Category Archives: Data Mining
Are Surgeons More Likely To Kill You On Their Birthday? – American Council on Science and Health
The paper is the work of two physicians trained in internal medicine and a health economist, so they are free of bias as well asany practical knowledge about how surgeons operate, literally and figuratively. They made use of Medicare's dataset of beneficiaries and considered the 30-day outcome for surgery performed on a surgeon's birthday. They found 2064 operations (0.2%) of 980,000 procedures performed by 45,000 surgeons that were performed on their birthdays. With lots of variables to choose from, they compared the results of those 2064 to all the surgery on the other days, 978,000 give or take a few. They limited themselves to 17 procedures, four cardiovascular ones, and 13 of the most common non-cardiovascular procedures among Medicare beneficiaries. The endpoint, how many of these patients, undergoing emergency surgery, died within 30 days of surgery. [1]
They found
"The overall unadjusted 30-day mortality of patients on the surgeon's birthday was 7.0% (145/2064), and that on other days was 5.6% (54,824/978,812)."
To be fair, they tested many variations and adjustments to make sure that they were comparing apples to apples and found a 1.6% increase in mortality for those patients, even with adjustments. For the mathematically inclined, you could say that emergency surgery on your surgeon's birthday was associated with a 29% greater risk of dying or 30 excessive deaths. You should also know that this was over three years, so we are talking about ten excess deaths per year.
Much of the article's red-meat is alluded to in the text but found in the supplements that accompany the article. Let me share some of my favorite findings:
My favorite part, though, was the connect the dots section, labeled discussion. What could have caused this to happen, other than finding a pattern in random data after careful statistical magic? The answer is the distracted surgeon. Included in their list of possible distractions,
These last two possible distractions get at an even bigger problem with the paper, none of the authors know anything about surgical care's practical realities. The heartbreaking problem is that this peer-reviewed paper will now wander in the medical literature and undoubtedly gather citations enforcing the concept of the distracted surgeon, too enamored with their upcoming birthday celebration to pay attention to caring for the life in front of them. It is demeaning. That it would be picked up by the media and further publicized is demoralizing.
[1] Deaths, as a result of surgery, has multiple definitions. They can refer to deaths while hospitalized within the 30-day interval or during the hospitalization irrespective of its length. It can refer to deaths only in the hospital or those at home.
Source: Patient mortality after surgery on the surgeons birthday: observational study BMJ DOI:10.1136/bmj.m4381
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Are Surgeons More Likely To Kill You On Their Birthday? - American Council on Science and Health
The platform reveals more than 550 technical vacancies with salaries ranging from R $ 3,000 to R $ 15,000 – Prudent Press Agency
The occupations involving technology areas are those that have grown the most during the pandemic. Due to digital acceleration, many companies have expanded their IT teams. A survey conducted by GeekHunter, a company that specializes in recruiting software developers and data science, indicates that job offerings brokered by the platform more than tripled throughout 2020. According to a study conducted by BrazilLAB in partnership with Fundao Brava and the Center for Public Impact (CPI), By 2024, it is estimated that more than 300,000 professionals will be needed in the region.
> Learn how to receive news from NSC Total on WhatsApp
> Are you looking for IT jobs in Santa Catarina? stay tuned!
On GeekHunter, December contains 552 job opportunities for developers from all over the country with salaries ranging between 3,000 and 15,000 BRL. The majority of vacancies, around 75%, are for home office work, which increases the chances of hiring candidates from anywhere in Brazil.
There are vacancies for full and advanced professionals, back-end, front-end and full-stack developers, from different programming languages, as well as opportunities for a data scientist and engineer, data analytics and business intelligence analyst. The companies with the largest number of open positions on the platform are Everis, Accenture, FCamara, Zup, IBM, and others.
Interested parties can register the curriculum at GeekHunter, Free and, in some cases, programming tests. Geek reverses the selection process, that is, after a candidate has approved the profile, companies have access to it and can contact him for interviews. However, a professional looking for a new job can show interest in the vacancy and receive recommendations from Geek according to their profile.
> The technology sector in the SC found an opportunity to grow during the pandemic
To an area Data Scientist He has remote job vacancies in PJ format and a salary between R $ 10,000 and R $ 12,000. Must have experience in Python, machine learning, data science, and desirable technologies in BigData, SCRUM, Python, Django, and Data Mining.
Another remote work opportunity is React front end developer Wages ranging from 6,000 R $ to 8,000 R $ under the Commercial Law System. You should have between two to four years of React language experience.
> 2020: A year of challenges and overcoming the tech sector in Santa Catarina
There are also job vacancies for Data Engineer The complete CLT system, in person or remotely, and a bonus ranging from R $ 4.5k to R $ 6.5k. In addition to advanced English for conversation, the candidate needs development experience in Python, Java or Scala programming languages, knowledge of the main tools of the Hadoop ecosystem, such as Spark, Hive, Impala, Kafka, HBase, and familiarity with the Linux environment, Shell Script and PySpark commands, software engineering and design patterns And data modeling.
Opportunity to Full-stack developer To Sao Paulo (SP). The vacancy is personal and the pay ranges between 9,000 Rials and 11,000 Rials under the tax law system. To apply, you must have built-in experience in JavaScript (ES6), React, Redux HTML / CSS, and Node.js to implement a REST API, as well as Git, SQL, and TDD. Practical knowledge in AWS, Firebase, and PostgreSQL services, experience in Agile / Scrum methodology, and knowledge in TypeScript are differences that can highlight the candidate.
> The legal framework for startups is essential for economic recovery
Already for Node.js backend developer There is an opportunity to work personally in So Paulo (SP), on a CLT system and a salary between R $ 9,000 and R $ 11,000. The company is looking for a high-level professional with extensive background development experience in Node.js, as well as cloud knowledge, with AWS and Elasticsearch.
