Article originally posted on InfoQ. Visit InfoQ

The Linux Foundation announced today that it would host the AsyncAPI Initiative. It will provide a forum where individuals and organizations can advance AsyncAPI and nurture collaboration in a neutral platform that can support the growth that AsyncAPI is experiencing.

Fran Méndez, the founder of AsyncAPI, commented about the move:

As the growth of AsyncAPI skyrocketed, it became clear to us that we needed to find a neutral, trusted home for its ongoing development. The Linux Foundation is without question the leader in bringing together interested communities to advance technology and accelerate adoption in an open way. This natural next step for the project represents the maturity and strength of AsyncAPI. We expect the open governance model architected and standardized by the Linux Foundation will ensure the initiative continues to thrive.

“AsyncAPI joining the Linux Foundation is the final cornerstone in the foundation of the open-source event-driven API specification,” said Kin Lane, Chief Evangelist at Postman. “Laying the foundation for defining the next generation of API infrastructure beginning with HTTP request and response APIs, but also event-driven approaches spanning multiple protocols and patterns including Kafka, GraphQL, MQTT, AMQP, and much more. Providing what is needed to power documentation, mocking, testing, and other critical stops along a modern enterprise API lifecycle.” AsyncAPI recently announced a partnership with Postman to boost the development of Asynchronous APIs.

AsyncAPI is an open specification meant to be an industry standard for defining asynchronous APIs. It helps unify documentation automation and code generation and manages, testing, and monitoring asynchronous APIs. The specification provides a language for describing event-driven systems’ interface regardless of the underlying technology and supports event-driven architecture’s complete development cycle. Currently, AsyncAPI is in production at Adidas, PayPal, Salesforce, SAP, Slack, and others.

InfoQ spoke with Chris Aniszczyk, VP Developer Relations at The Linux Foundation, about the announcement.

InfoQ: Why did the Linux Foundation choose to support the AsyncAPI initiative?

First, The Linux Foundation already hosts various API-related projects and organizations like the OpenAPI Initiative and the GraphQL Foundation, so the AsyncAPI Initiative has a natural fit next to other widely used API-related technologies. Second, the Linux Foundation provides various services to accelerate the growth of specifications, from our ability to propose international standards to supporting our projects through services such as events and mentorships.

InfoQ: What can developers expect from the cooperation between AsyncAPI and the Linux Foundation?

In the beginning, we will ensure that the community has a neutral home for all of its assets as the AsyncAPI project is more significant than just one company or individual. We will work closely with the AsyncAPI community and collaborate on a plan to grow its impact. The initial steps will encourage the community to offer mentorships to expand the contributor base and collaborate with other Linux Foundation-related API projects through venues like the API Specifications Conference coming up in September 2021.

InfoQ: Would you recommend that organizations dealing with asynchronous communication invest their time in AsyncAPI? Why?

If your organization isn’t dealing with asynchronous communication at scale, then you may not need to worry about taking advantage of AsyncAPI. However, with the rise of cloud-native architectures and microservices, we see more event-driven architectures.

Also, REST APIs have a fantastic standardized documentation solution via OpenAPI, which has allowed a wonderful ecosystem of tooling to improve developer efficiency. Event-focused APIs are different and need a more optimal solution that covers the caveats of event-driven architectures.

This is where AsyncAPI fits in and aims to offer the same standardization that has driven powerful tools and developer efficiency in the OpenAPI ecosystem, on top of aiming for some basic compatibility with OpenAPI schemas. The AsyncAPI Initiative has excellent documentation available for folks to learn more about event-driven architectures and what advantages there may be, especially if you’re familiar with OpenAPI already.

The Linux Foundation is supported by more than 1,000 members and is a hub for collaboration on open source software, open standards, open data, and open hardware. Linux Foundation’s projects include Linux, Kubernetes, Node.js, and more. The Linux Foundation also hosts the OpenAPI Initiative, which focuses on synchronous REST. It is considered a sister project of the event-driven AsyncAPI Initiative.

Article originally posted on Data Science Central. Visit Data Science Central

Big data and data analytics have become a part of our everyday lives. From online shopping to entertainment to speech recognition programs like Siri, data is being used in most situations. 

Data and data analytics continue to change how businesses operate, and we have seen how data has improved industry sectors like logistics, financial services, and even healthcare. 

So how can you use data and data analytics to improve your golf game? 

It Can Perfect Your Swing 

Having proper posture on your swing helps maintain balance and allows a golfer to hit the ball squarely in the center of the club. A good setup can help a golfer control the direction of the shot and create the power behind it. 

Using big data and data analytics, you’re able to analyze your swing and identify areas they could improve upon. This allows you to understand how your shoulder tilts at the top of every swing and when it connects with the ball, and your hip sways when the club hits the ball. 

All this information can help a golfer see where their swing is angled and how the ball moves. This will help identify areas that can be worked on, leading to better balance, a better setup, and a sound golf swing. 

It Can Help You Get More Distance on the Ball 

Every golfer would love to have more distance on the ball, and it’s completely possible to gain that extra distance.  Golfers can use data to get the following information: 

• Swing speed
• Tempo
• Backswing position
• % of greens hit

By using data analytics, you’d be able to tell which part of the clubface you’re striking the ball with or if you’re hitting more towards the toe or heel. You’ll also get a better understanding of your shaft lean, which can help you get your shaft leaning more forward. This can help you gain distance just by improving your impact. 

When it comes to tempo, analytics can help you gain more speed in your backswing so that you get an increase in speed in your downswing. This will lead to more speed and can help you gain more distance.

