Diagnostic research: a quick overview
This paper examines how diagnostic research is undertaken using various statistical tools. It highlights the difference between a diagnostic test and a screening before evaluating a diagnostic test. The paper then presents statistical methods and classifiers.
how diagnostic research is undertaken using various statistical tools - Nicely covering all the topic and flow
Generating Synthetic Time Series Data using Java Programming
As researchers push the boundaries of computational analytics to predict outcomes from measurements and trends, they are challenged by the availability of datasets to test their programs. Thus, a faster and easier data synthesis can help such researchers by generating synthesized virtual data sets which will allow them to validate their programs and thus improve the quality of their research..
As researchers push the boundaries of computational analytics to predict outcomes from measurements and trends, they are challenged by the availability of datasets to test their program - Agree
The Data Truthfulness: A Big Data understanding
As the smart information age matures, data has become the most powerful resource enterprises have at their disposal. The data is consider as the most valuable commodity on the globe, far ahead than the crude oil in economy list. The paper is an attempt to provide a complete understanding about the trustworthiness of data in the big data framework. The current article provide the complete insight a
Big data is a term that describes the large volume of data – both structured and unstructured that inundates a business on a day-to-day basis. .It's what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.The above article clearly explain the Big Data concepts.Very well written.
Blockchain Technology a Changing Face of Healthcare
Blockchain innovation has increased extensive consideration, with a raising enthusiasm for a plenty of various applications, going from information the board, money related administrations, digital security, IoT, and nourishment science to human services industry and mind examine. There has been a wonderful intrigue seen in utilization of blockchain for the conveyance of sheltered and secure human
Blockchain a very vast and big topic to cover. It is the next big thing and will prove a revolution in future..
Data Analytics Integrity: Challenges to Implementation of the Automated Data Collection Processes
In recent months, my company (Baron Consulting) has been proactively involved in setting up Data Collection Systems for a range of Private and Public Organisations we are servicing. Some of the data collection challenges have already been discussed in our recent Raw Data Collection 2020: Principles and Challenges White Paper. While the RDC (Raw Data Collection) paper analysed the current state of
. Some of the data collection challenges have already been discussed in our recent Raw Data Collection 2020: - Nicely explained concept
Evolving Role of Data Scientist in the Age of Personalization
This point of view is an exploration of the possibilities engendered by rethinking the role of data scientists in the wake of the industrial revolution. It also explicitly highlights the potential role of Data scientists as an emerging phenomenon, and then to show some of the benefits that this role can bring as we move towards industrial disruption
the potential role of Data scientists as an emerging phenomenon, and then to show some of the benefits that this role can bring as we move towards industrial disruption - Agree nice point
What are some of the latest techniques in data science
What are some of the latest techniques in data science and machine learning. Can we bring down some of the trending one and its use?
Thanks for your comment. Lets keep adding more and more algorithms and techniques in it...
Tools for Data science
What are the famous tools aperson should be aware of while working in Data science ?
Julia, R, Python, etc.RStudio, PyCharm, Notepad++, These are the.some tools working in Data science
Regarding with Gradient Descent in Data Science
Hello Everyone, I am learning Data Science and My few interviews are scheduled next to next week on skype. Can anyone explain in-depth information about gradient descent in Data Science, As my research, the degree of change in the output of a function relating to the changes made to the inputs is known as a gradient. It measures the change in all weights with respect to the change in error. A gradient can also be comprehended as the slope of a function, this is according to this post https://hackr.io/blog/data-science-interview-questions when I was searching for this query. Can anyone know this is the perfect description of Gradient Descent?
Can anyone explain in-depth information about gradient descent in Data Science, As my research, - Any specific?
DataOps or DevOps? What is the difference?
Data is the key to success for many organisations and it should be gathered from and shared with all functions. This is what would happen in an ideal world.
DataOps utilizes the most effective practices of the Development Operations approach - Agree thanks for iinput