> Public Tenders at SC: See December vacancies, salaries and how to apply
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Quantum Computing And Investing – ValueWalk
At a conference on quantum computing and finance on December 10, 2020, William Zeng, head of quantum research at Goldman Sachs, told the audience that quantum computing could have a revolutionary impact on the bank, and on finance more broadly. In a similar vein, Marco Pistoia of JP Morgan stated that new quantum machines will boost profits by speeding up asset pricing models and digging up better-performing portfolios. While there is little dispute that quantum computing has great potential to perform certain mathematical calculations much more quickly, whether it can revolutionize investing by so doing is an altogether different matter.
Get the entire 10-part series on Seth Klarman in PDF. Save it to your desktop, read it on your tablet, or email to your colleagues.
Q3 2020 hedge fund letters, conferences and more
The hope is that the immense power of quantum computers will allow investment managers earn superior investment returns by uncovering patterns in prices and financial data that can be exploited. The dark side is that quantum computers will open the door to finding patterns that either do not actually exist, or if they did exist at one time, no longer do. In more technical terms, quantum computing may allow for a new level of unwarranted data mining and lead to further confusion regarding the role of nonstationarity.
ValueWalk's Raul Panganiban interviews George Mussalli, Chief Investment Officer and Head of Equity Research at PanAgora Asset Management. In this epispode, they discuss quant ESG as well as PanAgoras unique approach to it. The following is a computer generated transcript and may contain some errors. Q3 2020 hedge fund letters, conferences and more Interview . Read More
Any actual sequence of numbers, even one generated by a random process, will have certain statistical quirks. Physicist Richard Feynman used to make this point with reference to the first 767 digits of Pi, replicated below. Allegedly (but unconfirmed) he liked to reel off the first 761 digits, and then say 9-9-9-9-9 and so on.[1] If you only look at the first 767 digits the replication of six straight nines is clearly an anomaly a potential investment opportunity. In fact, there is no discernible pattern in the digits of Pi. Feynman was purposely making fun of data mining by focusing on the first 767 digits.
3 .1 4 1 5 9 2 6 5 3 5 8 9 7 9 3 2 3 8 4 6 2 6 4 3 3 8 3 2 7 9 5 0 2 8 8 4 1 9 7 1 6 9 3 9 9 3 7 5 1 0 5 8 2 0 9 7 4 9 4 4 5 9 2 3 0 7 8 1 6 4 0 6 2 8 6 2 0 8 9 9 8 6 2 8 0 3 4 8 2 5 3 4 2 1 1 7 0 6 7 9 8 2 1 4 8 0 8 6 5 1 3 2 8 2 3 0 6 6 4 7 0 9 3 8 4 4 6 0 9 5 5 0 5 8 2 2 3 1 7 2 5 3 5 9 4 0 8 1 2 8 4 8 1 1 1 7 4 5 0 2 8 4 1 0 2 7 0 1 9 3 8 5 2 1 1 0 5 5 5 9 6 4 4 6 2 2 9 4 8 9 5 4 9 3 0 3 8 1 9 6 4 4 2 8 8 1 0 9 7 5 6 6 5 9 3 3 4 4 6 1 2 8 4 7 5 6 4 8 2 3 3 7 8 6 7 8 3 1 6 5 2 7 1 2 0 1 9 0 9 1 4 5 6 4 8 5 6 6 9 2 3 4 6 0 3 4 8 6 1 0 4 5 4 3 2 6 6 4 8 2 1 3 3 9 3 6 0 7 2 6 0 2 4 9 1 4 1 2 7 3 7 2 4 5 8 7 0 0 6 6 0 6 3 1 5 5 8 8 1 7 4 8 8 1 5 2 0 9 2 0 9 6 2 8 2 9 2 5 4 0 9 1 7 1 5 3 6 4 3 6 7 8 9 2 5 9 0 3 6 0 0 1 1 3 3 0 5 3 0 5 4 8 8 2 0 4 6 6 5 2 1 3 8 4 1 4 6 9 5 1 9 4 1 5 1 1 6 0 9 4 3 3 0 5 7 2 7 0 3 6 5 7 5 9 5 9 1 9 5 3 0 9 2 1 8 6 1 1 7 3 8 1 9 3 2 6 1 1 7 9 3 1 0 5 1 1 8 5 4 8 0 7 4 4 6 2 3 7 9 9 6 2 7 4 9 5 6 7 3 5 1 8 8 5 7 5 2 7 2 4 8 9 1 2 2 7 9 3 8 1 8 3 0 1 1 9 4 9 1 2 9 8 3 3 6 7 3 3 6 2 4 4 0 6 5 6 6 4 3 0 8 6 0 2 1 3 9 4 9 4 6 3 9 5 2 2 4 7 3 7 1 9 0 7 0 2 1 7 9 8 6 0 9 4 3 7 0 2 7 7 0 5 3 9 2 1 7 1 7 6 2 9 3 1 7 6 7 5 2 3 8 4 6 7 4 8 1 8 4 6 7 6 6 9 4 0 5 1 3 2 0 0 0 5 6 8 1 2 7 1 4 5 2 6 3 5 6 0 8 2 7 7 8 5 7 7 1 3 4 2 7 5 7 7 8 9 6 0 9 1 7 3 6 3 7 1 7 8 7 2 1 4 6 8 4 4 0 9 0 1 2 2 4 9 5 3 4 3 0 1 4 6 5 4 9 5 8 5 3 7 1 0 5 0 7 9 2 2 7 9 6 8 9 2 5 8 9 2 3 5 4 2 0 1 9 9 5 6 1 1 2 1 2 9 0 2 1 9 6 0 8 6 4 0 3 4 4 1 8 1 5 9 8 1 3 6 2 9 7 7 4 7 7 1 3 0 9 9 6 0 5 1 8 7 0 7 2 1 1 3 4 9 9 9 9 9 9
When it comes to investing, there is only one sequence of historical returns. With sufficient computing power and with repeated torturing of the data, anomalies are certain to be detected. A good example is factor investing. The publication of a highly influential paper by Professors Eugene Fama and Kenneth French identified three systematic investment factors, which started an industry focused on searching for additional factors. Research by Arnott, Harvey, Kalesnik and Linnainmaa reports that by year-end 2018 an implausibly large 400 significant factors had been discovered. One wonders how many such anomalies quantum computers might find.