The goal of using data is to get the golfer to swing the club faster without swinging out of control. 

How Can You Track Your Data?

There are a few ways in which a golfer can track and analyze their golf swing. The first is by attaching golf sensors like the Arccos Caddie Smart Sensors to your golf clubs. 

This will record the golfer’s swing speed, tempo, and backswing position on every club used on every hole. Once you’re done with the round of golf, you’d upload the information to your PC, and this would give the golfer the statistics of their game.

You can also use your mobile phone to record your swing shot and then use an app like V1 to analyze the video. This will allow you to see your down line or front line and show you the swing angle. 

You can also use golf simulators like Optishot, which has 32 sensors and tracks both the swing and your face. It’s also pre-loaded with key data points to track your swing speed, tempo, and backswing position. This simulator also lets you play golf against your friends online. 

Benefits of Using Data in Golf

Practice will help your game improve, but our daily lifestyles don’t always allow us to practice regularly. Using data, you’re getting unbiased feedback, which allows a golfer to evaluate their strengths and weaknesses. 

This will allow you to customize your practice time to what you need to focus on, making sure you make efficient use of the practice time. You can also set realistic goals where you can track and measure your progress. 

Conclusion 

Big data is here to stay, and it’s found its way into almost every aspect of life. Why not include it in your golf game if you’re looking for a way to improve and make more efficient use of your practice time? 

Author bio:

Jordan Fuller is a retired golfer, mentor, and coach. He also owns a golf publication site, https://www.golfinfluence.com/, where he writes about a lot of stuff on golf. 

Article originally posted on Data Science Central. Visit Data Science Central

Big data and data analytics have become a part of our everyday lives. From online shopping to entertainment to speech recognition programs like Siri, data is being used in most situations. 

Data and data analytics continue to change how businesses operate, and we have seen how data has improved industry sectors like logistics, financial services, and even healthcare. 

So how can you use data and data analytics to improve your golf game? 

It Can Perfect Your Swing 

Having proper posture on your swing helps maintain balance and allows a golfer to hit the ball squarely in the center of the club. A good setup can help a golfer control the direction of the shot and create the power behind it. 

Using big data and data analytics, you’re able to analyze your swing and identify areas they could improve upon. This allows you to understand how your shoulder tilts at the top of every swing and when it connects with the ball, and your hip sways when the club hits the ball. 

All this information can help a golfer see where their swing is angled and how the ball moves. This will help identify areas that can be worked on, leading to better balance, a better setup, and a sound golf swing. 

It Can Help You Get More Distance on the Ball 

Every golfer would love to have more distance on the ball, and it’s completely possible to gain that extra distance.  Golfers can use data to get the following information: 

• Swing speed
• Tempo
• Backswing position
• % of greens hit

By using data analytics, you’d be able to tell which part of the clubface you’re striking the ball with or if you’re hitting more towards the toe or heel. You’ll also get a better understanding of your shaft lean, which can help you get your shaft leaning more forward. This can help you gain distance just by improving your impact. 

When it comes to tempo, analytics can help you gain more speed in your backswing so that you get an increase in speed in your downswing. This will lead to more speed and can help you gain more distance.

The goal of using data is to get the golfer to swing the club faster without swinging out of control. 

How Can You Track Your Data?

There are a few ways in which a golfer can track and analyze their golf swing. The first is by attaching golf sensors like the Arccos Caddie Smart Sensors to your golf clubs. 

This will record the golfer’s swing speed, tempo, and backswing position on every club used on every hole. Once you’re done with the round of golf, you’d upload the information to your PC, and this would give the golfer the statistics of their game.

You can also use your mobile phone to record your swing shot and then use an app like V1 to analyze the video. This will allow you to see your down line or front line and show you the swing angle. 

You can also use golf simulators like Optishot, which has 32 sensors and tracks both the swing and your face. It’s also pre-loaded with key data points to track your swing speed, tempo, and backswing position. This simulator also lets you play golf against your friends online. 

Benefits of Using Data in Golf

Practice will help your game improve, but our daily lifestyles don’t always allow us to practice regularly. Using data, you’re getting unbiased feedback, which allows a golfer to evaluate their strengths and weaknesses. 

This will allow you to customize your practice time to what you need to focus on, making sure you make efficient use of the practice time. You can also set realistic goals where you can track and measure your progress. 

Conclusion 

Big data is here to stay, and it’s found its way into almost every aspect of life. Why not include it in your golf game if you’re looking for a way to improve and make more efficient use of your practice time? 

Author bio:

Jordan Fuller is a retired golfer, mentor, and coach. He also owns a golf publication site, https://www.golfinfluence.com/, where he writes about a lot of stuff on golf. 

Posted on mongodb google news. Visit mongodb google news

Chicago, United States: –  The report comes out as an intelligent and thorough assessment tool as well as a great resource that will help you to secure a position of strength in the global Document Databases Software Market. It includes Porter’s Five Forces and PESTLE analysis to equip your business with critical information and comparative data about the Global Document Databases Software Market. We have provided deep analysis of the vendor landscape to give you a complete picture of current and future competitive scenarios of the global Document Databases Software market. Our analysts use the latest primary and secondary research techniques and tools to prepare comprehensive and accurate market research reports.

