Fox & Friends, före och efter valet i USA

Från Journalism++ Stockholm

Här har Vox gjort textanalys på allt den konservativa morgonshowen Fox & Friends sänt de senaste 17 månaderna, baserat på Internet Archives textarkiv över amerikansk tv. De har använt Pythonbiblioteket NLTK för det mesta jobbet, och något som heter Vader för sentimentanalys.

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Big data – a new opportunity for statistics production

http://vilniusworkshop2017.vgtu.lt/wp-content/uploads/2017/07/vilnius-japec-final.pdf

From: Workshop on Survey Statistics Theory and Methodology

The Baltic-Nordic-Ukrainian Network on Survey Statistics is a non-governmental group of enthusiastic survey statisticians in Estonia, Finland, Latvia, Lithuania, Sweden, Ukraine and Belarus and their home institutions, most of them being a university or a national statistical agency. It started in 1996 as a Baltic-Nordic network and was extended to include also Ukraine and Belarus. Four Summer Schools 1997, 2009, 2013 and 2016 in Estonia, Ukraine, Belarus and Ukraine, twelve Workshops 1998-2001, 2003-2006 and 2008, 2010, 2012 and 2014 in Latvia, Lithuania and Estonia, and four Conferences 2002, 2007, 2011 and 2015 in Sweden and Finland were organized. Exchange visits for university teachers, research students and survey practitioners to universities within the Network with a total duration of more than 100 months were also arranged. The website of the Network is http://wiki.helsinki.fi/display/BNU/Home.

Krönikör Johan Norberg: "I anonymiteten blir vi skamlöst ärliga"

Godmorgon, världen! | Sveriges Radio:
Få opinionsundersökare eller analytiker trodde att Donald Trump skulle vinna presidentvalet. Han låg under ett par procentenheter bland annat efter rasistiska utfall och svarta väljare skulle ju mobilisera mot honom.

Men om vi hade konsulterat dataanalytiker hade dessa kunnat berätta att det fanns ett problem med den förutsägelsen. Vita amerikaners googlesökningar i flera swingstater antydde att de hade större rasfördomar än de uttryckte öppet, och svarta sökte inte efter information om väljarregistrering och vallokaler som de borde ha gjort om de tänkt rösta i den utsträckning de påstod sig planera.

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Big Geospatial Data helps modernize the Statistical Field

Big Geospatial Data helps modernize the Statistical Field:
Geospatial tools and information are going mainstream, as is evident following the recent opening of a new data science campus at the Office for National Statistics headquarters. Niall Conway explains how the fields of Statistics, Big Data and Geospatial are beginning to converge in order to deliver better insights to government.

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OLLE NERMAN SYMPOSIUM 1-2 SEPTEMBER 2017

Alla är välkomna 1-2 september på en konferens i Göteborg. Program och detaljer om anmälan framgår av webb-sidan.

​- From Branching Processes to Clinical Trials
The title ties together Olle Nerman’s work in Mathematical Statistics during more than three decades. Mathematical Statistics is a wide branch of mathematical sciences including both probability and statistics and not the least many applications in various other scientific disciplines. The research of Olle Nerman is a perfect illustration of this. In particular, he has been instrumental in establishing and promoting biostatistics in its various forms stretching from general population models, with applications to cell kinetics, to clinical trials and bioinformatics. His early theoretical work, which led to a fundamental understanding of the structure of general branching processes, is increasingly referred to in new publications.

​Symposium web site: http://www.chalmers.se/sv/institutioner/math/forskning/konferenser-pa-MV/Sidor/Nerman-Symposium-2017.aspx

Plats: Euler, Skeppsgränd 3, Matematiska Vetenskaper, Chalmers och Göteborgs Universitet
Tid: 2017-09-01 12:30
Sluttid: 2017-09-02 13:00
Vänliga hälsningar, Peter Jagers

Statistical Analysis with the Wolfram Language

Statistics Video Series: Wolfram U:
Analyze your data using a framework of model fitting and statistical analysis built into the Wolfram Language. Whether it is basic descriptive and exploratory statistics or advanced modeling with statistical distributions, you can follow this learning path to gain an understanding of the statistical functionality available in the Wolfram Language. Topics covered include descriptive measures, transformations, basic clustering, statistical distributions, parameter estimation and hypothesis testing. Advanced topics on using optimization functions, linear algebra functions, analysis of variance (ANOVA) and generalized logit and probit linear models will also be explored.