Factor investing is just one example among many. Richard Roll, a leading academic financial economist with in-depth knowledge of the anomalies literature has also been an active financial manager. Based on his experience Roll stated that his money management firms attempted to make money from numerous anomalies widely documented in the academic literature but failed to make a nickel.
The simple fact is that if you have machines that can look closely enough at any historical data set, they will find anomalies. For instance, what about the anomalous sequence 0123456789 in the expansion of Pi.? That anomaly can be found beginning at digit 17,387,594,880.
The digits of Pi may be random, but they are stationary. The process that generates the first million digits is the same as the one which generates the million digits beginning at one trillion. The same is not true of investing. Consider, for example, providing a computer the sequence of daily returns on Apple stock from the day the company went public to the present. The computer could sift through the returns looking for patterns, but this is almost certainly a fruitless endeavor. The company that generated those returns is far from stationary. In 1978, Apple was run by two young entrepreneurs and had total revenues of $0.0078 billion. By 2019, the company was run by a large, experienced, management team and had revenues of $274 billion, an increase of about 35,000 times. The statistical process generating those returns is almost certainly nonstationary due to fundamental changes in the company generating them. To a lesser extent, the same is true of nearly every listed company. The market is constantly in flux and the companies are constantly evolving as consumer demands, government regulation, and technology, among other things, continually change. It is hard to imagine that even if there were past patterns in stock prices that were more than data mining, they would persist for long due to nonstationarity.
In the finance arena, computers and artificial intelligence work by using their massive data processing skills to find patterns that humans may miss. But in a nonstationary world the ultimate financial risk is that by the time they are identified those patterns will be gone. As a result, computerized trading comes to resemble a dog chasing its tail. This leads to excessive trading and ever rising costs without delivering superior results on average. Quantum computing risks simply adding fuel. Of course, there are individual cases where specific quant funds make highly impressive returns, but that too could be an example of data mining. Given the large number of firms in the money management business, the probability that a few do extraordinarily well is essentially one.
These criticisms are not meant to imply that quantum computing has no role to play in finance. For instance, it has great potential to improve the simulation analyses involved in assessing risk. The point here is that it will not be a holy grail for improving investment performance.
Despite the drawbacks associated with data mining and nonstationarity, there is one area in which the potential for quantum computing is particularly bright marketing quantitative investment strategies. Selling quantitative investment has always been an art. It involves convincing people that the investment manager knows something that will make them money, but which is too complicated to explain to them and, in some cases, too complicated for the manager to understand. Quantum computing takes that sales pitch to a whole new level because virtually no one will be able to understand how the machine decided that a particular investment strategy is attractive.
This skeptics take is that quantum computing will have little impact on what is ultimately the source of successful investing allocating capital to companies that have particularly bright prospects for developing profitable business in a highly uncertain and non-stationary world. Perhaps at some future date a computer will development the business judgment to determine whether Teslas business prospects justify its current stock price. Until then being able to comb through historical data in search of obscure patterns at ever increasing rates is more likely to produce profits through the generation of management fees rather than the enhancement of investor returns.
[1] The Feynman story has been repeated so often that the sequence of 9s starting at digit 762 is now referred to as the Feynman point in the expansion of Pi.
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2020 in Review: 10 AI-Powered Tools Tackling COVID-19 – Synced
Over 82 million people have been infected worldwide, and the number of new COVID-19 cases has continued to climb in recent months. As we anxiously await vaccines, artificial intelligence is already battling the virus on a number of fronts from predicting protein structure and diagnosing patients to automatically disinfecting public areas.
As part of our year-end series, Synced highlights 10 AI-powered efforts that contributed to the fight against COVID-19 in 2020.
To help the global research community better understand the coronavirus, UK-based AI company and research lab DeepMind in March leveraged their AlphaFold system to releasestructure predictions for six proteins associated with SARS-CoV-2, the virus that causes COVID-19. In August, DeepMind released additional SARS-CoV-2 structure predictions for five understudied SARS-CoV-2 targets.
AlphaFold was introduced in December 2018. The deep learning system is designed to accurately predict protein structure even when no structures of similar proteins are available, and can generate 3D models of proteins with SOTA accuracy. On November 30, the latest version of AlphaFold was recognized for solving the biennial Critical Assessment of Protein Structure Prediction (CASP) grand challenge with unparalleled levels of accuracy. DeepMind says AlphaFolds success demonstrates the impact AI can have on scientific discovery and its potential to dramatically accelerate progress in some of the most fundamental fields that explain and shape our world.
CT (computed tomography) lung scans and nucleic acid tests are the two main diagnostic tools doctors use in confirming COVID-19 infections, and CT imaging is crucial for lung infection diagnosis verification and severity assessment.
In January, the Shanghai Public Health Clinical Center (SPHCC) partnered with Chinese AI startup YITU Technologys healthcare division which provides AI-powered medical imaging solutions for lung cancer diagnosis to build an AI CT image reader.
By February 5, the AI diagnostic system had been deployed in four Hubei province hospitals struggling with ongoing doctor and supply shortages. Functionalities catering to the specific needs of clinical departments such as radiology, respiratory, emergency, intensive care, etc., were built into the system.
In response to the COVID-19 pandemic, the US White House joined with research groups in March to announce the release of the COVID-19 Open Research Dataset (CORD-19) of scholarly literature about COVID-19, SARS-CoV-2, and the coronavirus group. The release came with an urgent call to action to the worlds AI experts to develop new text and data mining techniques that can help the science community answer high-priority scientific questions related to COVID-19.
The online ML community Kaggle is hosting a CORD-19 dataset challenge that defines 10 tasks based on key scientific questions developed in coordination with the WHO and the National Academies of Sciences, Engineering, and Medicines Standing Committee on Emerging Infectious Diseases and 21st Century Health Threats.
