Alternative Data News. 09, September 2020
Finding sources and uses for alternative data can be difficult. At CloudQuant we regularly read and search the internet for new sources of data that can be used in our mission to find alpha signals and build quantitative trading strategies. We recognize that we are technology and data junkies so we wrote our own crawler that specifically seeks out web pages, posts, and news articles that give us a snapshot of what is going on in the world of Alt Data. The following is a collection of articles that we think you will find interesting from the past week.
CloudQuant Nominated for Benzinga Fintech Award!
Those of you who already know us know that we deliver possibly the best unified alternative data research archive in the world, a temporal dataset technology which works seamlessly with our industry-leading applications and our customers’ private tools.
CloudQuant has opened the world of data analysis through sharing research tools, advanced analysis, white papers, data, and source code. We have overcome the Cambrian explosion of alternative data through finely tuned data onboarding and temporal APIs. Clients rapidly move from ideas to value. Unique datasets, trading algorithms, stock market backtesting, and support help investment managers launch new funds and new investment algorithms all while maintaining proper privacy and security.
We are proud to announce that our industry leading technology has been nominated for a Benzinga Fintech Award 2020 in the category of Best Data Analysis Tool.
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NSF $1billion for 12 New AI Institutes!
Feds Investing $1B to Fund 12 New AI Institutes
The federal government is increasing its investment in AI research, with the announcement on August 26 of over $1 billion of awards to establish 12 new AI and quantum information science (QIS) research institutes nationwide.
The announcement was from the White House Office of Science and Technology Policy, the National Science Foundation (NSF) and the US Department of Energy (DOE), in a release issued from the Brookhaven National Laboratory of Upton, N.Y.
The $1 billion will go toward NSF-led AI Research Institutes and DOE QIS Research Centers of five years, establishing 12 multi-disciplinary and multi-institutional national hubs for research and workforce development. The goals are to spur innovation, support regional economic growth and advance American leadership in strategic industries.
2020-09-03 19:53:58+00:00 Read the full story…
Weighted Interest Score: 4.0495, Raw Interest Score: 1.6336,
Positive Sentiment: 0.2193, Negative Sentiment 0.1973
CloudQuant Thoughts : We covered this on our AI and Machine Learning post earlier this week but we now have more info and the following 12 AI hubs + The Quantum Center Formation will be created…
- AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography, University of Oklahoma.
- AI Institute for Foundations of Machine Learning, University of Texas.
- AI Institute for Student-AI Teaming, University of Colorado, Boulder.
- AI Institute for Molecular Discovery, Synthetic Strategy, and Manufacturing (the NSF Molecule Maker Lab), University of Illinois at Urbana-Champaign.
- AI Institute for Artificial Intelligence and Fundamental Interactions, Massachusetts Institute of Technology.
- AI Institute for Next Generation Food Systems, University of California, Davis.
- AI Institute for Future Agricultural Resilience, Management and Sustainability, University of Illinois at Urbana-Champaign.
- Next Generation Quantum Science and Engineering Center (Q-NEXT), Argonne National Laboratory.
- Co-design Center for Quantum Advantage (C²QA), Brookhaven National Laboratory.
- Superconducting Quantum Materials and Systems Center (SQMS), Fermi National Accelerator Laboratory.
- Quantum Systems Accelerator Center (QSA), Lawrence Berkeley National Laboratory.
- Quantum Science Center (QSC), Oak Ridge National Laboratory.
- Quantum Center Formation, Includes University of Chicago, Harvard, Cornell, IBM, Intel, Lockheed Martin, and Microsoft.
Who’s Talking in Popular Films: Dialogue Breakdown by Gender
By reddit user : BoMcCready
Sources: IMDb and The Pudding
Interactive version here : You can change the vote threshold, mouse over films on the scatterplot for more detail, and highlight specific films.
CloudQuant Thoughts : Our now, almost obligatory Reddit Data Is Beautiful post. This one demonstrates the shocking disparity between Male and Female roles in Hollywood blockbusters. There are so many ways we can, using our data science skill sets, highlight where society needs to change. When you look at how many Women actually make movies, direct and produce, the disparity is even worse 96% directed by men 4% directed by women, despite Film school graduates being very balanced around 50/50. There is a whole section of society whose voice is effectively silenced. If you want to look into this more there are a number of TED talks (here and here) and organizations which encourage women to make movies.
Backed by $12.5M in federal funding, Univ. of Washington leads new data science institute
With $12.5 million in federal funding, the University of Washington will lead a cohort of institutions tackling foundational challenges in the field of data science.
The UW is teaming up with interdisciplinary researchers from University Wisconsin-Madison, University California-Santa Cruz and University of Chicago to form the Institute for Foundations of Data Science (IFDS). The effort will be led by Maryam Fazel, a UW electrical and computer engineering professor.