Top Key players cited in the report: MongoDB, Amazon, ArangoDB, Azure Cosmos DB, Couchbase, MarkLogic, RethinkDB, CouchDB, SQL-RD, OrientDB, RavenDB, Redis

Get PDF Sample Copy of this Report to understand the structure of the complete report: (Including Full TOC, List of Tables & Figures, Chart)

The final report will add the analysis of the Impact of Covid-19 in this report Document Databases Software Market

Document Databases Software Market reports offers important insights which help the industry experts, product managers, CEOs, and business executives to draft their policies on various parameters including expansion, acquisition, and new product launch as well as analyzing and understanding the market trends.

Each segment of the global Document Databases Software market is extensively evaluated in the research study. The segmental analysis offered in the report pinpoints key opportunities available in the global Document Databases Software market through leading segments. The regional study of the global Document Databases Software market included in the report helps readers to gain a sound understanding of the development of different geographical markets in recent years and also going forth. We have provided a detailed study on the critical dynamics of the global Document Databases Software market, which include the market influence and market effect factors, drivers, challenges, restraints, trends, and prospects. The research study also includes other types of analysis such as qualitative and quantitative.

Global Document Databases Software Market: Competitive Rivalry

The chapter on company profiles studies the various companies operating in the global Document Databases Software market. It evaluates the financial outlooks of these companies, their research and development statuses, and their expansion strategies for the coming years. Analysts have also provided a detailed list of the strategic initiatives taken by the Document Databases Software market participants in the past few years to remain ahead of the competition.

 Global Document Databases Software Market: Regional Segments

The chapter on regional segmentation details the regional aspects of the global Document Databases Software market. This chapter explains the regulatory framework that is likely to impact the overall market. It highlights the political scenario in the market and the anticipates its influence on the global Document Databases Software market.

• The Middle East and Africa (GCC Countries and Egypt)
• North America (the United States, Mexico, and Canada)
• South America (Brazil etc.)
• Europe (Turkey, Germany, Russia UK, Italy, France, etc.)
• Asia-Pacific (Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia)

Request For Customization: https://www.reporthive.com/request_customization/2384554

Report Highlights

• Comprehensive pricing analysis on the basis of product, application, and regional segments

• The detailed assessment of the vendor landscape and leading companies to help understand the level of competition in the global Document Databases Software market

• Deep insights about regulatory and investment scenarios of the global Document Databases Software market

• Analysis of market effect factors and their impact on the forecast and outlook of the global Document Databases Software market

• A roadmap of growth opportunities available in the global Document Databases Software market with the identification of key factors

• The exhaustive analysis of various trends of the global Document Databases Software market to help identify market developments

Table of Contents

Report Overview: It includes six chapters, viz. research scope, major manufacturers covered, market segments by type, Document Databases Software market segments by application, study objectives, and years considered.

Global Growth Trends: There are three chapters included in this section, i.e. industry trends, the growth rate of key producers, and production analysis.

Document Databases Software Market Share by Manufacturer: Here, production, revenue, and price analysis by the manufacturer are included along with other chapters such as expansion plans and merger and acquisition, products offered by key manufacturers, and areas served and headquarters distribution.

Market Size by Type: It includes analysis of price, production value market share, and production market share by type.

Market Size by Application: This section includes Document Databases Software market consumption analysis by application.

Profiles of Manufacturers: Here, leading players of the global Document Databases Software market are studied based on sales area, key products, gross margin, revenue, price, and production.

Document Databases Software Market Value Chain and Sales Channel Analysis: It includes customer, distributor, Document Databases Software market value chain, and sales channel analysis.

Market Forecast – Production Side: In this part of the report, the authors have focused on production and production value forecast, key producers forecast, and production and production value forecast by type.

Get Free Sample Copy of this report: https://www.reporthive.com/request_sample/2384554

About Us:
Report Hive Research delivers strategic market research reports, statistical survey, and Industry analysis and forecast data on products and services, markets and companies. Our clientele ranges mix of United States Business Leaders, Government Organizations, SME’s, Individual and Start-ups, Management Consulting Firms, and Universities etc. Our library of 600,000+ market reports covers industries like Chemical, Healthcare, IT, Telecom, Semiconductor, etc. in the USA, Europe Middle East, Africa, Asia Pacific. We help in business decision-making on aspects such as market entry strategies, market sizing, market share analysis, sales and revenue, technology trends, competitive analysis, product portfolio and application analysis etc.

https://bisouv.com/

Article originally posted on mongodb google news. Visit mongodb google news

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Chicago, United States: –  The report comes out as an intelligent and thorough assessment tool as well as a great resource that will help you to secure a position of strength in the global NoSQL Databases Software Market. It includes Porter’s Five Forces and PESTLE analysis to equip your business with critical information and comparative data about the Global NoSQL Databases Software Market. We have provided deep analysis of the vendor landscape to give you a complete picture of current and future competitive scenarios of the global NoSQL Databases Software market. Our analysts use the latest primary and secondary research techniques and tools to prepare comprehensive and accurate market research reports.

Top Key players cited in the report: MongoDB, Amazon, ArangoDB, Azure Cosmos DB, Couchbase, MarkLogic, RethinkDB, CouchDB, SQL-RD, OrientDB, RavenDB, Redis

Get PDF Sample Copy of this Report to understand the structure of the complete report: (Including Full TOC, List of Tables & Figures, Chart)

The final report will add the analysis of the Impact of Covid-19 in this report NoSQL Databases Software Market

NoSQL Databases Software Market reports offers important insights which help the industry experts, product managers, CEOs, and business executives to draft their policies on various parameters including expansion, acquisition, and new product launch as well as analyzing and understanding the market trends.