Developed by the Pande Laboratory at Stanford University in 2000 as a distributed computing project for simulating protein dynamics including the process of protein folding and the movements of protein implicated in a variety of diseases the Folding@Home project aims to build a network of protein dynamics simulations run on volunteers personal computers to provide insights that could help researchers develop new therapeutics.
The current focus of Folding@Home is modelling the structure of the 2019-nCoV spike protein to identify sites that can be targeted by therapeutic antibodies. Coronaviruses invade cells via spike protein on their surfaces, which binds to a lung cells receptor protein. Understanding the structure of viral spike protein and how it binds to the ACE-2 human host cell receptor can help scientists stop viral entry into human cells. Anyone who would like to donate their unused computing power can join Folding@Homes fight against the coronavirus.
In March, Canadian startup DarwinAI released COVID-Net, an open-sourced neural network for COVID-19 detection using chest radiography (X-Rays). Company CEO Sheldon Fernandez says COVID-Net has been leveraged by researchers in Italy, Canada, Spain, Malaysia, India and the US.
Fernandez explains that rather than treating AI as a tool, his company reimagines AI as a collaborator that learns from a developers needs and subsequently proposes multiple design approaches with different trade-offs in order to enable a rapid and iterative approach to model building.
In response to the COVID-19 pandemic, Andrej Karpathy director of artificial intelligence and Autopilot Vision at Tesla and developer of the arXiv sanity preserver web interface introduced Covid-Sanity, a web interface designed to navigate the flood of bioRxiv and medRxiv COVID-19 papers and make the research within more searchable and sortable.
Covid-Sanity organizes COVID-19-related papers with a most similar search that uses an exemplar SVM trained on TF-IDF feature vectors from the abstracts of the papers. This is similar to the Google search engine, which responds by finding the relevance of a query in all texts, ranks by similarity scores and returns the top-k results. Based on paper abstracts, the web interface returns all papers similar to the best-matched paper result to a query.
Disinfection of public areas is a challenging but crucial process in the fight to stop the spread of the COVID-19. In China, ad hoc teams of DJI drone hobbyists sprung up nationwide to provide this service for free. By February, total DJI agricultural drone disinfection coverage had exceeded 600 million square meters across more than 1,000 villages including schools, isolation wards, food waste treatment plants, waste incineration plants, livestock and poultry epidemic prevention centres and more.
Shenzhen-based DJI is a leading drone and associated technologies company. In February they launched the DJI Army Against the Virus project, providing subsidies to support working pilots, with provisions for pilot protective kits and assistance to villages who perform drone disinfection. Spare parts and drone repair services were also provided during the missions.
Many AI-powered autonomous vehicles navigated Chinese streets in response to the COVID-19 outbreak. Developed and modified for the purpose by Chinese O2O local life service company Meituan, Modai (Magic Bag) vehicles delivered much-needed groceries to communities in Beijings Shunyi District.
Self-driving delivery vehicles like Modai were an effective solution to the COVID-triggered surge of online grocery orders and the need to reduce interpersonal contact to slow disease spread. The urgent needs and empty streets drew many companies into autonomous delivery JD Logistics developed self-driving delivery vehicles in Wuhan and for the first time delivered medical supplies to the Wuhan Ninth Hospital, and the Suning Logistics 5G Wolong self-driving car delivered its first orders in Suzhou.
Japans Fujitsu Ltd developed an artificial intelligence monitor to ensure healthcare, hotel, and food industry workers wash their hands properly, according to a Reuters report. The system is based on crime surveillance technology that detects suspicious body movements, and can recognize and classify complex hand movements. It checks whether people complete a Japanese health ministry six-step hand washing procedure similar to guidelines issued by the WHO (clean palms, wash thumbs, between fingers and around wrists and scrub fingernails). The monitor can even tag instances of people not using soap.
Fei-Fei Li, Stanford computer science professor and co-director of Stanfords Human-Centered AI Institute (HAI), shared her thoughts on AI technologies that could help seniors during the coronavirus pandemic in Aprils COVID-19 and AI: A Virtual Conference. Li identified AI-powered smart home sensor technology as a way to help families and clinicians remotely monitor housebound seniors for infection symptoms or symptom progression or regression and potentially also help manage their chronic health issues.
Research institution Strategy Analytics predicts the smart home market will resume in 2021 and consumer spending will increase to US$62 billion. The post-pandemic global smart home device market is expected to maintain a compound annual growth rate of 15 percent.
As efforts to control the spread of COVID-19 continue, contact tracing has emerged as a public health tool where ML can play an important role in optimizing systems. Various countries have developed digital contact tracing processes with mobile applications, utilizing technologies like Bluetooth, the Global Positioning System (GPS), social graphs, network-based API, mobile tracking data, system physical addresses, etc. These apps collect massive data from individuals, which ML and AI tools analyze to identify and trace vulnerable people.
A study published by the US National Library of Medicine shows that by June, over 36 countries had successfully employed digital contact tracing systems using a mixture of ML and other techniques.
Reporter: Yuan Yuan | Editor: Michael Sarazen
Synced Report |A Survey of Chinas Artificial Intelligence Solutions in Response to the COVID-19 Pandemic 87 Case Studies from 700+ AI Vendors
This report offers a look at how China has leveraged artificial intelligence technologies in the battle against COVID-19. It is also available onAmazon Kindle.Along with this report, we also introduced adatabasecovering additional 1428 artificial intelligence solutions from 12 pandemic scenarios.
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2020 in Review: 10 AI-Powered Tools Tackling COVID-19 - Synced
Data Warehouse As A Service Dwaas Market 2020 Global Trends, Market Share, Industry Size, Growth, Opportunities Analysis and Forecast to 2026 : IBM,…
The Data Warehouse As A Service Dwaas Market report classifies the market into different segments based on the application, technique and end user. These segments are studied in detail incorporating the market estimates and forecast at the regional and country level. The segment analysis is useful in the understanding the growth areas and credible opportunities of the market. In the end, the report makes some important proposal of the new project of Data Warehouse As A Service Dwaas industry before evaluating its feasibility. The report provides an in-depth insight of global market covering all important parameters.
