Tag: Data

Business data #business #liability #insurance

#business data

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U.S. International Trade in Goods and Services

Monthly report that provides national trade data including imports, exports, and balance of payments for goods and services. Statistics are also reported on a year-to-date basis. Data are continuously compiled and processed. Documents are collected as shipments arrive and depart, and are processed on a flow basis. The BEA uses the data to update U.S. balance of payments, gross domestic product, and national accounts. Other federal agencies use them for economic, financial, and trade policy analysis (such as import/export promotion studies and import/export price indexes). Private businesses and trade associations use them for domestic and overseas market analysis, and industry-, product-, and area-based business planning. Major print and electronic news media use them for general and business news reports.

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US Census Bureau, Department of Commerce

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Commerce Non Spatial Data.json Harvest Source

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Bureau of the Census

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Department of Commerce





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30 Can – t miss Harvard Business Review articles on Data Science, Big Data

#harvard business journal

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KDnuggets

30 Can t miss Harvard Business Review articles on Data Science, Big Data and Analytics

Here are 30 Harvard Business Review (HBR) articles on big data, data science and analytics that provide insights about the latest technology and happenings in the world of data.

There are dozens of HBR articles that are worth recommending, but here are our picks on big data, data science and analytics collected using most popular and next recommended article filters based on search term.


Full Disclosure. You can view 5 articles per month without the need to sign up and upto 15 articles can be accessed after sign up. KDnuggets derives no form of benefit if you subscribe to HBR.

On Data Science

  1. Data Scientist: the sexiest job of the 21st centuryby Thomas H. Davenport and D.J. Patil (Oct 2012)
    How the idea of LinkedIn’s People You May Know feature really clicked! The key player involved was a “Data Scientist”, a title coined by the two authors.
  2. The Sexiest Job of the 21st Century is Tedious, and that Needs to Changeby Sean Kandel (Apr 2014)
    Which phase does a data scientist spend more time on? Data Discovery, data structuring and creating context. Should they shift their focus?
  3. What Every Manager Should Know About Machine Learningby Mike Yeomans (July 2015)
    With the right mix of technical skill human judgment, machine learning could be a new tool for decision makers. Learn what mistakes to avoid.
  4. Data Scientists Don’t Scaleby Stuart Frankel (May 2015)
    We are at a new phase of big data. Is Data capture and storage now less relevant than making it more useful impactful?
  5. Get the Right Data Scientists Asking the “Wrong” Questionsby Josh Sullivan (Mar 2014)
    What makes an exceptional data scientist? Data by itself is meaningless. The skill curiosity is what makes the difference.
  6. A Data Scientist’s Real Job: Storytellingby Jeff Bladt and Bob Filbin (Mar 2013)
    How to derive insights intuitions from data? We “humanize” the data by turning raw numbers into a story about our performance.
  7. What Separates a Good Data Scientist from a Great Oneby Thomas C. Redman (Jan 2013)
    Better than the Best! Great data scientists bring four mutually reinforcing traits to bear that even the good ones can’t.
  8. Still the Sexiest Profession Aliveby DJ Patil (Nov 2013)
    Data scientist jobs are very much in demand as companies grapple with the challenge of making valuable discoveries from Big Data. Is a huge crowd just joining the bandwagon?
  9. 10 Kinds of Stories to Tell with Databy Tom Davenport (Nov 2013)
    Narrative is—along with visual analytics—an important way to communicate analytical results to non-analytical people. Explore the 10 types.
  10. How to Start Thinking Like a Data Scientistby Thomas C. Redman (Nov 2013)
    You don’t have to be a data scientist or a Bayesian statistician to tease useful insights from data. The author demonstrates how to think with a small exercise.
  11. Stop Searching for That Elusive Data Scientistby Michael Schrage(Sep 2014)
    Stop hunting for that data science unicorn and/or silver bullet. What to do instead?
  12. How to Explore Cause and Effect Like a Data Scientistby Thomas C. Redman (Feb 2014)
    While we can use data to understand correlation, the more fundamental understanding of cause and effect requires more.