Each segment of the global NoSQL Databases Software market is extensively evaluated in the research study. The segmental analysis offered in the report pinpoints key opportunities available in the global NoSQL Databases Software market through leading segments. The regional study of the global NoSQL Databases Software market included in the report helps readers to gain a sound understanding of the development of different geographical markets in recent years and also going forth. We have provided a detailed study on the critical dynamics of the global NoSQL Databases Software market, which include the market influence and market effect factors, drivers, challenges, restraints, trends, and prospects. The research study also includes other types of analysis such as qualitative and quantitative.

Global NoSQL Databases Software Market: Competitive Rivalry

The chapter on company profiles studies the various companies operating in the global NoSQL Databases Software market. It evaluates the financial outlooks of these companies, their research and development statuses, and their expansion strategies for the coming years. Analysts have also provided a detailed list of the strategic initiatives taken by the NoSQL Databases Software market participants in the past few years to remain ahead of the competition.

 Global NoSQL Databases Software Market: Regional Segments

The chapter on regional segmentation details the regional aspects of the global NoSQL Databases Software market. This chapter explains the regulatory framework that is likely to impact the overall market. It highlights the political scenario in the market and the anticipates its influence on the global NoSQL Databases Software market.

• The Middle East and Africa (GCC Countries and Egypt)
• North America (the United States, Mexico, and Canada)
• South America (Brazil etc.)
• Europe (Turkey, Germany, Russia UK, Italy, France, etc.)
• Asia-Pacific (Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia)

Request For Customization: https://www.reporthive.com/request_customization/2384652

Report Highlights

• Comprehensive pricing analysis on the basis of product, application, and regional segments

• The detailed assessment of the vendor landscape and leading companies to help understand the level of competition in the global NoSQL Databases Software market

• Deep insights about regulatory and investment scenarios of the global NoSQL Databases Software market

• Analysis of market effect factors and their impact on the forecast and outlook of the global NoSQL Databases Software market

• A roadmap of growth opportunities available in the global NoSQL Databases Software market with the identification of key factors

• The exhaustive analysis of various trends of the global NoSQL Databases Software market to help identify market developments

Table of Contents

Report Overview: It includes six chapters, viz. research scope, major manufacturers covered, market segments by type, NoSQL Databases Software market segments by application, study objectives, and years considered.

Global Growth Trends: There are three chapters included in this section, i.e. industry trends, the growth rate of key producers, and production analysis.

NoSQL Databases Software Market Share by Manufacturer: Here, production, revenue, and price analysis by the manufacturer are included along with other chapters such as expansion plans and merger and acquisition, products offered by key manufacturers, and areas served and headquarters distribution.

Market Size by Type: It includes analysis of price, production value market share, and production market share by type.

Market Size by Application: This section includes NoSQL Databases Software market consumption analysis by application.

Profiles of Manufacturers: Here, leading players of the global NoSQL Databases Software market are studied based on sales area, key products, gross margin, revenue, price, and production.

NoSQL Databases Software Market Value Chain and Sales Channel Analysis: It includes customer, distributor, NoSQL Databases Software market value chain, and sales channel analysis.

Market Forecast – Production Side: In this part of the report, the authors have focused on production and production value forecast, key producers forecast, and production and production value forecast by type.

Get Free Sample Copy of this report: https://www.reporthive.com/request_sample/2384652

About Us:
Report Hive Research delivers strategic market research reports, statistical survey, and Industry analysis and forecast data on products and services, markets and companies. Our clientele ranges mix of United States Business Leaders, Government Organizations, SME’s, Individual and Start-ups, Management Consulting Firms, and Universities etc. Our library of 600,000+ market reports covers industries like Chemical, Healthcare, IT, Telecom, Semiconductor, etc. in the USA, Europe Middle East, Africa, Asia Pacific. We help in business decision-making on aspects such as market entry strategies, market sizing, market share analysis, sales and revenue, technology trends, competitive analysis, product portfolio and application analysis etc.

https://bisouv.com/

Article originally posted on InfoQ. Visit InfoQ

Scientists at Google Research have announced APOLLO, a framework for optimizing AI accelerator chip designs. APOLLO uses evolutionary algorithms to select chip parameters that minimize deep-learning inference latency while also minimizing chip area. Using APOLLO, researchers found designs that achieved 24.6% speedup over those chosen by a baseline algorithm.

Research Scientist Amir Yazdanbakhsh gave a high level overview of the system in a recent blog post. APOLLO searches for a set of hardware parameters, such memory size, I/O bandwidth, and processor units, that provides the best inference performance for a given deep-learning model. By using evolutionary algorithms and transfer learning, APOLLO can efficiently explore the space of parameters, reducing the overall time and cost of producing the design. According to Yazdanbakhsh,

We believe that this research is an exciting path forward to further explore ML-driven techniques for architecture design and co-optimization (e.g., compiler, mapping, and scheduling) across the computing stack to invent efficient accelerators with new capabilities for the next generation of applications.

Deep-learning models have been developed for a wide variety of problems, from computer vision (CV) to natural language processing (NLP). However, these models often require large amounts of compute and memory resources at inference time, straining the hardware constraints of edge and mobile devices. Custom accelerator hardware, such as Edge TPUs, can improve model inference latency, but often require modifications to the model, such as parameter quantization or model pruning. Some researchers, including a team at Google, have proposed using AutoML to design high-performance models targeted for specific accelerator hardware.

The APOLLO team’s strategy, by contrast, is to customize the accelerator hardware to optimize performance for a given deep-learning model. The accelerator is based on a 2D array of processing elements (PEs), each of which contains a number of single instruction multiple data (SIMD) cores. This basic pattern can be customized by choosing values for several different parameters, including the size of the PE array, the number of cores per PE, and the amount of memory per core. Overall, there are nearly 500M parameter combinations in the design space. Because a proposed accelerator design must be simulated in software, evaluating its performance on a deep-learning model is time and compute intensive.