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IBM, AWS, Google, Snowflake, Teradata, Microsoft, SAP, Micro Focus, Cloudera, Actian, Pivotal Software, Hortonworks, Solver, Yellowbrick, Panoply, MemSQL, Netavis, LUX Fund Technology & Solutions, MarkLogic, Transwarp Technology
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Data Mining Software Market Size 2020 by Top Key Players, Global Trend, Types, Applications, Regional Demand, Forecast to 2027 – LionLowdown
New Jersey, United States,- The report, titled Data Mining Software Market Size By Types, Applications, Segmentation, and Growth Global Analysis and Forecast to 2019-2027 first introduced the fundamentals of Data Mining Software: definitions, classifications, applications and market overview; Product specifications; Production method; Cost Structures, Raw Materials, etc. The report takes into account the impact of the novel COVID-19 pandemic on the Data Mining Software market and also provides an assessment of the market definition as well as the identification of the top key manufacturers which are analyzed in-depth as opposed to the competitive landscape. In terms of Price, Sales, Capacity, Import, Export, Data Mining Software Market Size, Consumption, Gross, Gross Margin, Sales, and Market Share. Quantitative analysis of the Data Mining Software industry from 2019 to 2027 by region, type, application, and consumption rating by region.
Impact of COVID-19 on Data Mining Software Market: The Coronavirus Recession is an economic recession that will hit the global economy in 2020 due to the COVID-19 pandemic. The pandemic could affect three main aspects of the global economy: manufacturing, supply chain, business and financial markets. The report offers a full version of the Data Mining Software Market, outlining the impact of COVID-19 and the changes expected on the future prospects of the industry, taking into account political, economic, social, and technological parameters.
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In market segmentation by manufacturers, the report covers the following companies-
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Presently, going to the main part-outside elements. Porters five powers are the main components to be thought of while moving into new business markets. The customers get the opportunity to use the approaches to plan the field-tested strategies without any preparation for the impending monetary years.
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Data Mining Software Market: Regional analysis includes:
?Asia-Pacific(Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia)?Europe(Turkey, Germany, Russia UK, Italy, France, etc.)?North America(the United States, Mexico, and Canada.)?South America(Brazil etc.)?The Middle East and Africa(GCC Countries and Egypt.)
The report includes Competitors Landscape:
? Major trends and growth projections by region and country? Key winning strategies followed by the competitors? Who are the key competitors in this industry?? What shall be the potential of this industry over the forecast tenure?? What are the factors propelling the demand for the Data Mining Software Industry?? What are the opportunities that shall aid in the significant proliferation of market growth?? What are the regional and country wise regulations that shall either hamper or boost the demand for Data Mining Software Industry?? How has the covid-19 impacted the growth of the market?? Has the supply chain disruption caused changes in the entire value chain?
The report also covers the trade scenario,Porters Analysis,PESTLE analysis, value chain analysis, company market share, segmental analysis.
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Lifesciences Data Mining and Visualization Market by Manufacturers, Regions, Type and Application, Forecast To 2026 Tableau Software, SAP SE, IBM,…
Zeal Insider offers in-depth report on Lifesciences Data Mining and Visualization market which consists of wide range of crucial parameters affecting the growth of Lifesciences Data Mining and Visualization market. It is actually a comprehensive primary and secondary research study in order to provide market size, industry growth opportunities and challenges, current market trends, potential players, and expected performance of the market in near future across the globe. It centers its content on client requirement so as it help our clients to make right decision about their business investment plans and strategies.
Additionally, Lifesciences Data Mining and Visualization Market report covers complete summary of its segments and sub-segments in terms of types and applications. Further, it involves regional analysis and competitive analysis. It moreover adds current scenario of COVID-19 impact that the world is facing. There is hardly any place in the world that has remained unaffected by the brutality of the Covid-19 pandemic; thus, our research team has explained dynamics of the market, future business impact, competition landscape of the companies, and the flow of the global supply and consumption accordingly by analyzing.
Major players involved in Lifesciences Data Mining and Visualization market report:
Tableau SoftwareSAP SEIBMSAS InstituteMicrosoftOracle
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Research methodology used to bind up Lifesciences Data Mining and Visualization market report include primary and secondary research ways. Primary research type consists of interviews to take basic idea about the market. Our research team has interviewed concerned people from manufacturing companies, executives & representatives of products, and people involved in supply chain. The report has also combined its data from trusted secondary sources, such as companys annual reports, sites, etc.
Complete Lifesciences Data Mining and Visualization market report is made up of some graphical representations, tables, and figures which displays a clear picture of the developments of the products and its market performance during the estimated time period. The pictorial representation makes easy understanding about the growth rate, regional shares as well as segmentation revenue growth. Moreover, Lifesciences Data Mining and Visualization market report covers recent agreements including merger & acquisition, partnership or joint venture and latest developments of the manufacturers to sustain in the global competition.
We can add more companies as per the requirement. The report involves complete profiling of major players involved in Lifesciences Data Mining and Visualization market. It includes business overview, basic information of company and its competitors. Further, their R&D investment, sales by segment and sales by regions for consecutive years has been included. Profiling of company also include SWOT analysis, key developments and business strategy.
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Segmentation of Lifesciences Data Mining and Visualization Market:
Market, By Types:On PremiseOn DemandBoth
Market, By Applications:AcademiaBiotechGovernmentPharmaceuticalsContract Research Organization (CRO)Others
As per the report, Lifesciences Data Mining and Visualization market revenue in year 2015 was USD XX Million and is expected to reach USD XX Million in year 2027 at XX% CAGR. It describes current changing market trends to help our client make astute decisions accordingly. We are also ready to serve with customized report. According to the need of the clients, this report can be customized and available in a separate report for the specific region.
