The document discusses economic insights from LinkedIn's professional network as presented by Dr. June Andrews on February 20, 2015. It examines industry migration patterns, growth in C-suite positions, and the likelihood of knowing future colleagues based on the size and strength of one's professional network. Specifically, it finds that construction workers frequently migrate between real estate, oil/mining industries. It also finds that the ratio of C-suite executives to company employees peaks at around 3,000 employees, and investing in one's network up to 45 connections maximizes the chance of knowing future work colleagues.
Saturday economists charts of the week, inflation and jobsJohn Ashcroft
The Saturday Economist, Charts of the Week on inflation and employment. We review the latest data on retail and manufacturing prices, jobs, vacancies and claimant count and update our forecasts. JKA
Saturday economists charts of the week, inflation and jobsJohn Ashcroft
The Saturday Economist, Charts of the Week on inflation and employment. We review the latest data on retail and manufacturing prices, jobs, vacancies and claimant count and update our forecasts. JKA
5 Tips Mendapatkan Beasiswa Keluar Negeri
Untuk info lebih lanjut mengenai kuliah diluar negeri, bisa kunjungi www.adinnyparamita.com tentang share kuliah diluar negeri.
Presentation given by Brookings' Marek Gootman at a workshop between U.S. and Australian leaders entitled "Building and Sustaining Globally Competitive Regions."
Charts from a presentation I made to the National Association of Pipe Fabricators (NAPF) Annual Meeting Feb 28, 2014 on the outlook for construction with an emphasis on residential and water & sewer
State Online College Job Market: Ranking the StatesCEW Georgetown
Groundbreaking report uses online job ads to analyze the state college labor markets.Massachusetts, Delaware, and Washington State provide college graduates with the best odds of landing a job, according to a new report by the Georgetown University Center on Education and the Workforce.
Every so often, the conundrum of the Uncanny Valley re-emerges as advanced technologies evolve from clearly experimental products to refined accepted technologies. We have seen its effects in robotics, computer graphics, and page load times. The debate of how to handle the new technology detracts from its benefits. When machine learning is added to human decision systems a similar effect can be measured in increased response time and decreased accuracy. These systems include radiology, judicial assignments, bus schedules, housing prices, power grids and a growing variety of applications. Unfortunately, the Uncanny Valley of ML can be hard to detect in these systems and can lead to degraded system performance when ML is introduced, at great expense. Here, we’ll introduce key design principles for introducing ML into human decision systems to navigate around the Uncanny Valley and avoid its pitfalls.
Critical turbine maintenance: Monitoring and diagnosing planes and power plan...June Andrews
GE produces a third of the world’s power and 60% of its airplane engines—a critical portion of the world’s infrastructure that requires meticulous monitoring of the hundreds of sensors streaming data from each turbine.
The infrastructure and teams involved with processing this data have evolved over the course of decades, incorporating expert knowledge on how sensor variations act as leading indicators for maintenance issues, compliance with regulations and customer agreements, and innovative digital twin models to identify potential issues. These maintenance systems have performed spectacularly, minimizing down time and identifying critical issues early. They have also collected years of hand-labeled data connecting sensor output with downstream impact such as hazardous gas leaks and melting fan blades. With sensor technology steadily increasing the amount of data streaming from these engines, making them harder to analyze, and failure modes becoming increasingly nuanced, GE has embarked on the next chapter of innovation in maintenance, incorporating machine learning.
June Andrews and John Rutherford explain how GE’s monitoring and diagnostics teams released the first real-time ML systems used to determine turbine health into production.
Topics include:
- Understanding new and complex domains
- Navigating ML product design to maximize the probability of a successful release
- Focusing on scalable ML implementations so development efforts in one domain can benefit another
- Safely releasing and support models in critical environments distributed across continents
- How to make critical data products “walk-away safe”
- How to incorporate innovations into a platform for additional turbine applications
5 Tips Mendapatkan Beasiswa Keluar Negeri
Untuk info lebih lanjut mengenai kuliah diluar negeri, bisa kunjungi www.adinnyparamita.com tentang share kuliah diluar negeri.
Presentation given by Brookings' Marek Gootman at a workshop between U.S. and Australian leaders entitled "Building and Sustaining Globally Competitive Regions."
