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Articles related to "project"


What we can learn from a decade of dead Google projects

  • There’s a pattern to these shutdowns: Google will announce something cool at its I/O developer conference with a hazy release date, it will ship six to 12 months later, and it’ll last a few years before the company decides it’s not worth the upkeep.
  • For most of the decade, the company was organized into small teams, with more resources available as the team grew, so it’s not that costly to spend a year or two launching something.
  • Google’s graveyard isn’t just full of well-intentioned lab goofs; there are also real, ambitious projects that had a chance and failed, in part because the company was so eager to pull the plug.
  • Daydream VR could have been a true rival to Oculus, but after years of false starts, Google’s buy-in wasn’t enough to get Android manufacturers on board or convince developers that the platform was for real.

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Chevron will absorb a nearly $11 billion writedown this quarter — and it could weigh on the entire stock market

  • Oil giant Chevron's Tuesday announcement that it will write down nearly $11 billion in assets in the fourth quarter could weigh on the entire market.
  • A writedown of that size could reduce overall S&P 500 earnings for the quarter by $1.32 per share, depending on how the company decides to record the writedown, according to Howard Silverblatt of S&P Dow Jones Indices.
  • In the fourth quarter, companies in the S&P 500 are expected to earn $40.40 per share, according to S&P Dow Jones data.
  • For the entire year, it is expected that the S&P 500 will earn $158.50 per share, CNBC reported.
  • Chevron is writing down between $10 billion and $11 billion of assets related to a deepwater Gulf of Mexico project as well as shale gas in Appalachia, both due to low prices of oil and natural gas.

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Sydney's emerging mid-town to get $250m hotel

  • Sydney developers Allen Linz and Warren Duncan will start construction late next year on a $250 million hotel joint-venture at 375  Pitt Street, timing the development to tap the slew of infrastructure and other commercial projects boosting the city's emerging mid-town precinct.
  • Mr Linz's Rebel Property and Mr Duncan's Everest Property are now seeking final approval and a builder, and expect to start construction by November on the Crone-designed four-star hotel with more than 300 rooms in a 35-level tower that will rise out of a recycled brick mixed-use podium.
  • The partners are funding development of the hotel on a podium containing facilities such as gyms, restaurants and co-working space that will tap the growing demand of business travellers in the area sandwiched between Town Hall and Central Station.
  • The mixed-use businesses in the podium will be visible from the hotel lobby and the street level.

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Data Whisperer — an emerging role

  • To begin with, an end in mind is to first understand the business needs, so that the problem solving approach and the project task can be scoped up, and the right data can be pulled out to support the task.
  • Data whisperers understand the iterative nature of the data science workflow, as opposed to the traditional objective-deliverable-milestone project workflow.
  • The primary purpose of the project management role is to manage resources:· time (schedule, work plan)· money (budget and expenditure)· people (to synergise with the right competence and skillsets)· data (analogous to raw materials in manufacturing)· capabilities (tools, infrastructure and data pipeline)· sub-contractors (precious additional resource in critical path)etc, and to lead/drive/motivate the team to get the great work done.
  • Data science involves not only analytical and coding skills but also domain knowledge to understand how to deploy data to solve problems.

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Data Whisperer — an emerging role

  • To begin with, an end in mind is to first understand the business needs, so that the problem solving approach and the project task can be scoped up, and the right data can be pulled out to support the task.
  • Data whisperers understand the iterative nature of the data science workflow, as opposed to the traditional objective-deliverable-milestone project workflow.
  • The primary purpose of the project management role is to manage resources:· time (schedule, work plan)· money (budget and expenditure)· people (to synergise with the right competence and skillsets)· data (analogous to raw materials in manufacturing)· capabilities (tools, infrastructure and data pipeline)· sub-contractors (precious additional resource in critical path)etc, and to lead/drive/motivate the team to get the great work done.
  • Data science involves not only analytical and coding skills but also domain knowledge to understand how to deploy data to solve problems.