Top Stories Past 30 Days

  1. The 10 Algorithms Machine Learning Engineers Need to Know
  2. 7 Steps to Mastering Machine Learning With Python
  3. 21 Must-Know Data Science Interview Questions and Answers
  4. Bayesian Machine Learning, Explained
  5. How to Become a Data Scientist – Part 1
  6. Why Big Data is in Trouble: They Forgot About Applied Statistics
  7. Data Science for Beginners: Fantastic Introductory Video Series from Microsoft
  1. The 10 Algorithms Machine Learning Engineers Need to Know
  2. Data Science for Beginners: Fantastic Introductory Video Series from Microsoft
  3. How to Become a (Type A) Data Scientist
  4. 5 EBooks to Read Before Getting into A Data Science or Big Data Career
  5. A Beginner s Guide to Neural Networks with R!
  6. How to Become a Data Scientist Part 1
  7. Reinforcement Learning and the Internet of Things




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Markets Data Center Mobile – Company Data, Indexes, Stock Quotes – More – Wall

#stock market prices

#

Footnotes

Stocks: Real-time U.S. stock quotes reflect trades reported through Nasdaq only; comprehensive quotes and volume reflect trading in all markets and are delayed at least 15 minutes. International stock quotes are delayed as per exchange requirements. Indexes may be real-time or delayed; refer to time stamps on index quote pages for information on any delays. Source: SIX Financial Information

Bonds: Bond quotes are updated in real-time. Source: Tullett Prebon.

Currencies: Currency quotes and charts are updated in real-time. Source: Tullett Prebon.

Commodities & Futures: Futures prices reflect electronic trading and are delayed 10 minutes. Futures quotes show contract month with the highest level of open interest, except crude oil, which always shows the “front month” contract (the contract that will expire soonest). Change value during the period between open outcry settle and the commencement of the next day’s trading is calculated as the difference between the last trade and the prior day’s settle. Change value during other periods is calculated as the difference between the last trade and the most recent settle. Source: SIX Financial Information.

Data are provided ‘as is’ for informational purposes only and is not intended for trading purposes. SIX Financial Information (a) does not make any express or implied warranties of any kind regarding the data, including, without limitation, any warranty of merchantability or fitness for a particular purpose or use; and (b) shall not be liable for any errors, incompleteness, interruption or delay, action taken in reliance on any data, or for any damages resulting therefrom. Data may be intentionally delayed pursuant to supplier requirements.

Sections





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Data Scientists in Demand: Salaries Rise as Talent Shortage Looms #canadian #business

#business week

#

Help Wanted: Black Belts in Data

A new species of techie is in demand these days—not only in Silicon Valley, but also in company headquarters around the world. “Data scientists are the new superheroes,” says Pascal Clement, the head of Amadeus Travel Intelligence in Madrid. The description isn’t exactly hyperbolic: The qualifications for the job include the strength to tunnel through mountains of information and the vision to discern patterns where others see none. Clement’s outfit is part of Amadeus IT Holding, the world’s largest manager of flight bookings for airlines, which has more than 40 data scientists on its payroll, including some with a background in astrophysics. The company recently launched Schedule Recovery, a product that tracks delays and automatically rebooks all affected passengers.

A study by McKinsey projects that “by 2018, the U.S. alone may face a 50 percent to 60 percent gap between supply and requisite demand of deep analytic talent.” The shortage is already being felt across a broad spectrum of industries, including aerospace, insurance, pharmaceuticals, and finance. When the consulting firm Accenture surveyed its clients on their big-data strategies in April 2014, more than 90 percent said they planned to hire more employees with expertise in data science—most within a year. However, 41 percent of the more than 1,000 respondents cited a lack of talent as a chief obstacle. “It will get worse before it gets better,” says Narendra Mulani, senior managing director at Accenture Analytics.

