be replaced with objects of type S without altering any of the desirable properties of that program.

Classes should be open for extension, but closed for modification

A class should have a single responsibility

Alternatively, think about the decision in different timelines. Ask yourself: What will be the consequences of this decision in 10 minutes?  10 months? 10 years?

Consider a decision you have to make. Start by looking at the most immediate effects of making this decision – the first order. Then for each of the effect ask yourself: "And then what?"

I've found that the most underrated problem solving tool is simply typing out my thought process.I used to be the type who asked a lot of questions until I realized that formulating the question was often more important than getting the answer.

Amazon Machine Learning Deprecated. Use SageMaker instead.

The MRI results showed that people for whom this effect was the strongest--those whose exposure to diverse experiences was more strongly associated with positive feeling ("affect")--exhibited greater correlation between brain activity in the hippocampus and the striatum. These are brain regions that are associated, respectively, with the processing of novelty and reward-- beneficial or subjectively positive experiences.

The interface is slightly less polished (and, unfortunately, I experience a delay between opening it and getting a chart), but the tracking works. Since it is in Vue I love, one day I might modify it to my needs.

It is a modern-day “memento mori”

With Intention I:know how much of my time is spent on distractions,decide how much time I need,I see the countdown (so I know if I need to wrap-up a reply, or if it makes sense to start writing a new one),it automatically blocks these sites,yet, it distinguishes between “normal use” of YouTube and e.g. using it for creating a workshop on deep learning (looking for video abstract of recent papers).

There are a few plugins (e.g. ColdTurkey) that are “too nuclear”. Being halted in the middle of writing a reply (on Facebook or Hacker News), with no prior warning, left me disturbed.

Hot Reloading refers to the ability to automatically update a running web application when changes are made to the application’s code.

JupyterDash supports three approaches to displaying a Dash application during interactive development.

# Run app and display result inline in the notebookapp.run_server(mode='inline')

app.run_server(mode='inline')

If running the server blocks the main thread, then it’s not possible to execute additional code cells without manually interrupting the execution of the kernel.JupyterDash resolves this problem by executing the Flask development server in a background thread. This leaves the main execution thread available for additional calculations. When a request is made to serve a new version of the application on the same port, the currently running application is automatically shut down first. This makes is possible to quickly update a running application by simply re-executing the notebook cells that define it.

You can also try it out, right in your browser, with binder.

Then, copy any Dash example into a Jupyter notebook cell and replace the dash.Dash class with the jupyter_dash.JupyterDash class.

from jupyter_dash import JupyterDash

import dash

import plotly.express as px
from jupyter_dash import JupyterDash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output

sci-hub is a service that lets you access research papers for free. It’s not quuiiiiiiiiite legal, but buying papers is super expensive, like fifty dollars per paper. Not something I can afford, and I think the entire industry is evil anyway.

So instead I have it open up Frink. If Frink is already open, it activates it. If it’s already active, it minimizes it. Much better UX.3

toggle_app(app, location) 
{
    if WinExist(app)   
    {
        if !WinActive(app)
        {
            WinActivate
        }
        else
        {
            WinMinimize
        }
  }
    else if location != ""
    {
        Run, %location%
    }
}

Launch_App2::toggle_app("Frink", "\Path\to\frink.jar")

My friends ask me if I think Google Cloud will catch up to its rivals. Not only do I think so — I’m positive five years down the road it will surpass them.

So if GCP is so much better, why so many more people use AWS?

As I mentioned I think that AWS certainly offers a lot more features, configuration options and products than GCP does, and you may benefit from some of them. Also AWS releases products at a much faster speed.You can certainly do more with AWS, there is no contest here. If for example you need a truck with a server inside or a computer sent over to your office so you can dump your data inside and return it to Amazon, then AWS is for you. AWS also has more flexibility in terms of location of your data centres.

I would argue that there isn’t any other company on the planet that does scalability and global infrastructure better than Google (although CloudFlare definitely gives it a run for its money in some areas).

Both AWS and GCP are very secure and you will be okay as long as are not careless in your design. However GCP for me has an edge in the sense that everything is encrypted by default.