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Jobs Rated Report 2017: Ranking 200 Jobs

CareerCast.com:
Research from the Bureau of Labor Statistics’ American Time Use Survey indicates the majority of adults in the United States spend 8.8 hours a day on their careers, or on activities directly related. Over a 5-day week, that translates to 44 hours, and that totals more than one-quarter of our lives.

CareerCast.com’s Jobs Rated report aims to provide a general snapshot of 200 careers every year, using key criteria: Income, Growth Outlook, Work Environment and Stress. The results offer a glimpse into this fundamental aspect of day-to-day life.

The Jobs Rated report has tracked for almost 30 years, and in that time, the labor market’s changed dramatically.

Some of the careers ranking among the Top 10 best jobs of 2017 illustrate these changes. Careers in mathematics rank highly, with Statistician, Mathematician and Data Scientist all in the top 10 — but these jobs aren’t just about numbers in the current landscape. A career in one of these mathematical fields can translate into work in healthcare, business, marketing, even entertainment.

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Canada to modernize statistics agency, look at new data collection

Reuters

Nasdaq.com:
The Canadian government on Friday outlined its plans to modernize its main statistical agency, which include identifying new ways of collecting data and making it easier for people and businesses to find and use the information.

With information available more quickly and from a wider variety of sources than ever before, "Statistics Canada must innovate to remain responsive to the needs of Canadians", Innovation Minister Navdeep Bains said in a speech to a business group in Toronto, his first remarks on how Canada wants to become a leader in an increasingly data-driven world.

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Defining the Data Science Landscape

insideBIGDATA:
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In general, a data scientist needs to know more about statistics than an average programmer, more programming than an average statistician, and be able to apply both skills to solve business problems.

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Educators | This is Statistics

Educators | This is Statistics:
As an expert in statistics, you understand the value of an education in statistical science. Fortunately, more students are beginning to see the value, too. The number of students earning bachelor’s degrees in statistical science nearly doubled between 2009 and 2013, according to the National Center for Education Statistics. That’s great news, but it’s still not enough to fill the many jobs that require expertise in statistics.

A report by McKinsey Global Institute predicts the U.S. will need up to 190,000 new professionals with analytical skills to help manage the Big Data movement and run data analytics and business intelligence operations in the private and public sectors. And it’s not just big data that’s driving it—it’s many types of statistics jobs in all areas of the economy.

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Vem kan leda SCB in i framtiden?

Ekonomistas:
En av Sveriges viktigaste generaldirektörstjänster är vakant: den för Statistiska Centralbyrån. I dagens kunskapssamhälle spelar statistiska fakta och analyser om ekonomi, politik och medborgarnas välfärd en central roll. SCB har sedan länge stått för en hög kvalitet i den statistik som produceras, men det finns trots det utrymme för ytterligare förbättring inom vissa områden. Ett sådant är infrastrukturen gällande forskningsdata och analys, alltså både data till externa forskare och SCBs egen analytiska kapacitet. SCBs nya gd bör vara en person som besitter både kompetens och förståelse för SCBs olika roller i den moderna kunskapsnationen Sverige.

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Building capability and community through the Government Data Science Partnership

Building capability and community through the Government Data Science Partnership | Government Digital Service:
Across government, departments are beginning to realise not only the value of the data they hold, but also how they can use data science to make more effective, data-informed decisions.

The Government Data Science Partnership is a collaboration between the Government Digital Service (GDS), Office for National Statistics (ONS) and the Government Office for Science. It was formed to help government realise the potential of data science. And to support the development of skills and tools to kickstart greater take-up across departments and agencies.

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Beginner's guide to build data visualisations on the web with D3.js

From Beginner's guide to build data visualisations on the web with D3.js:
Introduction
I am sure you have heard this many times. A picture is worth a thousand words. I think with the proliferation of data, this statement can easily be modified to A picture is worth thousand(s) of data points. If you are not convinced, look at the example below.

Storytelling through Data Visualisation
Let’s look at the following statement:
“In 2013, Gun Deaths Claimed 11,419 lives in the U.S.”

What comes to your mind first?
In 2013, US saw a lot of gun violence
Gun violence is high in the US
Deaths due to Gun violence were 11,419 (in 2013)

Without visualisation the above are just a set of statistics. Though this stat is useful, it still falls short in conveying the bigger picture. You don’t get the complete context – What kind of people were involved? How much was the nation’s loss? etc.