Many data scientists have Ph.D.s or postdoctorates and a background in academic research, says Marco Bressan, president for data and analytics at BBVA, a Spanish bank that operates in 31 countries and has a team of more than 20 data scientists. “We have nanotechnologists, physicists, mathematicians, specialists in robotics,” he says. “It’s people who can explore large volumes of data that aren’t structured.”

So-called unstructured data can include e-mails, videos, photos, social media, and other user-generated content. Data scientists write algorithms to extract insights from these troves of information. But “true data scientists are rare,” says Ricard Benjamins, head of business intelligence and big data at Telefónica, Europe’s second-largest phone company, which employs more than 200 of them. Says Stan Humphries, chief economist at Zillow, the real estate listings site: “You can find a great developer and a great researcher who has a background in statistics, and maybe you can find a great problem solver, but to find that in the same person is hard.”

Universities are taking note. MIT, where graduate students in physics, astronomy, and biology are fielding offers from outside their chosen fields, is in the process of setting up a dedicated data-science institute. Marilyn Wilson, the university’s associate director for career development, says the center will begin enrolling graduate degree candidates in 2016.

In the U.K. the University of Warwick introduced a three-year undergraduate data-science program last year, which David Firth, the program’s mastermind, says may well be the first of its kind. “Big Business was complaining about the lack of people,” he says. “Finance is a major employer, but also large-scale insurers, large online commercial retailers, high-tech startups, and government, which has huge data sets.”

Accenture’s Mulani says he’s tallied some 30 new data-science programs in North America, either up and running or in the works. The University of Virginia began offering a master’s in 2014, as did Stanford. Many of those students may be tempted to drop out before collecting their degree. “Companies are scrambling,” says Margot Gerritsen, director of Stanford’s Institute for Computational Mathematical Engineering. “We have second- and third-year students getting offered salaries much higher than what I get.” Starting pay for some full-time jobs is above $200,000, she reports. Summer internships, meanwhile, pay anywhere from $6,000 to $10,000 a month. To make these stints memorable, many employers offer perks such as free meals, complimentary gym memberships, and occasionally temporary housing. “Sometimes you read about students getting abused in internships and working like slaves,” Gerritsen says. “We don’t see that.”

The bottom line: McKinsey projects that by 2018 demand for data scientists may be as much as 60 percent greater than the supply.

Before it’s here, it’s on the Bloomberg Terminal. LEARN MORE





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The Business of Data #weekend #business #ideas

#business data

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The Business of Data

More and more organisations are collecting and storing vast amounts of data. Yet for all the excitement generated by the potential of this data to transform business models—turning it directly into cold, hard cash can prove difficult.

Despite the obvious benefits of using superior data to drive value-added marketing strategies, companies are facing many barriers, including regulatory uncertainty, consumer privacy issues, security concerns and budget constraints. So while the possibilities of digital disruption and big data are endless, companies need to think very carefully about how to execute their plans to avoid some common pitfalls.

This EIU report examines how companies are positioning themselves to benefit directly from the wave of opportunities offered by fast-evolving data technologies. It is based on a cross-industry survey of 476 executives based largely in North America, Europe and Asia on their companies’ data plans and practices, as well as insights from the leaders of organisations at the forefront of the emerging data industry.

Read report in English | 日本語





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South Africa stock market data – prices and news #get #a #business #loan

#current stock market prices

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Apply Cancel Comparisons

All markets data located on FT.com is subject to the FT Terms & Conditions

All content on FT.com is for your general information and use only and is not intended to address your particular requirements. In particular, the content does not constitute any form of advice, recommendation, representation, endorsement or arrangement by FT and is not intended to be relied upon by users in making (or refraining from making) any specific investment or other decisions.

Any information that you receive via FT.com is at best delayed intraday data and not “real time”. Share price information may be rounded up/down and therefore not entirely accurate. FT is not responsible for any use of content by you outside its scope as stated in the FT Terms & Conditions .