The institute marks the culmination of three years of work supported by the National Science…
2020-09-01 23:59:00+00:00 Read the full story…
Weighted Interest Score: 3.3509, Raw Interest Score: 1.5326,
Positive Sentiment: 0.2395, Negative Sentiment 0.2874
CloudQuant Thoughts : Yet more governent investment in Data Science!
Google is releasing data on how people have been searching for COVID-19 symptoms, in the hope it will help researchers track the virus
Google is releasing a database of US search trends for COVID-19 symptoms, hoping it will help public health authorities and researchers track how the virus is spreading.
Google built its dataset with user searches for more than 400 symptoms such as coughing, fever, and difficulty breathing. The aggregated data, which Google says is anonymized, shows trends in the volume of symptom-related searches at the US county level.
But Google says it isn’t revealing the raw number of specific searches, and will instead normalize the search terms on a scale of 1 to 100, similar to how its Google Trends tool works, so researchers can identify spikes in search trends.
2020-09-02 00:00:00 Read the full story…
Weighted Interest Score: 3.6641, Raw Interest Score: 1.7834,
Positive Sentiment: 0.0000, Negative Sentiment 0.0973
CloudQuant Thoughts : This is interesting data but I cant help but think it is a promotion for their Dataset Search Engine.
Covid-19 forced Google to change how it predicts traffic in Google Maps
For the past 13 years, when you started a route in Google Maps, Google provided an estimated time of arrival based on years of data and intelligence from DeepMind. Google found global traffic dropped 50% after lock-downs started earlier this year. Google said it had to deprioritize older traffic data and has changed its models to first prioritize traffic patterns from the last 2-4 weeks.
Google on Thursday explained in a blog post how Covid-19 has forced it to rethink how it predicts driving conditions, like traffic, for people who use Google Maps.
For the past 13 years, when you started a route in Google Maps, Google provided an estimated time of arrival based on years a combination of live traffic data and historical traffic patterns that were accurate for over 97% of trips worldwide. Now Google is partnering with DeepMind to make their ETAs even more accurate.
Pre-coronavirus, your morning commute may have been about an hour, taking into condition years of information that Google knew about weather, potential accidents and roads along your route. But, Google found global traffic dropped 50% after lockdowns started earlier this year, so that method doesn’t work anymore.
2020-09-03 00:00:00 Read the full story…
Weighted Interest Score: 2.0264, Raw Interest Score: 1.4097,
Positive Sentiment: 0.0000, Negative Sentiment 0.2643
CloudQuant Thoughts : We should all be thinking about how we are going to handle the effects of Covid on our datasets. Hopefully we will move past this pandemic soon and will have to be very selective as to which data we can use and which we cannot. #TheNewNormal!
What’s the difference between a Data Scientist and a Quant?
When I started working in finance, nearly two decades ago, the trading floor was full of ‘Quants’. Usually with Phds, these erudite employees were extremely clever but for some reason somewhat undervalued and underpaid. Since then things have changed, and if you come across a clever person with a Phd in an investment bank or hedge fund it’s most likely their job title is ‘Data Scientist’, though you will still find a few Quants. Is there any difference, or are these just fancy job titles for what is essentially the same job?
Let’s start by defining what a Quant does. When I started working on the buy-side, most Quants working in investment banking were concerned with pricing or risk management. They were usually from maths or physics backgrounds, and their core knowledge was an understanding of theoretical asset pricing models.
2020-09-04 06:22:00-06:00 Read the full story…
Weighted Interest Score: 7.3038, Raw Interest Score: 2.8762,
Positive Sentiment: 0.1251, Negative Sentiment 0.0625
Enterprise Data Literacy: Understanding Data Management
To truly understand data-as-an-asset requires Enterprise Data Literacy, an organizational capability to take, analyze, and use data to remain secure and competitive. But achieving a high Enterprise Data Literacy can remain daunting when business and IT interact together.
All too often in the middle of a project sprint, IT gets stuck on a minor problem, such as new customers only being able to see their monthly invoice in landscape view. IT imple…
2020-09-08 07:35:18+00:00 Read the full story…
Weighted Interest Score: 3.1059, Raw Interest Score: 1.6815,
Positive Sentiment: 0.1071, Negative Sentiment 0.2035
5 Step Process For Insightful Data Driven Business Decision Making
Big data is becoming increasingly important in business decision-making. The market for data analytics applications and solutions is expected to reach $105 billion by 2027.