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Lifesciences Data Mining and Visualization Market report will help you to understand the market components by analyzing it for the period of 2015-2027. It offers a structural framework of major players along with their dynamics and strategies. Further, it adds detailed account on the impact of COVID-19 on Lifesciences Data Mining and Visualization market. It helps to understand the changing scenario of market due to the out-burst of corona virus across the globe. The companys report involves concise analysis of Lifesciences Data Mining and Visualization market for historical years, base year as well as forecast period. Thus, we provide complete guideline for the clients to make correct decision offering current & future market situation.
Key Questions Answered in this Report:
What is the market size?This report covers the historical market size of the industry (2014-2027), and forecasts for 2020 and the next 7 years. Market size includes the total revenues of companies.
What is the outlook for Lifesciences Data Mining and Visualization Industry?This includes complete analysis of industry along with number of companies, attractive investment opportunities, operating expenses, and others.
How many companies are in Lifesciences Data Mining and Visualization market and what are their strategies?This report analyzes the historical and forecasted number of companies, locations in the industry, and breaks them down by company size over time. Report also provides company rank against its competitors with respect to revenue, profit comparison, operational efficiency, cost competitiveness and market capitalization.
What are the financial metrics for the industry?This report covers many financial metrics for the industry including profitability, Market value- chain and key trends impacting every node with reference to companys growth, revenue, return on sales, etc.
Which region is highest market share in Lifesciences Data Mining and Visualization Market?It gives reasons for that particular region which holds highest market share.
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An introduction to data science and machine learning with Microsoft Excel – TechTalks
This article is part ofAI education, a series of posts that review and explore educational content on data science and machine learning. (In partnership withPaperspace)
Machine learning and deep learning have become an important part of many applications we use every day. There are few domains that the fast expansion of machine learning hasnt touched. Many businesses have thrived by developing the right strategy to integrate machine learning algorithms into their operations and processes. Others have lost ground to competitors after ignoring the undeniable advances in artificial intelligence.
But mastering machine learning is a difficult process. You need to start with a solid knowledge of linear algebra and calculus, master a programming language such as Python, and become proficient with data science and machine learning libraries such as Numpy, Scikit-learn, TensorFlow, and PyTorch.
And if you want to create machine learning systems that integrate and scale, youll have to learn cloud platforms such as Amazon AWS, Microsoft Azure, and Google Cloud.
Naturally, not everyone needs to become a machine learning engineer. But almost everyone who is running a business or organization that systematically collects and processes can benefit from some knowledge of data science and machine learning. Fortunately, there are several courses that provide a high-level overview of machine learning and deep learning without going too deep into math and coding.
But in my experience, a good understanding of data science and machine learning requires some hands-on experience with algorithms. In this regard, a very valuable and often-overlooked tool is Microsoft Excel.
To most people, MS Excel is a spreadsheet application that stores data in tabular format and performs very basic mathematical operations. But in reality, Excel is a powerful computation tool that can solve complicated problems. Excel also has many features that allow you to create machine learning models directly into your workbooks.
While Ive been using Excels mathematical tools for years, I didnt come to appreciate its use for learning and applying data science and machine learning until I picked up Learn Data Mining Through Excel: A Step-by-Step Approach for Understanding Machine Learning Methods by Hong Zhou.
Learn Data Mining Through Excel takes you through the basics of machine learning step by step and shows how you can implement many algorithms using basic Excel functions and a few of the applications advanced tools.
While Excel will in no way replace Python machine learning, it is a great window to learn the basics of AI and solve many basic problems without writing a line of code.
Linear regression is a simple machine learning algorithm that has many uses for analyzing data and predicting outcomes. Linear regression is especially useful when your data is neatly arranged in tabular format. Excel has several features that enable you to create regression models from tabular data in your spreadsheets.
One of the most intuitive is the data chart tool, which is a powerful data visualization feature. For instance, the scatter plot chart displays the values of your data on a cartesian plane. But in addition to showing the distribution of your data, Excels chart tool can create a machine learning model that can predict the changes in the values of your data. The feature, called Trendline, creates a regression model from your data. You can set the trendline to one of several regression algorithms, including linear, polynomial, logarithmic, and exponential. You can also configure the chart to display the parameters of your machine learning model, which you can use to predict the outcome of new observations.
You can add several trendlines to the same chart. This makes it easy to quickly test and compare the performance of different machine learning models on your data.
In addition to exploring the chart tool, Learn Data Mining Through Excel takes you through several other procedures that can help develop more advanced regression models. These include formulas such as LINEST and LINREG formulas, which calculate the parameters of your machine learning models based on your training data.
The author also takes you through the step-by-step creation of linear regression models using Excels basic formulas such as SUM and SUMPRODUCT. This is a recurring theme in the book: Youll see the mathematical formula of a machine learning model, learn the basic reasoning behind it, and create it step by step by combining values and formulas in several cells and cell arrays.
While this might not be the most efficient way to do production-level data science work, it is certainly a very good way to learn the workings of machine learning algorithms.
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Beyond regression models, you can use Excel for other machine learning algorithms. Learn Data Mining Through Excel provides a rich roster of supervised and unsupervised machine learning algorithms, including k-means clustering, k-nearest neighbor, nave Bayes classification, and decision trees.
The process can get a bit convoluted at times, but if you stay on track, the logic will easily fall in place. For instance, in the k-means clustering chapter, youll get to use a vast array of Excel formulas and features (INDEX, IF, AVERAGEIF, ADDRESS, and many others) across several worksheets to calculate cluster centers and refine them. This is not a very efficient way to do clustering, youll be able to track and study your clusters as they become refined in every consecutive sheet. From an educational standpoint, the experience is very different from programming books where you provide a machine learning library function your data points and it outputs the clusters and their properties.
In the decision tree chapter, you will go through the process calculating entropy and selecting features for each branch of your machine learning model. Again, the process is slow and manual, but seeing under the hood of the machine learning algorithm is a rewarding experience.