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Markets Data Center Mobile – Company Data, Indexes, Stock Quotes – More – Wall

#stock market prices

#

Footnotes

Stocks: Real-time U.S. stock quotes reflect trades reported through Nasdaq only; comprehensive quotes and volume reflect trading in all markets and are delayed at least 15 minutes. International stock quotes are delayed as per exchange requirements. Indexes may be real-time or delayed; refer to time stamps on index quote pages for information on any delays. Source: SIX Financial Information

Bonds: Bond quotes are updated in real-time. Source: Tullett Prebon.

Currencies: Currency quotes and charts are updated in real-time. Source: Tullett Prebon.

Commodities & Futures: Futures prices reflect electronic trading and are delayed 10 minutes. Futures quotes show contract month with the highest level of open interest, except crude oil, which always shows the “front month” contract (the contract that will expire soonest). Change value during the period between open outcry settle and the commencement of the next day’s trading is calculated as the difference between the last trade and the prior day’s settle. Change value during other periods is calculated as the difference between the last trade and the most recent settle. Source: SIX Financial Information.

Data are provided ‘as is’ for informational purposes only and is not intended for trading purposes. SIX Financial Information (a) does not make any express or implied warranties of any kind regarding the data, including, without limitation, any warranty of merchantability or fitness for a particular purpose or use; and (b) shall not be liable for any errors, incompleteness, interruption or delay, action taken in reliance on any data, or for any damages resulting therefrom. Data may be intentionally delayed pursuant to supplier requirements.

Sections





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Data Scientists in Demand: Salaries Rise as Talent Shortage Looms #small #business #loan

#business week

#

Help Wanted: Black Belts in Data

A new species of techie is in demand these days—not only in Silicon Valley, but also in company headquarters around the world. “Data scientists are the new superheroes,” says Pascal Clement, the head of Amadeus Travel Intelligence in Madrid. The description isn’t exactly hyperbolic: The qualifications for the job include the strength to tunnel through mountains of information and the vision to discern patterns where others see none. Clement’s outfit is part of Amadeus IT Holding, the world’s largest manager of flight bookings for airlines, which has more than 40 data scientists on its payroll, including some with a background in astrophysics. The company recently launched Schedule Recovery, a product that tracks delays and automatically rebooks all affected passengers.

A study by McKinsey projects that “by 2018, the U.S. alone may face a 50 percent to 60 percent gap between supply and requisite demand of deep analytic talent.” The shortage is already being felt across a broad spectrum of industries, including aerospace, insurance, pharmaceuticals, and finance. When the consulting firm Accenture surveyed its clients on their big-data strategies in April 2014, more than 90 percent said they planned to hire more employees with expertise in data science—most within a year. However, 41 percent of the more than 1,000 respondents cited a lack of talent as a chief obstacle. “It will get worse before it gets better,” says Narendra Mulani, senior managing director at Accenture Analytics.

Many data scientists have Ph.D.s or postdoctorates and a background in academic research, says Marco Bressan, president for data and analytics at BBVA, a Spanish bank that operates in 31 countries and has a team of more than 20 data scientists. “We have nanotechnologists, physicists, mathematicians, specialists in robotics,” he says. “It’s people who can explore large volumes of data that aren’t structured.”

So-called unstructured data can include e-mails, videos, photos, social media, and other user-generated content. Data scientists write algorithms to extract insights from these troves of information. But “true data scientists are rare,” says Ricard Benjamins, head of business intelligence and big data at Telefónica, Europe’s second-largest phone company, which employs more than 200 of them. Says Stan Humphries, chief economist at Zillow, the real estate listings site: “You can find a great developer and a great researcher who has a background in statistics, and maybe you can find a great problem solver, but to find that in the same person is hard.”

Universities are taking note. MIT, where graduate students in physics, astronomy, and biology are fielding offers from outside their chosen fields, is in the process of setting up a dedicated data-science institute. Marilyn Wilson, the university’s associate director for career development, says the center will begin enrolling graduate degree candidates in 2016.