APOLLO builds on Google’s internal Vizier “black box” optimization tool, and Vizier’s optimization Bayesian method is used as a baseline comparison for evaluating APOLLO’s performance. The APOLLO framework supports several optimization strategies, including random search, model-based optimization, evolutionary search, and an ensemble method called population-based black-box optimization (P3BO). The Google team performed several experiments, searching for optimal accelerator parameters for a set of CV models, including MobileNetV2 and MobileNetEdge, for three different chip-area constraints. They found that the P3BO algorithm produced the best designs and its performance improved compared to Vizier as available chip area decreased. Compared to a manually-guided exhaustive or “brute-force” search, P3BO found a better configuration while performing 36% fewer search evaluations.

The design of accelerator hardware for improving AI inference is an active research area. Apple’s new M1 processor includes a neural engine designed to speed up AI computations. Stanford researchers recently published an article in Nature describing a system called Illusion that uses a network of smaller chips to emulate a single larger accelerator. At Google, scientists have also published work on optimizing chip floorplanning, to find the best placement of integrated-circuit components on the physical chip.

Article originally posted on Data Science Central. Visit Data Science Central

The use of Big Data as an insight-generating engine has opened up new job opportunities in the market with Data scientists being in high demand at the enterprise level across all industry verticals. Organizations have started to bet on the data scientist and their skills to maintain, expand, and remain one up from their competition, whether it’s optimizing the product creation process, increasing customer engagement, or mining data to identify new business opportunities.

The year 2021 is the year for data science, I bet you. As the demand for qualified professionals shoots up, a growing number of people are enrolling in data science courses. You’ll also need to develop a collection of skills if you want to work as a data scientist in 2021. In this post, we will be discussing the important skills to have to be a good data scientist in the near future.

But first what is data science?

The Data Science domain is majorly responsible for all of the massive databases, as well as figuring out how to make them useful and incorporating them into real-world applications. With its numerous industry, science, and everyday-life benefits, digital data is considered one of the most important technological advancements of the twenty-first century. 

Data Scientists’ primary task is to sift through a wide variety of data. They are adept at providing crucial information, which opens the path for better decision-making. Most businesses nowadays have become the flag bearers of data science and make use of it. It is a defined data science precisely. In a larger context, data science entails the retrieval of clean data from raw data, as well as the study of these datasets to make sense of them, or, in most terms, the visualization of meaningful and actionable observations.

What is a Data Scientist, and how can one become one?

Extracting and processing vast quantities of data to identify trends and that can support people, enterprises, and organizations are among the duties of a Data Scientist. They employ sophisticated analytics and technologies, including statistical models and deep learning, as well as a range of analytics techniques. Reporting and visualization software is used to show data mining perspectives, which aids in making better customer-oriented choices and considering potential sales prospects, among other things.

Now let’s find out how to get started with Data science

First thing first, start with the basics

Though not a complicated step, but still many people skip it, because- math.

Understanding how the algorithms operate requires one to have a basic understanding of secondary-level mathematics.

Linear Algebra, Calculus, Permutation and Combination, and Gradient Descent are all concerned. 

No matter how much you despise this subject, it is one of the prerequisites and you must make sure to go through them to have a better standing in the job market.

Learn Programming Language

R and Python are the most widely used programming languages. You should start experimenting with the software and libraries for Analytics in any language. Basic programming principles and a working knowledge of data structures are important.

Python has rapidly risen to the top of the list of most common and practical programming languages for data scientists. However, it is not the only language in which data scientists can work.

The more skills you have, the more programming languages you will learn; however, which one do you choose?

The following are the most important ones:

• JavaScript 
• SQL (Structured Query Language)
• Java 
• Scala is a programming language.

Read regarding the advantages and disadvantages in both — as well as where they’re more often found — before deciding which would fit better with your ventures.

Statistics and Probability

Data science employs algorithms to collect knowledge and observations and then makes data-driven decisions. As a result, things like forecasting, projecting, and drawing inferences are inextricably linked to the work.

The data industry’s cornerstone is statistics. Your mathematical abilities would be put to the test in every career interview. 

Probability and statistics are fundamental to data science, and they’ll assist you in generating predictions for data processing by allowing you in:

• Data exploration and knowledge extraction
• Understanding the connections between two variables
• Anomalies of data sets are discovered.
• Future trend analysis based on historical evidence

Data Analysis

The majority of Data Scientists’ time is spent cleaning and editing data rather than applying Machine Learning in most professions.

The most critical aspect of the work is to understand the data and look for similarities and associations. It will give you an idea of the domain as well as which algorithm to use for this sort of query.

‘Pandas’ and ‘Numpy’, two popular Python data analysis applications, are also popular.

Data Visualization 

Clients and stakeholders would be confused by the mathematical jargon and the Model’s forecasts. Data visualization is essential for presenting patterns in a graphic environment using different charts and graphs to illustrate data and study behavior.

Without a question, data visualization is one of the most essential skills for interpreting data, learning about its different functions, and eventually representing the findings. It also assists in the retrieval of specific data information that can be used to create the model.

Machine learning

Machine learning will almost always be one of the criteria for most data scientist work. There’s no denying Machine learning’s influence. And it’s just going to get more and more common in the coming years.