I felt that performance was almost always better in GCP, for example copying from instances to buckets in GCP is INSANELY fast

AWS charges substantially more for their services than GCP does, but most people ignore the real high cost of using AWS, which is; expertise, time and manpower.

GCP on the other hand has fewer products but the ones they have (at least in my experience) feel more complete and well integrated with the rest of the ecosystem

GCP provides a smaller set of core primitives that are global and work well for lots of use cases. Pub/Sub is probably the best example I have for this. In AWS you have SQS, SNS, Amazon MQ, Kinesis Data Streams, Kinesis Data Firehose, DynamoDB Streams, and maybe another queueing service by the time you read this post. 2019 Update: Amazon has now released another streaming service: Amazon Managed Streaming Kafka.

At the time of writing this, there are 169 AWS products compared to 90 in GCP.

Spinning an EKS cluster gives you essentially a brick. You have to spin your own nodes on the side and make sure they connect with the master, which a lot of work for you to do on top of the promise of “managed”

You can forgive the documentation in AWS being a nightmare to navigate for being a mere reflection of the confusing mess that is trying to describe. Whenever you are trying to solve a simple problem far too often you end up drowning in reference pages, the experience is like asking for a glass of water and being hosed down with a fire hydrant.

Jeff Bezos is an infamous micro-manager. He micro-manages every single pixel of Amazon’s retail site. He hired Larry Tesler, Apple’s Chief Scientist and probably the very most famous and respected human-computer interaction expert in the entire world, and then ignored every goddamn thing Larry said for three years until Larry finally — wisely — left the company. Larry would do these big usability studies and demonstrate beyond any shred of doubt that nobody can understand that frigging website, but Bezos just couldn’t let go of those pixels, all those millions of semantics-packed pixels on the landing page. They were like millions of his own precious children. So they’re all still there, and Larry is not.

The AWS interface looks like it was designed by a lonesome alien living in an asteroid who once saw a documentary about humans clicking with a mouse. It is confusing, counterintuitive, messy and extremely overcrowded.

After you login with your token you then need to create a script to give you a 12 hour session, and you need to do this every day, because there is no way to extend this.

In GCP you have one master account/project that you can use to manage the rest of your projects, you log in with your company google account and then you can set permissions to any project however you want.

It’s not that AWS is harder to use than GCP, it’s that it is needlessly hard; a disjointed, sprawl of infrastructure primitives with poor cohesion between them.

So be careful running editing a bash script that may be currently executing. It could execute an invalid command, or do something very surprising.

“If you are a non-college graduate man you have a less than 50/50 shot of ever being married in your life” – Andrew YangIn the 1970s and ‘80s, there were about 17 million manufacturing jobs in the USToday, there are about 12 million of those jobsMore women are graduating from college than men58% of college graduates in the US are women

Today, 40% of children are born to unmarried mothersBack in the 70s and 80s, it was only 15%

The most common method of preventing CSRF is by generating a secret random string, known as a CSRF token, on the server, and checking for that token when the client performs a write.

CORS relaxes the Same-Origin Policy (SOP), a critical security measure that prevents scripts on one site (e.g. the attacker’s site) from accessing sensitive data on another site (e.g. the Definitely Secure Bank portal). If something was protecting you from CSRF, it would be the SOP.

Cross-Site Request Forgery is a web security exploit where an attacker induces a victim to perform an action they didn’t mean to. In this case, the attacker tricked you into unintentionally transferring them money.

This might be super basic, but... assume positive intent.Your parent is not your enemy. Your teacher is not your enemy. Your boss is not your enemy. The other team at work is not your enemy.

We present results from technical experiments which reveal that WeChat communications conducted entirely among non-China-registered accounts are subject to pervasive content surveillance that was previously thought to be exclusively reserved for China-registered accounts.

So write with a combination of short, medium, and long sentences. Create a sound that pleases the reader’s ear. Don’t just write words. Write music.”

You can create estimation plots here at estimationstats.com, or with the DABEST packages which are available in R, Python, and Matlab.

Relative to conventional plots, estimation plots offer five key benefits:

For comparisons between 3 or more groups that typically employ analysis of variance (ANOVA) methods, one can use the Cumming estimation plot, which can be considered a variant of the Gardner-Altman plot.

