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The Top 7 Best Data Science Platforms in 2020

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To begin with, a data science platform can be defined as a software hub. All the data science works such as exploring and integrating data utilizing different resources, coding and building models so as to leverage the new-found data, installing those models into the process of production, and serving up results through the reports or applications powered by models.

On a precise note, the data science platform works as a storage of diverse tools to accommodate the entire process of data modeling. These platforms not only empower data scientists to craft refined insights from collected data from different resources; but also helps them to communicate the probable results with the clients or stakeholders.

Businesses are opting for the data science platforms in order to incorporate smart decision-making process with data analytics and enhance customer satisfaction. With ceaseless advancements of technology, the data science platform is now capable of providing better flexibility and scalability.

A smart data science platform helps the data scientists offering the building blocks to create a solution. Also, such platforms create a comfortable environment for incorporating the solutions into products and business processes. Moreover, the best platforms supports the data scientists throughout the process of data and analytics tasks which encompass interactive exploration, visualization, deployment, performance engineering data preparation and data access.

We’ve brought an exclusive list of the best data science and machine-learning platforms:

1. Alteryx Analytics

Headquartered in Irvine, CA, Alteryx Analytics is machine-learning platform that helps data scientists in structuring models in a workflow. The company has acquired a data science enterprise, Yhat, to enhance its capabilities. Yhat, the data science platform focuses mainly on model management and disposition. Alteryx analytics helps companies in nurturing a successful data analytics culture without data scientists.

2. Databricks Unified Analytics Platform

This is an Apache Spark-based platform which offers patented features for performance, operations, real-time enablement, reliability, and security on Amazon Web Services (AWS). Based out of San Francisco, CA, the Databricks Unified Analytics Platform primarily serves the open source community.

3. H2O.ai

H2O.ai is a deep machine-learning platform specially envisioned for data scientists. Situated in Mountain View, California, the leader in machine-learning unified platform offers H20 Deep Water for deep-learning, H2O Sparkling Water for Spark integration, H2O Steam and H2O Flow.

Practically an open source, H2O.ai also offers a segment for predictive analytics. Currently, the open source ML of this platform is an industry standard.

4. Microsoft Azure Learning Studio

Microsoft is one of the world’s largest software vendors. It has made its presence in the domain of data science platform market with its Azure software products. The products include Power BI, Azure Machine-learning which is inclusive of Azure Machine-learning Studio, Azure HDInsight, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics.

5. KNIME Analytics Platform

Headquartered in Zurich, Switzerland, KNIME is quite helpful in end-to-end workflows for predictive analytics and ML. This platform collects great chunks of data from huge depositories such as Google which is often used as an enterprise solution. With over 100,000 global user, KNIME Analytics, as an open-source platform, promises enhanced performance, security and collaboration in organizations. Microsoft Azure and AWS has the cloud versions of this platform.

6. Cloudera

Another popular platform is Cloudera which is augmented for the cloud and enterprise data solutions. This sophisticated platform comprises automatic data pipelines. It also supports full Hadoop authentication and encryption. The excellent work that can be done with Cloudera is to run the different types of delicate data by allowing Spark queries within a safe environment.

7. RapidMiner

A product of a Boston-based company of same name, the RapidMiner platform comes with RapidMiner Radoop which helps in enhancing the functional competencies to a Hadoop environment. RapidMiner Studio is designed for model development, while RapidMiner Server enables the data scientists to share, collaborate and uphold the models. RapidMiner presents new performance and productivity capabilities to model development and execution in an excellent way.

Conclusion

With the overflow of data everywhere, data science platforms are the need of the hour. Many industries have opted for data science platform so as to maintain, manage, and preserve their data in recent years. Data science platforms are used by industries such as information technology, healthcare and life sciences, banking, financial services, and insurance (BFSI), Research, Manufacturing, and Energy and Utilities. The increasing adoption of data analytical tools has surged the data science platform market like never before.

Image Credit: Data Science Platforms

Source: https://datafloq.com/read/the-top-7-best-data-science-platforms-2020/8475

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If you did not already know

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Apache Clerezza google


Clerezza allows to easily develop semantic web applications by providing tools to manipulate RDF data, create RESTful Web Services and Renderlets using ScalaServerPages. Contents are stored as triples based on W3C RDF specification. These triples are stored via Clerezza’s Smart Content Binding (SCB). SCB defines a technology-agnostic layer to access and modify triple stores. It provides a java implementation of the graph data model specified by W3C RDF and functionalities to operate on that data model. SCB offers a service interface to access multiple named graphs and it can use various providers to manage RDF graphs in a technology specific manner, e.g., using Jena or Sesame. It also provides for adaptors that allow an application to use various APIs (including the Jena api) to process RDF graphs. Furthermore, SCB offers a serialization and a parsing service to convert a graph into a certain representation (format) and vice versa. …

Robust Variable Power Fractional LMS Algorithm (RVP-FLMS) google


In this paper, we propose an adaptive framework for the variable power of the fractional least mean square (FLMS) algorithm. The proposed algorithm named as robust variable power FLMS (RVP-FLMS) dynamically adapts the fractional power of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the problems of system identification and channel equalization are considered. The experiments clearly show that the proposed approach achieves better convergence rate and lower steady-state error compared to the FLMS. The MATLAB code for the related simulation is available online at https://goo.gl/dGTGmP.