It is unquestionably a skill to which you can devote time (particularly as data science becomes increasingly linked to machine learning). And the combination of these two inventions is yielding some fascinating, leading-edge insights and innovations that will have a big effect on the planet.

Business Knowledge

Data science necessitates more than just technological abilities. They are, without a doubt, necessary. However, when employed in the IT field, don’t forget about market awareness, as driving business value is an important aspect of data science.

As a data scientist, you must have a thorough understanding of the industry in which your firm works. And you need to know what challenges your company is trying to fix before you can suggest new ways to use the results.

Soft Skills

As a data scientist, you are responsible for not only identifying accurate methods to satisfy customer demands, but also for presenting that information to the company’s customers, partners, and managers in simple terms so that they understand and follow your process. As a result, if you want to take on responsibilities for some vital projects that are critical to your business, you’ll need to improve your communication skills.

Final Thoughts

As the number of people interested in pursuing a career in data science increases, it is crucial that you master the fundamentals, set a firm base, and continue to improve and succeed throughout your journey.

Now that you’ve got the run, the next step is to figure out how to learn Data Science. Global Tech Council certification courses are a common option since they are both short-term and flexible. The data analytics certification focuses on the information and skills you’ll need to get a job, all bundled in a versatile learning module that suits your schedule. It’s about time you start looking for the best online data science courses that meet your requirements and catapult you into a dazzling career.

Article originally posted on Data Science Central. Visit Data Science Central

The organization of knowledge on the basis of semantic knowledge models is a prerequisite for an efficient knowledge exchange. A well-known counter-example are individual folder systems or mind maps for the organization of files. This approach to knowledge organization only works at the individual level and is not scalable because it is full of implicit semantics that can only be understood by the author himself.

To organize knowledge well, we should therefore use established knowledge organization systems (KOS) to model the underlying semantic structure of a domain. Many of these methods have been developed by librarians to classify and catalog their collections, and this area has seen massive changes due to the spread of the Internet and other network technologies, leading to the convergence of classical methods of library science and from the web community.

When we talk about KOSs today, we primarily mean Networked Knowledge Organization Systems (NKOS). NKOS are systems of knowledge organization such as glossaries, authority files, taxonomies, thesauri and ontologies. These support the description, validation and retrieval of various data and information within organizations and beyond their boundaries.

Let’s take a closer look: Which KOS is best for which scenario? KOS differ mainly in their ability to express different types of knowledge building blocks. Here is a list of these building blocks and the corresponding KOS.

The Simple Knowledge Organization System (SKOS), a widely used standard specified by the World Wide Web Consortium (W3C), combines numerous knowledge building blocks under one roof. Using SKOS, all knowledge from lines 1–4 can be expressed and linked to facts based on other ontologies.

Knowledge organization systems make the meaning of data or documents, i.e., their semantics, explicit and thus accessible, machine-readable and transferable. This is not the case when someone places files on their desktop computer in a folder called “Photos-CheeseCake-January-4711” or uses tags like “CheeseCake4711” to classify digital assets. Instead of developing and applying only personal, i.e., implicit semantics, that may still be understandable to the author, NKOS and ontologies take a systemic approach to knowledge organization.

Basic Principles of Semantic Knowledge Modeling

Semantic knowledge modeling is similar to the way people tend to construct their own models of the world. Every person, not just subject matter experts, organizes information according to these ten fundamental principles:

1. Draw a distinction between all kinds of things: ‘This thing is not that thing.’
2. Give things names: ‘This thing is a cheese called Emmental’ (some might call it Emmentaler or Swiss cheese, but it’s still the same thing).
3. Create facts and relate things to each other: ‘Emmental is made with cow’s milk’, Cow’s milk is obtained from cows’, etc.
4. Classify things: ‘This thing is a cheese, not a ham.’
5. Create general facts and relate classes to each other: ‘Cheese is made from milk.’
6. Use various languages for this; e.g., the above-mentioned fact in German is ‘Emmentaler wird aus Kuhmilch hergestellt’ (remember: the thing called ‘Kuhmilch’ is the same thing as the thing called ‘cow’s milk’—it’s just that the name or label for this thing that is different in different languages).
7. Putting things into different contexts: this mechanism, called “framing” in the social sciences, helps to focus on the facts that are important in a particular situation or aspect. For example, as a nutritional scientist, you are more interested in facts about Emmental cheese compared to, for example, what a caterer would like to know. (With named graphs you can represent this additional context information and add another dimensionality to your knowledge graph.)
8. If things with different URIs from the same graph are actually one and the same thing, merging them into one thing while keeping all triples is usually the best option. The URI of the deprecated thing must remain permanently in the system and from then on point to the URI of the newly merged thing.
9. If things with different URIs contained in different (named) graphs actually seem to be one and the same thing, mapping (instead of merging) between these two things is usually the best option.
10. Inferencing: generate new relationships (new facts) based on reasoning over existing triples (known facts).

Many of these steps are supported by software tools. Steps 7–10 in particular do not have to be processed manually by knowledge engineers, but are processed automatically in the background. As we will see, other tasks can also be partially automated, but it will by no means be possible to generate knowledge graphs fully automatically. If a provider claims to be able to do so, no knowledge graph will be generated, but a simpler model will be calculated, such as a co-occurrence network.

Article originally posted on Data Science Central. Visit Data Science Central

How extensive must data sets be to be considered as big data? For some, a slightly larger Excel spreadsheet is “big data”. Fortunately, there are certain characteristics that allow us to describe big data pretty well.