Efron developed the bias-corrected and accelerated bootstrap (BCa bootstrap) to account for the skew whilst obtaining the central 95% of the distribution.

We can calculate the 95% CI of the mean difference by performing bootstrap resampling.

Shown above is a Gardner-Altman estimation plot.

Jitter plots avoid overlapping datapoints (i.e. datapoints with the same y-value) by adding a random factor to each point along the orthogonal x-axes.

Unfortunately, the boxplot still doesn't show all our data.

The barplot has several shortcomings, even though its use in academic journals is endemic.

Continuous Delivery of Deployment is about running as thorough checks as you can to catch issues on your code. Completeness of the checks is the most important factor. It is usually measured in terms code coverage or functional coverage of your tests. Catching errors early on prevents broken code to get deployed to any environment and saves the precious time of your test team.

Continuous Integration is a trade off between speed of feedback loop to developers and relevance of the checks your perform (build and test). No code that would impede the team progress should make it to the main branch.

A good CD build: Ensures that as many features as possible are working properly The faster the better, but it is not a matter of speed. A 30-60 minutes build is OK

A good CI build: Ensures no code that breaks basic stuff and prevents other team members to work is introduced to the main branch Is fast enough to provide feedback to developers within minutes to prevent context switching between tasks

The idea of Continuous Delivery is to prepare artefacts as close as possible from what you want to run in your environment. These can be jar or war files if you are working with Java, executables if you are working with .NET. These can also be folders of transpiled JS code or even Docker containers, whatever makes deploy shorter (i.e. you have pre built in advance as much as you can).

Continuous Delivery is about being able to deploy any version of your code at all times. In practice it means the last or pre last version of your code.

Continuous Integration is not about tools. It is about working in small chunks and integrating your new code to the main branch and pulling frequently.

The app should build and start Most critical features should be functional at all times (user signup/login journey and key business features) Common layers of the application that all the developers rely on, should be stable. This means unit tests on those parts.

Continuous Integration is all about preventing the main branch of being broken so your team is not stuck. That’s it. It is not about having all your tests green all the time and the main branch deployable to production at every commit.

If knowledge of one item makes it harder to remember another item, we have a case of memory interference.

Here again are the twenty rules of formulating knowledge.

If you see a word and immediately check it's translation, you'll hardly memorize it at all. If you try your best to recall what the word means before checking the translation, the chances of memorization are much better. It is much easier to memorize a word's meaning when you know how to pronounce it corretly, so get it right.  

Somewhere between too hard and too easy, there’s a sweet-spot where reviews are challenging enough to hold your interest, but not so hard that it feels like torture. When the challenge of reviews is just right, you’ll actually get a sense of accomplishment and a little jolt of dopamine as you do them. Our brains actually enjoy challenges as long as they aren’t too hard or too easy. As I see it, this level of challenge is where you want to be.

Researchers have found that reviews are more effective when they’re difficult. That is, if you have to work at remembering a card, it’ll have a stronger effect on your memory. The harder a review is, the more it boosts your memory. This is called “desirable difficulty” in the literature.

Kolejna zmienna - "Recency", czyli informacja o tym, jak dawno klient robił zakupy w sklepie.

df['Recency'] = (today - df.InvoiceDate)/np.timedelta64(1,'D')

abt = df.groupby(['CustomerID']).agg({'Recency':'min', 'MonetaryValue':'sum', 'InvoiceNo':'count'})

abt = df.groupby(['CustomerID']).agg({'Recency':'min', 'MonetaryValue':'sum', 'InvoiceNo':'count'})
abt.rename(columns = {'InvoiceNo':'Frequency'}, inplace = True)
abt = abt[['Recency', 'Frequency', 'MonetaryValue']]
abt.head()

Będę oceniać klientów kryteriami, jakie zakłada metoda RFM.

Usuwam brakujące wartości w zmiennej "CustomerID".

df = df[~df.CustomerID.isnull()]

df['CustomerID'] = df.CustomerID.astype(int)

df.drop(['Description', 'StockCode', 'Country'], axis = 1, inplace = True)

Sprawdzam braki danych.

print(str(round(df.isnull().any(axis=1).sum()/df.shape[0]*100,2))+'% obserwacji zawiera braki w danych.')