Let’s see what happens when we visualise it:

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Vem tar vem? Ömsesidiga sympatier hos partiernas sympatisörer

Vem tar vem? Ömsesidiga sympatier hos partiernas sympatisörer:
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Figuren är hämtad från SOM-institutets årliga seminarium som hölls den 24 april, och där gjordes en särskild analys av Sverigedemokraternas plats i ett föränderligt svenskt partisystem (du kan se en inspelning av seminariet här). Sverigedemokraterna är därför utmärkta med orangea punkter i figuren. Resultaten illustrerar tydligt att SD gör skäl för namnet pariaparti. SD är ett starkt ogillat parti bland alla grupper av partisympatisörer utom, förstås, de egna sympatisörerna. Endast bland Moderaternas sympatisörer finns partier som är avgjort ännu mer ogillade (FI och V) än Sverigedemokraterna.

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Rupert Murdoch ‘could use Sky data trove for political ends’

Photograph: Josh Reynolds/AP

Business | The Guardian:
One of the “largest and most sophisticated datasets in the country” – including the TV viewing, internet and phone records of 13 million households – could be misused for political purposes if Rupert Murdoch is allowed to proceed with his plan to buy out Sky, six members of the House of Lords claim in a letter to the Observer.

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Statistics are lifeblood of democracy, and need to be protected

Quality of life depends on quality of statistics

Anthony Hilton | London Evening Standard:
People rarely think about this but the quality of daily life depends hugely on the quality of statistics.

How much do you pay for your flood insurance? How congested are the roads? How far does your child travel to school? How long do you wait for a hospital appointment? How polluted is the air? How large is your pension? The list goes on.

What governments decide is political; but the areas in which they intervene, the order on which they do things and the way a decision, once made, is implemented, relies on the available data.

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Relation between statistical machine learning and big data

Naveen Joshi | Pulse | LinkedIn:
ML and statistics

Often in fields, like pattern recognition, data mining, and knowledge discovery, we see both machine learning and statistics coming together. What brings them together is a common goal – learning from data; this means both of them focus on drawing insights or knowledge from data. However, both these methods are affected by their inherent cultural differences. While statistics is a subfield of mathematics, machine learning comes from computer science and artificial intelligence. Not to forget, machine learning is a comparatively new field, made possible by the availability of cheap computing power and availability of what we call as big data that helped data scientists to train computers to learn by analyzing data. On the contrary, statistics has existed long before computers were invented.

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Machine Learning Education: 3 Paths to Get Started

Datanami: 
Machine learning is the predictive heart of big data analytics, and one of the key skills that separates data scientists from mere analysts. But getting started with machine learning can be a challenge. Here are a few ways beginners can get off the ground with their machine learning adventure.

Machine learning is a vast field with many different specialties, so it’s quite easy for a beginner to get overwhelmed. For instance, one specialty called deep learning powers many of today’s artificial intelligence breakthroughs. But without a background in basic machine learning approaches, a prospective data scientist would have zero chance of mastering this powerful but complex technology.

Here are three general paths that neophytes can get started with machine learning: Read more...

Machine learning, and the way ahead

From MillenniumPost:
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At the very peak of the most recent 2016, Gartner Hype Cycle resides Machine Learning, which subsumes what used to be categorised as Big Data, Data Science, Artificial Intelligence, Data Mining, or Predictive Analytics. I would even include Statistics and Nonlinear Dynamics, two more traditional fields which continue to remain important, within the broad definition. While there may be slight technical differences, in common parlance they mean the same thing: the ability to use computers (i.e., "machines") and sophisticated mathematics to extract actionable predictive insights from data (i.e., "learn"). In our day and age, almost all mathematicians, statisticians, and physicists use computers, while all computer scientists who work with data usually learn the basics of traditional data science disciplines such as statistics and applied mathematics, nonlinear dynamics and network science in physics, signal processing in engineering, and econometrics in economics. Thus, any difference between the Machine Learning and what may have been once called traditional data sciences has been fast disappearing.

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Winner of the Significance young writer competition announced

StatsLife:
Judging took place last month for our writing competition for early-career statisticians. It was the most competitive contest in years, with entrants from several African nations, Australia, Belgium, Brunei, Costa Rica, India, Mexico, New Zealand, the United Kingdom and the United States.

Today we are delighted to announce our finalists, and this year’s winner. This year’s finalist are (in alphabetical order):

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