Breadth-first Search (BFS) google


In graph theory, breadth-first search (BFS) is a strategy for searching in a graph when search is limited to essentially two operations: (a) visit and inspect a node of a graph; (b) gain access to visit the nodes that neighbor the currently visited node. The BFS begins at a root node and inspects all the neighboring nodes. Then for each of those neighbor nodes in turn, it inspects their neighbor nodes which were unvisited, and so on. Compare BFS with the equivalent, but more memory-efficient Iterative deepening depth-first search and contrast with depth-first search. …

Stochastic Average Adjusted Gradient (SAAG) google


Big Data problems in Machine Learning have large number of data points or large number of features, or both, which make training of models di cult because of high computational complexities of single iteration of learning algorithms. To solve such learning problems, Stochastic Approximation o ers an optimization approach to make complexity of each it- eration independent of number of data points by taking only one data point or mini-batch of data points during each iteration and thereby helping to solve problems with large num- ber of data points. Similarly, Coordinate Descent o ers another optimization approach to make iteration complexity independent of the number of features/coordinates/variables by taking only one feature or block of features, instead of all, during an iteration and thereby helping to solve problems with large number of features. In this paper, an op- timization framework, namely, Batch Block Optimization Framework has been developed to solve big data problems using the best of Stochastic Approximation as well as the best of Coordinate Descent approaches, independent of any solver. This framework is used to solve strongly convex and smooth empirical risk minimization problem with gradient de- scent (as a solver) and two novel Stochastic Average Adjusted Gradient methods have been proposed to reduce variance in mini-batch and block-coordinate setting of the developed framework. Theoretical analysis prove linear convergence of the proposed methods and empirical results with bench marked datasets prove the superiority of proposed methods against existing methods.
SAAGs: Biased Stochastic Variance Reduction Methods

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://analytixon.com/2021/04/22/if-you-did-not-already-know-1376/

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Big Data

If you did not already know

Avatar

Published

on

Apache Clerezza google


Clerezza allows to easily develop semantic web applications by providing tools to manipulate RDF data, create RESTful Web Services and Renderlets using ScalaServerPages. Contents are stored as triples based on W3C RDF specification. These triples are stored via Clerezza’s Smart Content Binding (SCB). SCB defines a technology-agnostic layer to access and modify triple stores. It provides a java implementation of the graph data model specified by W3C RDF and functionalities to operate on that data model. SCB offers a service interface to access multiple named graphs and it can use various providers to manage RDF graphs in a technology specific manner, e.g., using Jena or Sesame. It also provides for adaptors that allow an application to use various APIs (including the Jena api) to process RDF graphs. Furthermore, SCB offers a serialization and a parsing service to convert a graph into a certain representation (format) and vice versa. …

Robust Variable Power Fractional LMS Algorithm (RVP-FLMS) google


In this paper, we propose an adaptive framework for the variable power of the fractional least mean square (FLMS) algorithm. The proposed algorithm named as robust variable power FLMS (RVP-FLMS) dynamically adapts the fractional power of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the problems of system identification and channel equalization are considered. The experiments clearly show that the proposed approach achieves better convergence rate and lower steady-state error compared to the FLMS. The MATLAB code for the related simulation is available online at https://goo.gl/dGTGmP.

Breadth-first Search (BFS) google


In graph theory, breadth-first search (BFS) is a strategy for searching in a graph when search is limited to essentially two operations: (a) visit and inspect a node of a graph; (b) gain access to visit the nodes that neighbor the currently visited node. The BFS begins at a root node and inspects all the neighboring nodes. Then for each of those neighbor nodes in turn, it inspects their neighbor nodes which were unvisited, and so on. Compare BFS with the equivalent, but more memory-efficient Iterative deepening depth-first search and contrast with depth-first search. …

Stochastic Average Adjusted Gradient (SAAG) google


Big Data problems in Machine Learning have large number of data points or large number of features, or both, which make training of models di cult because of high computational complexities of single iteration of learning algorithms. To solve such learning problems, Stochastic Approximation o ers an optimization approach to make complexity of each it- eration independent of number of data points by taking only one data point or mini-batch of data points during each iteration and thereby helping to solve problems with large num- ber of data points. Similarly, Coordinate Descent o ers another optimization approach to make iteration complexity independent of the number of features/coordinates/variables by taking only one feature or block of features, instead of all, during an iteration and thereby helping to solve problems with large number of features. In this paper, an op- timization framework, namely, Batch Block Optimization Framework has been developed to solve big data problems using the best of Stochastic Approximation as well as the best of Coordinate Descent approaches, independent of any solver. This framework is used to solve strongly convex and smooth empirical risk minimization problem with gradient de- scent (as a solver) and two novel Stochastic Average Adjusted Gradient methods have been proposed to reduce variance in mini-batch and block-coordinate setting of the developed framework. Theoretical analysis prove linear convergence of the proposed methods and empirical results with bench marked datasets prove the superiority of proposed methods against existing methods.
SAAGs: Biased Stochastic Variance Reduction Methods

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Source: https://analytixon.com/2021/04/22/if-you-did-not-already-know-1376/

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Big Data

Canada judge rules to delay Huawei CFO’s extradition hearings

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By Moira Warburton

VANCOUVER (Reuters) – A Canada judge has agreed to delay Huawei Chief Financial Officer Meng Wanzhou’s U.S. extradition hearings for three months, according to a ruling read in court on Wednesday, handing her defense team a win.