In the U.K. the University of Warwick introduced a three-year undergraduate data-science program last year, which David Firth, the program’s mastermind, says may well be the first of its kind. “Big Business was complaining about the lack of people,” he says. “Finance is a major employer, but also large-scale insurers, large online commercial retailers, high-tech startups, and government, which has huge data sets.”

Accenture’s Mulani says he’s tallied some 30 new data-science programs in North America, either up and running or in the works. The University of Virginia began offering a master’s in 2014, as did Stanford. Many of those students may be tempted to drop out before collecting their degree. “Companies are scrambling,” says Margot Gerritsen, director of Stanford’s Institute for Computational Mathematical Engineering. “We have second- and third-year students getting offered salaries much higher than what I get.” Starting pay for some full-time jobs is above $200,000, she reports. Summer internships, meanwhile, pay anywhere from $6,000 to $10,000 a month. To make these stints memorable, many employers offer perks such as free meals, complimentary gym memberships, and occasionally temporary housing. “Sometimes you read about students getting abused in internships and working like slaves,” Gerritsen says. “We don’t see that.”

The bottom line: McKinsey projects that by 2018 demand for data scientists may be as much as 60 percent greater than the supply.

Before it’s here, it’s on the Bloomberg Terminal. LEARN MORE





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Broker-dealer Data Center #stock #markets

#investment news

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Broker-dealer Data Center

Purchase the complete set of our independent broker-dealer data and profiles in an Excel spreadsheet.

For comments or suggestions about the BD Data Center, please contact us.

Disclaimer: All data and information is the property of InvestmentNews and is protected by copyright and other intellectual property laws. All rights are reserved by InvestmentNews. The data may only be used for internal business use such as to develop a mailing list but the data may not be resold, republished, redistributed, sublicensed or publicly displayed on a web site without the permission of InvestmentNews. All information contained within was obtained through InvestmentNews’ annual independent B-D surveys.

News About Broker-dealers



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SAS Business Data Network #carpet #cleaning #business

#business data

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SAS Products

SAS Business Data Network provides a business data term list, which is an authoritative vocabulary that promotes a common understanding between stakeholders in an organization.

The most recent release is SAS Lineage 3.1.

News

SAS Business Data Network 3.1 Now Available

SAS Business Data Network 3.1 is an application that enables you to manage a business data term list. It supports a collaborative approach to managing the following information:

  • Descriptions of business terms, including their requirements and attributes
  • Related source data and reference data
  • Contacts (such as technical owners, business owners, and interested parties)
  • Relationships between terms and processes (such as Data Management Studio jobs, services, and business rules)

By linking terms to business rules and data monitoring processes, SAS Business Data Network provides a single entry point for all data consumers to better understand their data. Data stewards, IT staff and enterprise architects can use the terms to promote a common vocabulary across projects and business units. Permissions can be set to allow only specific users to access and/or control the data in SAS Business Data Network.

SAS Business Data Network also provides workflow support that enables you to divide the responsibilities for creating, reviewing, and approving terms among the members of your team. In this way, each role can be fulfilled by the most qualified member of the group.

Free Online Documentation

  • Most recent release for SAS Business Data Network

SAS Business Data Network 3.1

  • SAS Business Data Network 3.1: User’s Guide PDF | HTML
  • The User’s Guide is accessible within the product.
  • All online documentation for supported releases of SAS Business Data Network [HTML]
  • Technical Papers

      SAS Global Forum 2014
    • Managing the Data Governance Lifecyle [PDF]
    • What’s New in SAS Data Management [PDF]

    SAS Publishing Representatives are available in the U.S. from 8-5 ET to answer your documentation questions. Contact us at 1-800-727-3228 or e-mail.

    Training

    Curriculum consultants are available in the U.S. from 9-5 EST. Contact us at 1-800-333-7660 or e-mail.

    International customers, please contact your country office.

    Online Support Resources

    This page contains online support resources that are specific to this product. Visit the Support page to access various self-help and assisted-help resources or submit a problem through the SAS Technical Support form.

    Data Management Community

    Share your experiences, questions and ideas with other SAS Business Data Network users.





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