However, big data technology is only a viable tool for business decision-making if it is utilized appropriately. Google has shown how to use big data effectively for decision-making, but many other companies don’t understand the principles to follow. Far too many businesses f…
2020-09-04 16:19:49+00:00 Read the full story…
Weighted Interest Score: 2.9201, Raw Interest Score: 1.4869,
Positive Sentiment: 0.3404, Negative Sentiment 0.1612
Webinar on “Data Engineering : Careers and Skills”
For every Data Scientist a company hires, they in turn need to hire an average 5 Data Engineers. This has led to a huge increase in the demand for data engineers – in India as well as globally. What makes a career in Data Engineering even more relevant is the fact that the demand is expected to grow even during the economic slowdown.
In this webinar, a panel of experts from LatentView, Genpact and Praxis Business School will talk to you on:
2020-09-08 03:09:23+00:00 Read the full story…
Weighted Interest Score: 2.5601, Raw Interest Score: 1.5171,
Positive Sentiment: 0.0948, Negative Sentiment 0.1264
Cybersecurity Startups Will Witness More Opportunities In The Post-COVID World
While businesses have been deeply affected by the ongoing pandemic, the startup community is not immune. In fact, many startups have faced visible declines in profits and structural transformations due to COVID. It has been a learning lesson for entrepreneurs to make startups thrive despite the challenges. Has the effect on deep learning and AI startups been also the same?
Analytics India Magazine got in touch with Sudhanshu Mittal, Head – CoE G…
2020-09-08 08:30:33+00:00 Read the full story…
Weighted Interest Score: 2.4256, Raw Interest Score: 1.4907,
Positive Sentiment: 0.4082, Negative Sentiment 0.2662
Data Visualization in R with ggplot2: A Beginner Tutorial
A famous general is thought to have said, “A good sketch is better than a long speech.” That advice may have come from the battlefield, but it’s applicable in lots of other areas — including data science. “Sketching” out our data by visualizing it using ggplot2 in R is more impactful than simply describing the trends we find.
This is why we visualize data. We visualize data because it’s easier to learn from something that we can see rather than read. And thankfully for data analysts and data scientists who use R, there’s a tidyverse package called ggplot2 that makes data visualization a snap! In…
2020-09-02 14:39:03+00:00 Read the full story…
Weighted Interest Score: 2.2871, Raw Interest Score: 1.1744,
Positive Sentiment: 0.1360, Negative Sentiment 0.0371
Twitter begins adding headlines and descriptions to some of its ‘trends’ – TechCrunch
Twitter is working to make its real-time Trending section less confusing. Last week, the company announced it would begin pinning to the trend’s page a representative tweet that gives more insight about a trend and promised more changes would soon be underway. Today, the company says it will begin writing headlines and descriptions for some of the trends, too, so you’ll better understand why something is showing up in the Explore tab or when you …
2020-09-08 00:00:00 Read the full story…
Weighted Interest Score: 2.0272, Raw Interest Score: 1.0812,
Positive Sentiment: 0.1179, Negative Sentiment 0.2162
Tackling one of the biggest single sources of CO2 emissions with machine learning
Presented by AWS Machine Learning
In the United States alone, there are close to 6 million buildings, nearly one for every 60 Americans. Together, they produce 40% of the country’s total emissions, most of which comes from day-to-day lighting, heating, cooling, and appliance operation — making it one of the largest single polluting factors in the U.S.
“What that means is that it’s a massive prize, both economically and environmentally, that’s w…
2020-09-08 00:00:00 Read the full story…
Weighted Interest Score: 2.0157, Raw Interest Score: 1.2319,
Positive Sentiment: 0.1971, Negative Sentiment 0.1807
3 Reasons To Use Data Analytics To Pursue Long Tail Keywords
Data analytics is becoming a critical component of modern SEO. We have previously identified the benefits of big data in SEO strategies. However, we thought it was time to talk about a more specific application of data analytics in SEO.
Data analytics can be extremely useful for finding long-tail keywords for search engine marketing. Whether you intend to use data analytics for paid or organic search marketing…
2020-09-01 23:58:00+00:00 Read the full story…
Weighted Interest Score: 1.9248, Raw Interest Score: 1.2389,
Positive Sentiment: 0.4646, Negative Sentiment 0.1106
Data Driven Insights For A Holistic Digital And Print Marketing Campaign
ing big data in marketing. You shouldn’t limit yourself to using data analytics in your SEO strategy. You should find ways to use big data to merge your digital and offline marketing strategies.
How Data Driven Marketing Should Be Adapted to Both Digital and Offline Approaches
The internet offers many benefits to the modern business, but among the most fundamental is its ability to spread a message. Through social media, we can get in touch with our would-be customers, start a dialogue, and thereby become visible on countless devices. Big data developments have heightened these benefits.
By providing …
2020-09-01 00:25:00+00:00 Read the full story…
Weighted Interest Score: 1.8742, Raw Interest Score: 1.0040,
Positive Sentiment: 0.1562, Negative Sentiment 0.0223
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