Meng, 49, was arrested at Vancouver International Airport on charges of bank fraud in the United States for allegedly misleading HSBC about Huawei’s business dealings in Iran, causing the bank to break U.S. sanctions.

Meng’s team had asked for more time to review additional documents that became available after HSBC and Huawei reached a settlement in Hong Kong. Extradition hearings were originally set to wrap up in May.

Defense attorney Richard Peck argued in court on Monday that they were requesting “a modest frame of time” to be able to read the documents and potentially file them as evidence in the British Columbia Supreme Court.

Lawyers representing the attorney general of Canada had fought the adjournment of hearings set to start on Monday, arguing that Meng’s team had been given more time than was usual in an extradition to make their case, and the contents of the documents were too redacted to be relied upon as significant to the case.

“The outstanding feature of this application is that it’s based on speculation,” prosecutor Robert Frater said on Monday.

But Associate Chief Justice Heather Holmes disagreed, siding with the defense in granting an adjournment.

Her reasons will be read out on in court on April 28.

(Reporting by Moira Warburton in Vancouver; Editing by Chris Reese and Marguerita Choy)

Image Credit: Reuters

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Source: https://datafloq.com/read/canada-judge-rules-delay-huawei-cfos-extradition-hearings/14122

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Here´s why crypto is the investment opportunity of a lifetime

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The rapidly developing crypto market is disrupting the world — and investors are finally waking up to it.

In the last year alone, the value of the crypto market has skyrocketed from $205 billion to $2 trillion. But the growth is far from over: The opportunity is massive.

The Value of the Crypto Market Is Skyrocketing

crypto market value 2020-2021

(Source: CoinMarketCap.)

Cryptos will become a critical component of the financial service, health care, logistics and real estate industries, just to name a few.

Powered by blockchain technology, cryptos solve many of the problems that nag these sectors. And with $45 trillion in combined revenues annually among the four, crypto has a large runway for growth…

WELCOME TO THE CRYPTO REVOLUTION

Financial services present the most obvious use case for cryptos. By removing middlemen, cryptos are democratizing finance and already transforming services such as lending and banking.

The traditional lending process is inefficient and prohibitive for institutions and consumers. It takes an average of three to four weeks to receive a credit card after approval, if approved at all. Mortgage closings take even longer, averaging 42 days according to Ellie Mae. This limits business for institutions and prevents timely purchases for consumers. Crypto-based lending can dramatically speed up this process, while potentially offering better financing terms.

Cryptos are also eliminating barriers present in the traditional banking system. The current method of transferring money across the globe is burdensome. The fastest method of transfer has historically been a money wire, but these can take up to five days to clear before the funds are usable. Crypto, on the other hand, enables transfers that are practically instantaneous, with funds that are made available right away.

Health care is another candidate for disruption. Smart contract cryptos will enable faster transactions, drug and medical device tracking, and streamlined medical data management.

Smart contracts operate without manual oversight and can ensure hospital supply orders are filled, paid and shipped quickly. This is favorable over typical transactions which require multiple steps such as signatures, invoices and payments before deliveries are made.

Cryptos can also contribute to increased patient engagement and foster greater connection between patients and providers. In return for tracking health care data, patients can receive crypto token incentives. This helps providers to better understand their patients and promotes patient adherence. Patients can leave reviews for health care services and receive crypto tokens in return, which can then be used toward future health care services.

Similar to the way cryptos streamline health care transactions, they can also make the logistics cycle more efficient. Smart contract cryptos enable supply chain transparency while securing and streamlining agreement terms, record keeping and payments. By eliminating middle men, cryptos can also save money for all parties involved.

The opportunity for cryptocurrencies in logistics is so huge that logistics leader DHL covered the topic in its Blockchain in Logistics report.

Cryptos are also opening the door for real estate investing on a large scale. Investing in real estate beyond primary residences has historically been limited to a select group of people with a lot of cash. Cryptos are breaking down this barrier.

Investors can exchange cryptos for fractional ownership stakes in hard assets such as real estate in a process known as tokenization. This allows investors to diversify their real estate portfolios and also improves liquidity, meaning you can buy and sell more easily.

THE CRYPTO FUTURE IS NOW

The use cases for cryptos that I’ve outlined above are just a few of many. In reality, cryptocurrencies will likely be influential in every industry in some way. And this influence is growing faster than you may realize.

With each new development, the opportunity in crypto grows stronger. But there are now over 9,000 tradeable cryptocurrencies, which means picking the right ones can be challenging.

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Source: https://www.fintechnews.org/heres-why-crypto-is-the-investment-opportunity-of-a-lifetime/

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