24.93% obserwacji zawiera braki w danych.

Buduję prosty data frame z podstawowymi informacjami o zbiorze.

summary = pd.DataFrame(df.dtypes, columns=['Dtype'])
summary['Nulls'] = pd.DataFrame(df.isnull().any())
summary['Sum_of_nulls'] = pd.DataFrame(df.isnull().sum())
summary['Per_of_nulls'] = round((df.apply(pd.isnull).mean()*100),2)
summary.Dtype = summary.Dtype.astype(str)
print(summary)

                      Dtype  Nulls  Sum_of_nulls  Per_of_nulls
InvoiceNo            object  False             0         0.000
StockCode            object  False             0         0.000
Description          object   True          1454         0.270
Quantity              int64  False             0         0.000
InvoiceDate  datetime64[ns]  False             0         0.000
UnitPrice           float64  False             0         0.000
CustomerID          float64   True        135080        24.930
Country              object  False             0         0.000

An animated step-by-step guide to improving your pie charts.

If you, on the other hand, want to go the student-like route (living in Wohngemeinschaft, not eating out too much) and try to save, you can easily live on 1,500-2,000 CHF per month and save the majority of your salary.

We are now cooperating with Credit Agricole Bank and Revolut - if you have already moved to Switzerland you can open a free bank account and get 100 CHF bonus - email us to get the bonus code.

120,000 CHF annually according to this calculator gets you 7,746.20 CHF net per month.

2) Rent only a room - it might be a good option if you come without family (in Switzerland it’s called living in a Wohngemeinschaft).

WHERE clause introduces a condition on individual rows; HAVING clause introduces a condition on aggregations, i.e. results of selection where a single result, such as count, average, min, max, or sum, has been produced from multiple rows. Your query calls for a second kind of condition (i.e. a condition on an aggregation) hence HAVING works correctly. As a rule of thumb, use WHERE before GROUP BY and HAVING after GROUP BY. It is a rather primitive rule, but it is useful in more than 90% of the cases.

Scrum means that “you have to get certain things done with those two weeks.” Kanban means “do what you can do in two weeks.”

What people will say is that estimates are for planning – that their purpose is to figure out how long some piece of work is going to take, so that everybody can plan accordingly. In all my five years shipping stuff, I can only recall one project where things really worked that way.

Be explicit about the difference between hard deadlines

If you delegate all your IT security to the InfoSec, they will come up with draconian rules

you should always advocate for having a dedicated SRE if there’s any real risk of after-hours pages that are out of your control.

I try to write a unit test any time the expected value of a defect is non-trivial.

I’m defining an integration test as a test where you’re calling code that you don’t own

Which database technology to choose

I would use a serverless function when I have a relatively small and simple chunk of code that needs to run every once in a while.

Programming languages These will probably expose my ignorance pretty nicely.

Now, a couple of years later, my guidelines for JS are:

Consuming media (books, blogs, whatever) is not inherently a compounding thing. Only if you have some kind of method to reflect, to digest, to incorporate your knowledge into your thoughts. If there is anything valuable in this post, you, reader, will probably not benefit from it unless you do something active to “process” it immediately.

if you have an amazing manager at a shit company you’ll still have a shit time. In some ways, it’ll actually be worse. If they’re good at their job (including retaining you), they’ll keep you at a bad company for too long. And then they’ll leave, because they’re smart and competent. Maybe they’ll take you with them.

I do think when a lot of managers realized they’ve hit their peak or comfort level, they then start to focus on playing politics instead of delivering results to hold onto their position. These are also the kind of managers who would only hire people less capable than them, for fear of being replaced.

“In practice people gravitate to, hire and promote individuals they like to be around, not people who demand accountability.”

Dr. Peter advises creative incompetence — pretending to be incompetent but doing it in an area or manner where it doesn’t actually impair your work.

Dr. Peter also surmised that “super competent” people tend to “disrupt the hierarchy.” I suppose that’s a nice way of saying you’ve made your boss look bad by being more capable.In such situations, you’ll probably find yourself deliberately suppressed or edged out sooner or later — for some stupid reason or blame pushing.

So if you’re a highly competent and aggressive individual, it’s best you find yourself a job in a startup, be an entrepreneur, or work in a company that needs turning around.