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Machine learning 101 & data science: Tips from an industry expert

  • Acquiring as much data as possible is a very important first step in getting started with machine learning systems.
  • In Supervised Learning, the training data provided as input to the algorithm includes the final solutions, called labels or classes because the algorithm learns by “looking” at the examples with correct answers.
  • Another typical task, of a different type, would be to predict a target numeric value like housing prices from a set of features like size, location, number of bedrooms.
  • To train the system, you again need to provide many correct examples of known housing prices, including both their features and their labels.
  • While categorizing emails or identifying whether the picture is of a cat or a dog was a supervised learning algorithm of type classification, predicting housing prices is known as regression.
  • Industry expert and Microsoft Senior AI Engineer, Samia Khalid, has compiled her learnings into a comprehensive course, Grokking Data Science.

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Data Whisperer — an emerging role

  • To begin with, an end in mind is to first understand the business needs, so that the problem solving approach and the project task can be scoped up, and the right data can be pulled out to support the task.
  • Data whisperers understand the iterative nature of the data science workflow, as opposed to the traditional objective-deliverable-milestone project workflow.
  • The primary purpose of the project management role is to manage resources:· time (schedule, work plan)· money (budget and expenditure)· people (to synergise with the right competence and skillsets)· data (analogous to raw materials in manufacturing)· capabilities (tools, infrastructure and data pipeline)· sub-contractors (precious additional resource in critical path)etc, and to lead/drive/motivate the team to get the great work done.
  • Data science involves not only analytical and coding skills but also domain knowledge to understand how to deploy data to solve problems.

save | comments | report | share on


Machine learning 101 & data science: Tips from an industry expert

  • Acquiring as much data as possible is a very important first step in getting started with machine learning systems.
  • In Supervised Learning, the training data provided as input to the algorithm includes the final solutions, called labels or classes because the algorithm learns by “looking” at the examples with correct answers.
  • Another typical task, of a different type, would be to predict a target numeric value like housing prices from a set of features like size, location, number of bedrooms.
  • To train the system, you again need to provide many correct examples of known housing prices, including both their features and their labels.
  • While categorizing emails or identifying whether the picture is of a cat or a dog was a supervised learning algorithm of type classification, predicting housing prices is known as regression.
  • Industry expert and Microsoft Senior AI Engineer, Samia Khalid, has compiled her learnings into a comprehensive course, Grokking Data Science.

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Cousin Greg to play WeWork co-founder Adam Neumann in new series

  • But there’s exciting news even for this early project: Nicholas Braun, best known for standing 6-foot-5 in his role as Cousin Greg on HBO’s Succession, has been cast as WeWork’s Adam Neumann, best known for standing 6-foot-5 while handing out tequila shots after firing employees.
  • Disclosure: I went to high school with Nicholas Braun.
  • Though he was far from the only person who suggested Braun for the role on Twitter, I do want to give credit to The Verge’s Dan Seifert for being the first person on staff to predict it.
  • My own suggestion was Billy Crudup, having seen how good his long, flowing hair looked in The Morning Show (arguably the best part of the series).
  • Disclosure: In case you missed it the first time, I went to high school with Nicholas Braun.

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Machine learning 101 & data science: Tips from an industry expert

  • Acquiring as much data as possible is a very important first step in getting started with machine learning systems.
  • In Supervised Learning, the training data provided as input to the algorithm includes the final solutions, called labels or classes because the algorithm learns by “looking” at the examples with correct answers.
  • Another typical task, of a different type, would be to predict a target numeric value like housing prices from a set of features like size, location, number of bedrooms.
  • To train the system, you again need to provide many correct examples of known housing prices, including both their features and their labels.
  • While categorizing emails or identifying whether the picture is of a cat or a dog was a supervised learning algorithm of type classification, predicting housing prices is known as regression.
  • Industry expert and Microsoft Senior AI Engineer, Samia Khalid, has compiled her learnings into a comprehensive course, Grokking Data Science.

save | comments | report | share on