In many of the books chapters, youll use the Solver tool to minimize your loss function. This is where youll see the limits of Excel, because even a simple model with a dozen parameters can slow your computer down to a crawl, especially if your data sample is several hundred rows in size. But the Solver is an especially powerful tool when you want to finetune the parameters of your machine learning model.
Learn Data Mining Through Excel shows that Excel can even advanced machine learning algorithms. Theres a chapter that delves into the meticulous creation of deep learning models. First, youll create a single layer artificial neural network with less than a dozen parameters. Then youll expand on the concept to create a deep learning model with hidden layers. The computation is very slow and inefficient, but it works, and the components are the same: cell values, formulas, and the powerful Solver tool.
In the last chapter, youll create a rudimentary natural language processing (NLP) application, using Excel to create a sentiment analysis machine learning model. Youll use formulas to create a bag of words model, preprocess and tokenize hotel reviews and classify them based on the density of positive and negative keywords. In the process youll learn quite a bit about how contemporary AI deals with language and how much different it is from how we humans process written and spoken language.
Whether youre making C-level decisions at your company, working in human resources, or managing supply chains and manufacturing facilities, a basic knowledge of machine learning will be important if you will be working with data scientists and AI people. Likewise, if youre a reporter covering AI news or a PR agency working on behalf a company that uses machine learning, writing about the technology without knowing how it works is a bad idea (I will write a separate post about the many awful AI pitches I receive every day). In my opinion, Learn Data Mining Through Excel is a smooth and quick read that will help you gain that important knowledge.
Beyond learning the basics, Excel can be a powerful addition to your repertoire of machine learning tools. While its not good for dealing with big data sets and complicated algorithms, it can help with the visualization and analysis of smaller batches of data. The results you obtain from a quick Excel mining can provide pertinent insights in choosing the right direction and machine learning algorithm to tackle the problem at hand.
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An introduction to data science and machine learning with Microsoft Excel - TechTalks
Alarm Bells Ringing On Education Technology – The Chattanoogan
The 2020 Netflix movie, The Social Dilemma, explores the growth of social media and the damage it has caused to society. It features interviews with many former executives and professionals from tech companies and social media platforms such as Facebook, Twitter, Google, and Apple who sounded the alarm on their industry.
The Social Dilemma asserts that tech companies exploit and manipulate their users for financial gain through surveillance capitalism and data mining. The movie is very thought-provoking and chillingly points out: Never before have a handful of tech designers had such control over the way billions of us think, act, and live our lives. It goes into depth on how social media's design is meant to nurture an addiction, manipulate its use in politics, and spread conspiracy theories. The Social Dilemma paints the picture of a dangerous human impact on society and the serious issue of social media's effect on mental health (including the mental health of adolescents and rising teen suicide rates).
In public education, online instruction is inferior to effective in-person instruction. Our fear is especially heightened with the younger students. Frank Ghinassi from Rutgers University suggested in USA Today that children most harmed being online are those who were already disadvantaged by food or housing instability, domestic violence, unsafe neighborhoods, fragmented families or absent role models. Yet, millions of students are now learning online, including thousands of students here in Tennessee.
Spiros Protopsaltis and Sandy Baum, both professors at George Mason University, issued a report observing that, Students in online education, and in particular underprepared and disadvantaged students, underperform and on average, experience poor outcomes, and that online education, does not produce a positive return on investment. Although like the authors, we share their optimism that technology has the potential to increase access to education, enhance learning experiences, and reduce the cost of providing high-quality education, in-person instruction will always be the preferred manner of instruction, especially for younger children.
We need more time and better evidence, including looking at best practices, before further implementation and expansion. The Center for Humane Technology suggests: Exposure to unrestrained levels of digital technology can have serious long-term consequences for childrens development, creating permanent changes in brain structure that impact how children will think, feel, and act throughout their lives. Due to COVID-19, and the rush to keep student instruction going, the process was rushed into existence---a forced necessity. In our haste to meet this need, the economics of scale, scope, and action could end up creating many unexpected consequences of not being properly scrutinized or implemented properly.
Educators have had to build the plane while flying it, with online K12 instruction. They should be commended and rewarded for their efforts. Standard implementation processes and systems were not followed. We need to analyze what went well and what went wrong---with the systems and the processes. We especially need to strengthen privacy laws and limit data collection, as well as addressing issues such as digital manipulation and boundaries of responsibility for algorithmic fairness.
In public education, we should ask if there is a correlation between rising concerns with social media and untested statewide online education? Will the next Netflix movie be sounding alarms on a dilemma with online education, or perhaps a dilemma that teachers themselves face in a virtual environment?
Overzealous data mining causes serious confidence in public education and creates privacy concerns if individual student data is compromised. Has anyone asked serious questions about what the contracts look like between those providing online education and the districts or state? What data is being collected? Schools have always collected data, but that information has been largely protected or ignored. That is now likely to change, and educational data will be captured, mined, and possibly manipulated.
Tristan Harris, featured in The Social Dilemma, writes in the New York Times: Simply put, technology has outmatched our brains, diminishing our capacity to address the worlds most pressing challenges. If Harris is as correct as he is persuasive, we have the power to reverse these trends. Will we exercise that power? How can we safeguard the beneficial aspects of technology while protecting individual privacy? We likely need additional policies and legislation to control and minimize the risks and propose necessary protections that empower the users of technology.
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Scott Cepicky is a State Representative of Tennessee in the 64th district. JC Bowman is the Executive Director of Professional Educators of Tennessee
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Alarm Bells Ringing On Education Technology - The Chattanoogan
Intersection of Big Data Analytics, COVID-19 Top Focus of 2020 – HealthITAnalytics.com
December 24, 2020 -The end of 2020 marks the conclusion of one of the most formidable years the healthcare industry has seen in recent memory.
The COVID-19 pandemic brought new challenges with it, while also shining a harsh light on longstanding issues. Leaders acted quickly to leverage big data analytics tools, including AI and machine learning, to make sense of the virus and control its spread, resulting in a year of technological achievements and rich data resources.
