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Discussion Forum

All Data Science Foundation members can open new discussion threads or add comments to existing threads. Non-members can read the discussions, but must become a member if they wish to join a discussion.

Cryptocurrency A Quick Guide to Buying, Using and Investing

posted by Peppersack 19 Apr 2020

Visualisation

Unless you’ve been living under that proverbial rock, you’ve at least heard of cryptocurrency – those digital, non-corporeal monetary units that have the world abuzz these days. Bitcoin is probably the most obvious such crypto coin, but there are plenty of others that have sprung up in the wake of bitcoin’s success, such as Ethereum and Ripple. However, chances are good that unless you’re a financial expert, and possibly even if you are such an expert, you’re not all that clear on what cryptocurrency is, how it works, the risks involved, or even why or how to accept it as payment. Within this guide, we’ll explore those topics and more to help you understand how to buy, use and invest with cryptocurrency. To many people, the term “cryptocurrency” might seem pretty self-explanatory, but if you take a deeper look at the subject, you’ll likely find that it’s more complicated than it might seem at first glance. After all, crypto coins have virtually nothing in common with traditional currency, other than acting as a store of value. Even that similarity can be questionable. So, what is a cryptocurrency?

latest comment by Abhishek Mishra 25 Apr 2020

how to buy, use and invest with cryptocurrency. - Some great platform and uses. Thanks for sharing

Big data management: How Organizations Create and Implement Data Strategies

posted by Francesco Corea 19 Apr 2020

Data Science

Big data is often approached as a mere technical problem, while many times projects fail because of lack of strategic focus. Implementing the right process may help companies to efficiently run analytics works and for this reason, the paper proposes a few concepts that can support the organizational development of a big data blueprint. Three main ideas will be outlined here: the data lean approach, the maturity map, and different organization models for a data team.

latest comment by Abhishek Mishra 25 Apr 2020

Implementing the right process may help companies to efficiently run analytics works and for this reason, - Agree

AI and Insurance

posted by Francesco Corea 19 Apr 2020

Artificial Intelligence

This paper focuses on how AI is disrupting the insurance sector. Starting from the conventional insurance process, we will move through the specific novelties AI is introducing in the field in order to understand how AI is completely changing the way people buy and think of insurance products. Finally, an eight-group classification is proposed for the insurtech ecosystem.

latest comment by Abhishek Mishra 25 Apr 2020

AI is introducing in the field in order to understand how AI is completely changing the way people buy and think of insurance - Agree

Fantastic (data)-Beasts and Where to Find Them: Data Scientists and Data Engineers

posted by Francesco Corea 19 Apr 2020

Data Science

This paper discusses the role of the data scientist, what a data scientist is, and the set of skills needed to become one.

latest comment by Abhishek Mishra 25 Apr 2020

Fantastic (data)-Beasts and Where to Find Them: Data Scientists and Data Engineers - Nicely written artcile.

Nonparametric Statistical Test Approaches in Genetics Data

posted by Rakesh Saroj 19 Apr 2020

Analytics

The biggest challenge of genetic research lies in significant and intellectual analysis of the large and complex data sets generated by the cutting edge techniques like massively parallel DNA sequencing and genome wide analysis. Statistical analyses are the most important of such experimental data. When the data are not normally distributed and using non numerical (rank, categorical) data then use the nonparametric test for exact result of research hypothesis. Order statistics are among the most fundamental tools in non-parametric statistics and inference. Non parametric test does not depend upon parameters of the population from which the samples are drawn, no strict assumption about the distribution of the population. Nonparametric tests are known as distribution free test also because their assumptions are less and weaker than those connected with parametric test. Nonparametric test does not follow probability distribution. To analyze microarrays and genomics data several non-parametric statistical techniques are used like Wilcoxon’s signed rank test (pre-post group),Mann-Whitney U test (two groups) or Kruskal-Wallis test (two or more groups).Importance of this paper is to look at the non-parametric test how to use in genetic research and provide the understanding of these test

latest comment by Abhishek Mishra 25 Apr 2020

Order statistics are among the most fundamental tools in non-parametric statistics and inference. - Agree nice point

Big Data: The next frontier for advance, competition, and efficiency

posted by Rakesh Saroj 19 Apr 2020

Data Science

Nowadays organisations are starting to realise the importance of using more data in order to support decision for their strategies. The size of data in the world is growing day by day. Data is growing because of vast use of internet, smart phone and social network. Big data is a collection of data sets which is very large in size as well as complex. Generally size of the data is Petabyte and Exabyte. Traditional database systems are not able to capture, store and analyse this large amount of data. As the internet is growing, the amount of big data continues to grow. Big data analytics provide new ways for businesses and government to analyse unstructured data. Nowadays, big data is one of the most talked about topic in the IT industry. It is going to play an important role in the future. Big data changes the way that data is managed and used. Some of the applications are in areas such as healthcare, defence, traffic management, banking, agriculture, retail, education and so on. Organisations are becoming more flexible and more open. New types of data will give new challenges as well.

latest comment by Abhishek Mishra 25 Apr 2020

Generally size of the data is Petabyte and Exabyte. Traditional database systems are not able to capture, store and analyse this large amount of data - Agree

Architecture of Data Lake

posted by Ajit Singh 19 Apr 2020

Data Science

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.

latest comment by Abhishek Mishra 25 Apr 2020

Managing data is one of the most serious challenges faced by organizations today. - Problem for almost every organisation

Data Science : Brief understanding of Typical Project Life-cycle, Tools, Techniques and skills

posted by Dibyendu Banerjee 19 Apr 2020

Analytics

Every step in the lifecycle of a data science project depends on various data scientist skills and data science tools. The typical lifecycle of a data science project involves jumping back and forth among various interdependent data science tasks using variety of tools, techniques (mostly statistical methods and formula), programming etc. Let us try to see what could be a typical life cycle.

latest comment by Abhishek Mishra 25 Apr 2020

he typical lifecycle of a data science project involves jumping back and forth among various interdependent data science tasks using variety - Agree

SURVIVAL PARAMETRIC MODELS TO ESTIMATE THE FACTORS OF UNDER-FIVE CHILD MORTALITY

posted by Rakesh Saroj 19 Apr 2020

Data Science

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.

latest comment by Abhishek Mishra 25 Apr 2020

Exploring parametric survival models in daily practice of child mortality research is challenging. - A nicely written paper

Diagnostic research: a quick overview

posted by Rakesh Saroj 19 Apr 2020

Data Science

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.

latest comment by Abhishek Mishra 25 Apr 2020

how diagnostic research is undertaken using various statistical tools - Nicely covering all the topic and flow

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