Charts from a presentation I made to the National Association of Pipe Fabricators (NAPF) Annual Meeting Feb 28, 2014 on the outlook for construction with an emphasis on residential and water & sewer
State Online College Job Market: Ranking the StatesCEW Georgetown
Groundbreaking report uses online job ads to analyze the state college labor markets.Massachusetts, Delaware, and Washington State provide college graduates with the best odds of landing a job, according to a new report by the Georgetown University Center on Education and the Workforce.
Every so often, the conundrum of the Uncanny Valley re-emerges as advanced technologies evolve from clearly experimental products to refined accepted technologies. We have seen its effects in robotics, computer graphics, and page load times. The debate of how to handle the new technology detracts from its benefits. When machine learning is added to human decision systems a similar effect can be measured in increased response time and decreased accuracy. These systems include radiology, judicial assignments, bus schedules, housing prices, power grids and a growing variety of applications. Unfortunately, the Uncanny Valley of ML can be hard to detect in these systems and can lead to degraded system performance when ML is introduced, at great expense. Here, we’ll introduce key design principles for introducing ML into human decision systems to navigate around the Uncanny Valley and avoid its pitfalls.
Critical turbine maintenance: Monitoring and diagnosing planes and power plan...June Andrews
GE produces a third of the world’s power and 60% of its airplane engines—a critical portion of the world’s infrastructure that requires meticulous monitoring of the hundreds of sensors streaming data from each turbine.
The infrastructure and teams involved with processing this data have evolved over the course of decades, incorporating expert knowledge on how sensor variations act as leading indicators for maintenance issues, compliance with regulations and customer agreements, and innovative digital twin models to identify potential issues. These maintenance systems have performed spectacularly, minimizing down time and identifying critical issues early. They have also collected years of hand-labeled data connecting sensor output with downstream impact such as hazardous gas leaks and melting fan blades. With sensor technology steadily increasing the amount of data streaming from these engines, making them harder to analyze, and failure modes becoming increasingly nuanced, GE has embarked on the next chapter of innovation in maintenance, incorporating machine learning.
June Andrews and John Rutherford explain how GE’s monitoring and diagnostics teams released the first real-time ML systems used to determine turbine health into production.
Topics include:
- Understanding new and complex domains
- Navigating ML product design to maximize the probability of a successful release
- Focusing on scalable ML implementations so development efforts in one domain can benefit another
- Safely releasing and support models in critical environments distributed across continents
- How to make critical data products “walk-away safe”
- How to incorporate innovations into a platform for additional turbine applications
Companies have adopted data into their DNA using a variety of methods, including data driven, data enabled, and data informed, but many implementations have fallen short of the promised ROI, the result of a gap between the cost of investing in people and infrastructure and the business value delivered.
June Andrews takes a structured look at how to strategically invest in data to maximize the benefit gained from incorporating data, highlighting situations when investment in experimental platforms doesn’t make sense and others when building a custom analysis platform does. Along the way, June explains how best practices have evolved over time. The result is to grow the mindset from creating data-driven organizations to creating data-competitive organizations that can adapt and deliver in the rapidly changing landscape between data science, machine learning, and artificial intelligence.
ML Products have become a prolific and integral part of taking the insights of Data Science from theory to reality. Oddly though, the path from conception to implementation is often unclear with seemingly few similar examples to work from. The result is often a sea of agony between sliding deadlines, heroic efforts of people working though unforeseen challenges and haphazard innovation. Each time a beautiful model makes its impact on the business bottom line, something worked. In this talk we present the ML Playbook. It pulls together the best aspects from a variety of successful ML Product launches into a cohesive strategy to Plan, Build, Test, Learn, and Release ML Products. We'll demonstrate the ML Playbook in action with the story of launching an alert monitoring product for the world's most powerful jet engines, the GE90-115B.
Counter Intuitive Machine Learning for the Industrial Internet of ThingsJune Andrews
The Industrial Internet of Things (IIoT) is the infrastructure and data flow built around the world’s most valuable things like airplane engines, medical scanners, nuclear power plants, and oil pipelines. These machines and systems require far greater uptime, security, governance, and regulation than the IoT landscape based around consumer activity. In the IIoT the cost of being wrong can be the catastrophic loss of life on a massive scale. Nevertheless, given the growing scale through the digitalization of industrial assets, there is clearly a growing role for machine learning to help augment and automate human decision making. It is against this backdrop that traditional machine learning techniques must be adapted and need based innovations created.