In a list of the top ten stories from the past 12 months, HealthITAnalytics describes the events and trends that dominated readers attention. While many will be glad to see 2020 go, a look back on some of its major incidents indicates that the crisis sparked innovations that will live on long after the new year.
Soon after the Trump administration declared COVID-19 a national emergency, officials sought the help of big data analytics tools to better understand virus transmission, risk factors, origin, diagnostics, and other vital information.
The White House Office of Science and Technology Policy issued a call to action for experts to develop artificial intelligence tools that could be applied to a COVID-19 dataset the most extensive machine-readable coronavirus literature collection available for data mining at that point.
READ MORE: 4 Emerging Strategies to Advance Big Data Analytics in Healthcare
The call to action showed leaders confidence in the potential of AI, and foreshadowed the critical role advanced analytics tools would play in mitigating the impact of the pandemic.
With the FDA recently granting emergency use authorizations for new COVID-19 vaccines, many people in the US are looking forward to the beginning of the end of the pandemic.
However, as this MIT study showed, these vaccines may not be the all-encompassing solutions theyre believed to be.
Researchers used an artificial intelligence tool to examine a kind of vaccine similar to COVID-19 vaccines and found that it could be less effective in people of black or Asian ancestry. The results further emphasize the stark racial and ethnic disparities that have been consistently highlighted throughout the pandemic.
As the pandemic has worn on, public health officials are continually searching for innovative tools to help allocate resources and guide decision-making. A team from Johns Hopkins School of Public Health leveraged big data analytics to develop a COVID-19 mortality risk calculator, which could inform public health policies around preventive resources, like N-95 masks.
READ MORE: Is Healthcare Any Closer to Achieving the Promises of Big Data Analytics?
The risk calculator could also help allocate early vaccines, acting as a companion to guidelines from other organizations and ensuring that the right people are vaccinated first.
The onset of COVID-19 sparked a new wave of data sharing and access in healthcare. In late March, Google Cloud announced that it would offer researchers free access to critical coronavirus information through its COVID-19 Public Dataset Program, which aims to accelerate analytics solutions during the global pandemic.
The program will make a hosted repository of public datasets free to access and query, including the Johns Hopkins Center for Systems Science and Engineering (JHU CSSE) dashboard, Global Health Data from the World Bank, and OpenStreetMap data.
Early in the pandemic, researchers were working to discover potential therapies for COVID-19 using AI and machine learning tools. Two graduates from the Data Science Institute at Columbia University launched a startup called EVQLV that creates algorithms capable of computationally generating, screening, and optimizing hundreds of millions of therapeutic antibodies.
Using this technology, the pair aimed to discover treatments that would likely help individuals infected by the virus that causes COVID-19. The machine learning algorithms are able to rapidly screen for therapeutic antibodies with a high probability of success.
READ MORE: Big Data Analytics Strategies are Maturing Quickly in Healthcare
When the virus began spreading throughout the US, it quickly became clear that some population groups were at higher risk than others. The virus has disproportionately impacted not only the elderly and people with underlying conditions, but also minority populations and individuals of lower socioeconomic status.
To get ahead of these trends, Medical Home Network leveraged artificial intelligence to identify individuals who have a heightened vulnerability to severe complications from COVID-19. The predictive analytics model has helped the Chicago-based organization prioritize care management outreach to patients most at risk from the virus.
With the surges of COVID-19 patients coming into hospitals and health systems, providers are in need of innovative tools that can help them prioritize and manage care. A team from NYU developed an artificial intelligence algorithm that could accurately predict which patients newly diagnosed with COVID-19 would go on to develop severe respiratory disease.
The study showed that characteristics thought to be hallmarks of COVID-19 like certain patterns in lung images, fever, and strong immune responses were not useful in predicting which patients with initial mild symptoms would go on to develop severe lung disease.
Data has been at the center of every COVID-19 research effort. The global spread of the virus, in conjunction with its complex nature, requires investigators to analyze massive amounts of information too much for the human brain to comprehend on its own.
In an interview with HealthITAnalytics, James Hendler, the Tetherless World Professor of Computer, Web, and Cognitive Science at Rensselaer Polytechnic Institute (RPI) and director of the Rensselaer Institute for Data Exploration and Applications (IDEA), discussed the ways in which researchers and developers are using AI, machine learning, and natural language processing to understand, track, and contain coronavirus.
RPI also offered government entities, research organizations, and industry access to innovative AI tools, as well as experts in data and public health to help combat COVID-19.
The rapid spread of COVID-19 meant that hospitals had to prepare for the worst. In a system that is already strained, the potential for waves of highly contagious patients can only translate to disaster.
With big data analytics tools, organizations were able to track and monitor the use of critical resources. Definitive Healthcare, in partnership with Esri, launched an interactive data platform allowing people to analyze US hospital bed capacity, as well as potential geographic areas of risk, during the COVID-19 outbreak.
The platform shows the location and number of licensed beds, staffed beds, ICU beds, and total bed utilization in the US.
Real-time data has been a primary focus throughout the COVID-19 pandemic, as evidenced by the most-read story on HealthITAnalytics in 2020.
Months before the US had implemented quarantine and social distancing measures, the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University released a web-based dashboard tracking real-time data on confirmed COVID-19 cases, deaths, and recoveries for all affected countries.
First publicly shared on January 22, 2020, the dashboard signified the pivotal role data and technology would play in the coming months, as leaders across the industry hurried to get ahead of the virus.
While 2020 may be not be a year that many remember fondly, it certainly could ignite some much-needed change in healthcare. From increased data sharing and access, to enhanced data analytics and AI tools, the pandemic has prompted researchers and developers to design innovative ways to mitigate the impact of COVID-19.
The COVID-19 pandemic will subside, but the strategies and advancements developed during this period may endure long after the crisis ends. Big data analytics tools have featured largely in the industrys response to coronavirus infections, and these technologies will likely continue to be an integral part of healthcare going forward.
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Intersection of Big Data Analytics, COVID-19 Top Focus of 2020 - HealthITAnalytics.com