Architecture of Data Lake
Data can be traced from various consumer sources. Managing data is one of the most serious challenges faced by organizations today. Organizations are adopting the data lake models because lakes provide raw data that users can use for data experimentation and advanced analytics. A data lake could be a merging point of new and historic data, thereby drawing correlations across all data using advanced analytics. A data lake can support the self-service data practices.
Managing data is one of the most serious challenges faced by organizations today. - Problem for almost every organisation
SURVIVAL PARAMETRIC MODELS TO ESTIMATE THE FACTORS OF UNDER-FIVE CHILD MORTALITY
Exploring parametric survival models in daily practice of child mortality research is challenging. It may be due to many reasons including popularity of Cox regression and lack of knowledge about how to perform it. This paper provides the application of parametric survival models by using available R software with illustration.
Exploring parametric survival models in daily practice of child mortality research is challenging. - A nicely written paper
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