According to IBM, 90% of the data that exists worldwide today was created in the last 2 years alone. Big Data Analysis in Healthcare could be helpful in many ways. For example, such analyzes may also counteract the spread of diseases and optimize the needs-based supply of medicinal products and medical devices.

In this article, we will define what is Big Data and discuss ways it could be applied in Healthcare.

Big data definition

The easiest way to say is: Big data is data that can no longer only be processed by one computer. They are so big that you have to store and edit them piece by piece on several servers.

A short definition can also be expressed by three Vs:

1. Volume – describes the size of the data
2. Variety – a variety of data
3. Velocity – the speed of the data

Volume – The Size of Data

As I said before, big data is most easily described by its sheer volume and complexity. These properties do not allow big data to be stored or processed on just one computer. For this reason, this data is stored and processed in specially developed software ecosystems, such as Hadoop.

Variety – Data Diversity

Mass data is very diverse and can also be structured, unstructured or semi-structured.

These data also mostly have different sources. For example, a bank could store transfer data from its customers, but also recordings of telephone conversations made by its customer support staff.

In principle, it makes sense to save data in the format in which it was recorded. The Hadoop Framework enables companies to do just that: the data is saved in the format in which it was recorded.

With Hadoop, there is no need to convert customer call data into text files. They can be saved directly as audio calls. However, the use of conventional database structures is then also not possible.

Velocity – The Speed of Data

This is about the speed at which the data is saved.

It is often necessary that data be stored in real-time. For companies like Zalando or Netflix, it is thus possible to offer their customers product recommendations in real-time.

Big Data Implementation in the Healthcare

There are three most obvious, but fundamentally revolutionizing ways of Big Data usage coupled with artificial intelligence.

1. On the one hand, the monitoring. Significant deviations in essential body data will be automatically enhanced in the future: Is the increased pulse a normal sequence of the staircase just climbed? Or does he point to cardiovascular disease in combination with other data and history? Thus, diseases can be detected in their early stages and treated effectively.

1. Diagnosis is the second one. Where it depends almost exclusively on the knowledge and the analysis capacity of the doctor, whether, for example, the cancer metastasis on the X-ray image is recognized as such, the doctor will use artificially intelligent systems, which become a little smarter with each analyzed X-ray image because of Big Data technology. The error probability in the diagnosis decreases, the accuracy in the subsequent treatment increases.

1. And third, after all, Big Data and artificial intelligence have the potential to make the search for new medicines and other treatment methods much more efficient. Today, countless molecular combinations must first be tested in the Petri dish, then in the animal experiment, and finally in clinical trials on their effectiveness, maybe a new drug in the end. A billion company roulette game, in which the winning opportunities can be significantly increased by computer-aided forecasting procedures, which in turn access a never-existed wealth of research data.

As with every innovation in the health system, it’s about the hopes of people to a longer and healthier life. For the urgent that you could be torn from life prematurely through cancer, heart attack, stroke, or another insidious disease from life.

If you want to examine the case of Big Data in practice, you can check this Big Data in the Healthcare Industry article.

Technology Stack

Apache Hadoop Framework

To meet these special properties and requirements of big data, the Hadoop framework was designed as open-source. It basically consists of two components:

HDFS

First: It stores data on several servers (in clusters) as so-called HDFS (Hadoop Distributed File System). Second: it processes this data directly on the servers without downloading it to a computer. The Hadoop system processes the data where it is stored. This is done using a program called MapReduce.

MapReduce

MapReduce processes the data in parallel on the servers, in two steps: first, smaller programs, so-called “mappers”, are used. Mappers sort the data according to categories. In the second step, so-called “reducers” process the categorized data and calculate the results.

Hive

The operation of MapReduce requires programming knowledge. To make this requirement a little easier, another superstructure was created on the Hadoop framework – Hive. Hive does not require any programming knowledge and is based on the HDFS and MapReduce framework.  The commands in Hive are reminiscent of the commands in SQL, a standard language for database applications, and are only then translated in MapReduce in the second step.

The disadvantage: it takes a little more time because the code is still translated into MapReduce.

The amount of data available is increasing exponentially. At the same time, the costs of saving and storing this data also decrease. This leads many companies to save data as a precaution and check how it can be used in the future.  As far as personal data is concerned, there are of course data protection issues.

Final thoughts

 In this article, I don’t mean to call a Big Data groundbreaking shot today. I believe it’s something that should be adopted widely, and that already has been taken by a lot of world-famous companies.

In the course of the digitization of the health system in general and currently, also with the corona crisis in particular, there are also new questions for data protection. The development and use of ever further technologies, applications, and means of communication offer a lot of benefits but also carries (data protection) risks. Medical examinations in video chat, telemedicine, attests over the internet and a large number of different health apps mean that health data does not simply remain within an institution like a hospital, but on private devices, on servers of app developers, or other places.

 Firstly we have to deal with the question of which data sets are actually decisive for the question that we want to answer with the help of data analysis. Without this understanding, big data is nothing more than a great fog that obscures a clear view through technology-based security.

Posted on mongodb google news. Visit mongodb google news

Predicting Growth Scope: Global HTAP-Enabling In-Memory Computing Technologies Market
The Global HTAP-Enabling In-Memory Computing Technologies Market research report is comprised of the thorough study of all the market associated dynamics. The research report is a complete guide to study all the dynamics related to global HTAP-Enabling In-Memory Computing Technologies market. The comprehensive analysis of potential customer base, market values and future scope is included in the global HTAP-Enabling In-Memory Computing Technologies market report. Along with that the research report on the global market holds all the vital information regarding the latest technologies and trends being adopted or followed by the vendors across the globe.The research report provides an in-depth examination of all the market risks and opportunities. The analysis covered in the report helps manufacturers in the industry in eliminating the risks offered by the global market. In addition, the market research report also offers readers with full documentation of past market valuation, present dynamics and future projections regarding market volume and size.