Counter Intuitive Machine Learning for the Industrial Internet of ThingsJune Andrews
The Industrial Internet of Things (IIoT) is the infrastructure and data flow built around the world's really valuable things like airplane engines, medical scanners, nuclear power plants, and oil pipelines. These machines and systems require far greater uptime, security, governance, and regulation than the IoT landscape based around consumer activity. In the IIoT, the cost of being wrong can be as dramatic as the catastrophic loss of life on a massive scale. Nevertheless, given the growing scale powered by the digitalization of industrial assets, there is clearly an increased role for machine learning to help automate and augment human decision making for the IIoT. It is against this backdrop that traditional machine learning techniques must necessarily be adapted and new approaches must be innovated. We see industrial machine learning as distinct from consumer machine learning and in this talk we will cover the counterintuitive changes of featurization, metrics for model performance, and human-in-the-loop design changes for using machine learning in an industrial environment.
An experiment at Pinterest revealed somewhat shocking results. When nine data scientists and ML engineers were asked the same constrained question, they gave nine spectacularly different answers. The implications for business are astronomical. June Andrews and Frances Haugen explore the aspects of analysis that cause differences in conclusions and offer some solutions.
Replication in Data Science - A Dance Between Data Science & Machine Learning...June Andrews
We use Iterative Supervised Clustering as a simple building block for exploring Pinterest's Content. But simplicity can unlock great power and with this building block we show the shocking result of how hard it is to replicated data science conclusions. This begs us to challenge the future for When is Data Science a House of Cards?
Meetup talk for Big Data Applications in Fashion:
Pinterest has +100M monthly active users augmenting our catalog of over 75 billion ideas worldwide. With this data we can see how consumer patterns emerge, grow, and evolve. The Pinterest Blog covers highlights of the latest trends including how midi skirts and culottes are popping up this spring. Here we'll look under the hood at how to detect trends amid rapid data growth and take an in-depth look at a what Pinterest data shows on fashion trends.
http://www.meetup.com/Fashion-big-data-Meetup/events/229944959/
Growth, Engagement & Search Metrics: Snake Oil or North StarsJune Andrews
Talk at Social Media & Web Analytics
LinkedIn's homepage contains content from over 40 product areas and has evolved over hundreds of experiments. For modern websites this is not an unusual phenomena. To parallelize website development and work in harmony, product teams rely on two guidance systems, organizational cohesion and analytical feedback. Our focus is analytics and in particular, metrics. Unfortunately, not all metrics are created equal. Common metrics such as mean average precision and engagement stickiness have massive downsides if used incorrectly. Here we explore criteria to align optimizing metrics with improving user experience and reaching company goals.
3. Explore Economic Insights
1 Industry Migration
2 C-Suite Growth
3 Future Colleagues
June Andrews Economic Insights February 20, 2015 3 / 30
4. Significant Relations with Realtors
Figure : Period of dramatic growth for real estate
June Andrews Economic Insights February 20, 2015 4 / 30
5. Significant Relations with Realtors
Figure : Period of economic change
June Andrews Economic Insights February 20, 2015 5 / 30
6. Significant Relations with Construction
Figure : Symmetric relationship between real estate and construction.
Construction workers migrate between real estate and oil and mining.
June Andrews Economic Insights February 20, 2015 6 / 30
7. Industry Migration - Mechanics
Figure : Construction workers connecting with Oil & Mining over Real Estate
June Andrews Economic Insights February 20, 2015 7 / 30
8. Industry Migration - Mechanics
How?
Is migration prompted by influential people?
Is migration independent pockets of movement?
June Andrews Economic Insights February 20, 2015 8 / 30
9. Industry Migration - Cascades
Figure : Median is 4 neighbors migrated before conversion
June Andrews Economic Insights February 20, 2015 9 / 30
10. Industry Migration - Mechanics
Figure : Size of bubble is proportional to size of complete cascade.
How?
Migration is largely independent, with some cascades.
June Andrews Economic Insights February 20, 2015 10 / 30
11. Explore Economic Insights
1 Industry Migration
2 C-Suite Growth
3 Future Colleagues
June Andrews Economic Insights February 20, 2015 11 / 30
12. C-Suite Growth
Figure : C-Suite size to company size. Two distinct periods of growth.
June Andrews Economic Insights February 20, 2015 12 / 30
13. C-Suite Growth
Figure : C-Suite approximate efficiency. Maximized at ≈ 3k employees.
June Andrews Economic Insights February 20, 2015 13 / 30
14. C-Suite Growth
Figure : C-Suite approximate efficiency. Maximized at ≈ 3k employees.