Dr. Peter also had another interesting theory about getting promoted. He considered working hard and improving your skill sets not as effective as something called pull promotion. That’s when you get promoted — faster than usual — when a mentor or patron pulls you up.No wonder there’s so much butt kissing in the corporate world. They must have read Dr. Peter’s research from the ‘60s.

managers fail to see and address this problem is that they are used to looking at communication and assume it's a good thing. Because they see activity

A study conducted by Gloria Marks, a Professor of Informatics at the University of California, revealed that it takes us an average of 23 minutes and 15 seconds to refocus on a task after an interruption, and even when we do, we experience a decrease in productivity

It doesn't mean that we ignore all messages and only look up from our work when something is on fire – but the general expectation is that it's okay to not be immediately available to your teammates when you are focusing on your work

Working in an open office renders us even more vulnerable

Office hours are chunks of time that makers set aside for meetings, while the rest of the time they are free to go into a Do Not Disturb mode

People think it’s efficient to distribute information all at the same time to a bunch of people around a room. But it’s actually a lot less efficient than distributing it asynchronously by writing it up and sending it out and letting people absorb it when they’re ready to so it doesn’t break their days into smaller bits.”

it's a matter of culture. None of these rules would work if the management fails to see that makers need to follow a different schedule

context switching between communication and creative work only kills the quality of both

since most powerful people operate on the manager schedule, they're in a position to force everyone to adapt to their schedule

The most straightforward way to address this is to build a team knowledge base. Not only does that minimize the number of repetitive questions bounced around the office, it allows new team members to basically onboard themselves.

almost no organizations today support maker schedules

For managers, interruptions in the form of meetings, phone calls, and Slack notifications are normal. For someone on the maker schedule, however, even the slightest distraction can have a disruptive effect

For many data scientists, the finished product of a work session is a business analysis. They need to show team members—who oftentimes aren’t technical—how their data became a specific recommendation or insight.

Work never ends. No matter how much you get done there will always be more. I see a lot of colleagues burn out because they think their extra effort will be noticed. Most managers appriciate it but do not promote their employees.

Don't focus too much on the salary. It's just one tiny part of the whole package.Your dev job pays your rent, food and savings. I assume that most dev jobs do this quite well.Beyond this, the main goal of a job is to increase your future market value, your professional network and to have fun. So. basically it's about how much you are worth in your next job and that you enjoy your time.A high salary doesn't help you if you do stuff which doesn't matter in a few years.

COVID-19 has spurred a shift to analyze things like supply chain disruptions, speech analytics, and filtering out pandemic-related behavior, such as binge shopping, Burtch Works says. Data teams are being asked to create new simulations for the coming recession, to create scorecards to track pandemic-related behavior, and add COVID-19 control variables to marketing attribution models, the company says.

70% async using Twist, Github, Paper25% sync using something like Zoom, Appear.in, or Google Meet5% physical meetings, e.g., annual company or team retreats

According to the Harvard Business Review article “Collaborative Overload”, the time employees spend on collaboration has increased by 50% over the past two decades. Researchers found it was not uncommon for workers to spend a full 80% of their workdays communicating with colleagues in the form of email (on which workers’ spend an average of six hours a day); meetings (which fill up 15 percent of a company’s time, on average); and more recently instant messaging apps (the average Slack user sends an average of 200 messages a day, though 1,000-message power users are “not the exception”)

I also recently took about 10 months off of work, specifically to focus on learning. It was incredible, and I don’t regret it financially. I would often get up at 6 in the morning or even earlier (which I never do) just from excitement about what I was going to learn about and accomplish in the day. Spending my time focused Only on what I was most interested in was incredibly rewarding.

I'm working for myself right now, but if one day I needed to go get a full-time job again, I would almost certainly not go to big tech again. I'd rather get paid a fifth of what I was doing, but do something that leaves me with some energy after I put in a day's work

more money comes at the cost of very high expectations and brutal deadlines

Things to consider when crafting out Job Ads and descriptions

Truth be told, we found that most companies we worked with preferred to own the analytical backend.

Talented people flock to employers that promise to invest in their development whether they will stay at the company or not.