Competition Spectrum:

Microsoft
IBM
MongoDB
SAP
Aerospike
DataStax
GridGain
…

An in-depth comparative and thorough analysis of the global HTAP-Enabling In-Memory Computing Technologies market offered in the research report. The market research report also includes the strategies used for thorough analysis of the global HTAP-Enabling In-Memory Computing Technologies market such as SWOT analysis for the global HTAP-Enabling In-Memory Computing Technologies industry, Potters Five Forces analysis and PESTEL analysis. The research report also includes necessary information about the major factors that are considered to be crucial in the study of every industry such as industry growth, revenue, profitability, product knowledge, end users, etc. Furthermore the HTAP-Enabling In-Memory Computing Technologies market research report offers thorough study of all the major factors that have impact on the growth of the market. The market report also provides users with a complete study of performance of HTAP-Enabling In-Memory Computing Technologies market throughout the years with the help of reliable numerical data.

Find full report and TOC here: @ https://www.orbisresearch.com/reports/index/global-htap-enabling-in-memory-computing-technologies-market-size-status-and-forecast-2020-2026?utm_source=PoojaM

The research report on global HTAP-Enabling In-Memory Computing Technologies market covers a full documentation of study of all the segments of the market. The detailed study offers an important microscopic view of the industry to define manufacturers footprints by awareness of manufacturers worldwide sales and costs, and manufacturers production over the forecast era. Leading and influential players in the global HTAP-Enabling In-Memory Computing Technologies market are narrowly analyzed on the basis of key factors in the competition analysis portion of the study. Furthermore, the research report provides a thorough description of the market size and volume per region in market terms. The report covers the study of all influential regions across the globe.In all the report plays an important role in understanding all the market related dynamics thoroughly

The market is roughly segregated into:

• Analysis by Product Type:

Single Node Based
Distributed Systems Based
Hybrid Memory Structure Based

• Application Analysis:

Retail
Banks
Logistics
Others

• Segmentation by Region with details about Country-specific developments
– North America (U.S., Canada, Mexico)
– Europe (U.K., France, Germany, Spain, Italy, Central & Eastern Europe, CIS)
– Asia Pacific (China, Japan, South Korea, ASEAN, India, Rest of Asia Pacific)
– Latin America (Brazil, Rest of L.A.)
– Middle East and Africa (Turkey, GCC, Rest of Middle East)

Table of Contents
Chapter One: Report Overview
1.1 Study Scope
1.2 Key Market Segments
1.3 Players Covered: Ranking by HTAP-Enabling In-Memory Computing Technologies Revenue
1.4 Market Analysis by Type
1.4.1 Global HTAP-Enabling In-Memory Computing Technologies Market Size Growth Rate by Type: 2020 VS 2026
1.5 Market by Application
1.5.1 Global HTAP-Enabling In-Memory Computing Technologies Market Share by Application: 2020 VS 2026
1.6 Study Objectives
1.7 Years Considered

Chapter Two: Global Growth Trends by Regions
2.1 HTAP-Enabling In-Memory Computing Technologies Market Perspective (2015-2026)
2.2 HTAP-Enabling In-Memory Computing Technologies Growth Trends by Regions
2.2.1 HTAP-Enabling In-Memory Computing Technologies Market Size by Regions: 2015 VS 2020 VS 2026
2.2.2 HTAP-Enabling In-Memory Computing Technologies Historic Market Share by Regions (2015-2020)
2.2.3 HTAP-Enabling In-Memory Computing Technologies Forecasted Market Size by Regions (2021-2026)
2.3 Industry Trends and Growth Strategy
2.3.1 Market Top Trends
2.3.2 Market Drivers
2.3.3 Market Challenges
2.3.4 Porter’s Five Forces Analysis
2.3.5 HTAP-Enabling In-Memory Computing Technologies Market Growth Strategy
2.3.6 Primary Interviews with Key HTAP-Enabling In-Memory Computing Technologies Players (Opinion Leaders)

Chapter Three: Competition Landscape by Key Players
3.1 Global Top HTAP-Enabling In-Memory Computing Technologies Players by Market Size
3.1.1 Global Top HTAP-Enabling In-Memory Computing Technologies Players by Revenue (2015-2020)
3.1.2 Global HTAP-Enabling In-Memory Computing Technologies Revenue Market Share by Players (2015-2020)
3.1.3 Global HTAP-Enabling In-Memory Computing Technologies Market Share by Company Type (Tier 1, Tier Chapter Two: and Tier 3)
3.2 Global HTAP-Enabling In-Memory Computing Technologies Market Concentration Ratio
3.2.1 Global HTAP-Enabling In-Memory Computing Technologies Market Concentration Ratio (Chapter Five: and HHI)
3.2.2 Global Top Chapter Ten: and Top 5 Companies by HTAP-Enabling In-Memory Computing Technologies Revenue in 2020
3.3 HTAP-Enabling In-Memory Computing Technologies Key Players Head office and Area Served
3.4 Key Players HTAP-Enabling In-Memory Computing Technologies Product Solution and Service
3.5 Date of Enter into HTAP-Enabling In-Memory Computing Technologies Market
3.6 Mergers & Acquisitions, Expansion Plans

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Article originally posted on mongodb google news. Visit mongodb google news