June Andrews Economic Insights February 20, 2015 14 / 30
15. C-Suite Growth
Implications
Regardless of management philosophy, ratio of CXO’s to
employees reaches a maximum
Coincidentally, Stanford’s McCaw Hall’s capacity is 942 people.
June Andrews Economic Insights February 20, 2015 15 / 30
16. C-Suite Growth Order of Growth
June Andrews Economic Insights February 20, 2015 16 / 30
17. C-Suite Growth Order of Growth
June Andrews Economic Insights February 20, 2015 16 / 30
18. C-Suite Growth Order of Growth
June Andrews Economic Insights February 20, 2015 16 / 30
19. C-Suite Growth Order of Growth
June Andrews Economic Insights February 20, 2015 16 / 30
20. C-Suite Growth Order of Growth
June Andrews Economic Insights February 20, 2015 16 / 30
21. C-Suite Growth Order of Growth
June Andrews Economic Insights February 20, 2015 16 / 30
22. C-Suite Growth Order of Growth
June Andrews Economic Insights February 20, 2015 16 / 30
23. C-Suite Growth Order of Growth
June Andrews Economic Insights February 20, 2015 16 / 30
24. C-Suite Growth Order of Growth
June Andrews Economic Insights February 20, 2015 16 / 30
25. C-Suite Growth Order of Growth
June Andrews Economic Insights February 20, 2015 16 / 30
26. C-Suite Growth Order of Growth 2nd
C-Suite
June Andrews Economic Insights February 20, 2015 17 / 30
27. C-Suite Growth Order of Growth 2nd
C-Suite
June Andrews Economic Insights February 20, 2015 17 / 30
28. C-Suite Growth Order of Growth 2nd
C-Suite
June Andrews Economic Insights February 20, 2015 17 / 30
29. C-Suite Growth Order of Growth 2nd
C-Suite
June Andrews Economic Insights February 20, 2015 17 / 30
30. C-Suite Growth Order of Growth 2nd
C-Suite
June Andrews Economic Insights February 20, 2015 17 / 30
31. C-Suite Gender Ratio
Figure : Women’s positions in C-Suites grow faster than the C-Suite.
Outliers are caused by Armed Security Organizations.
June Andrews Economic Insights February 20, 2015 18 / 30
32. CEO Gender Ratio
Figure : Growth trend for Women in the C-Suite holds for CEOs.
Outliers are caused by Armed Security Companies.
June Andrews Economic Insights February 20, 2015 19 / 30
33. CEO Gender Ratio
Figure : Overlay of CEO ratio with C-Suite ratio
June Andrews Economic Insights February 20, 2015 20 / 30
34. C-Suite Endorsements
Figure : Complete picture of endorsements in the c-suite
June Andrews Economic Insights February 20, 2015 21 / 30
35. C-Suite Endorsements
Figure : In small companies women in the c-suite are highly endorsed
June Andrews Economic Insights February 20, 2015 22 / 30
36. C-Suite Endorsements
Figure : Improve by comparison to who?
June Andrews Economic Insights February 20, 2015 23 / 30
37. Explore Economic Insights
1 Industry Migration
2 C-Suite Growth
3 Future Colleagues
June Andrews Economic Insights February 20, 2015 24 / 30
38. Prior Research Weak Tie Roles
How to Get a Job [Granovetter]
A quarter of people receive jobs through their network.
Four Degrees of Separation [Backstrom et al]
Weak Ties are critical to traversal of online social networks
June Andrews Economic Insights February 20, 2015 25 / 30
39. Probability Know Future Colleagues
Figure : The power of your network is maximized at ≈ 45
June Andrews Economic Insights February 20, 2015 26 / 30
40. Probability Know Future Colleagues
Figure : Increasing your network size, increases your network power.
Dunbar’s Number is the number of active social relationships.
June Andrews Economic Insights February 20, 2015 27 / 30
41. Weak Tie Roles
Median Age of Connection to Company before Joining
15 Months
Network
Outside studies show referrals result in higher job satisfaction rates.
Recommendation is invest in networking far in advance of job changes.
June Andrews Economic Insights February 20, 2015 28 / 30
42. Thankyou!
Thank you Sara Vera & Mathieu Bastian
juneandrews.com
June Andrews Economic Insights February 20, 2015 29 / 30
43. We’re Hiring
Drive the Data Driven Decision Engine!
June Andrews Economic Insights February 20, 2015 30 / 30