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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-

How to overcome obstacles for the septennial 2020-2027 using the Global Data Mining Software market report?

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.

We have faith in our services and the data we share with our esteemed customers. In this way, we have done long periods of examination and top to bottom investigation of the Global Data Mining Software market to give out profound bits of knowledge about the Global Data Mining Software market. Along these lines, the customers are enabled with the instruments of data (as far as raw numbers are concerned).

The graphs, diagrams and infographics are utilized to speak out about the market drifts that have formed the market. Past patterns uncover the market turbulences and the final results on the markets. Then again, the investigation of latest things uncovered the ways, the organizations must take for shaping themselves to line up with the market.

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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Data Mining Software Market Size 2020 by Top Key Players, Global Trend, Types, Applications, Regional Demand, Forecast to 2027 - LionLowdown

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

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

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Global Big Data And Analytics In Telecom Market To Witness Huge Gains Over 2020-2026 - The Courier