People analytics is raising the agenda for companies worldwide. Technology that provides companies with many more methods of collecting data from people is projected to accelerate by using this data to enhance insight into the workforce, increase business efficiency, and enhance employee experience.

Data is an important basis for people to judge something. If the data is analyzed accordingly, people will further understand the essence of something and make it used by people.

Data blending is a recently developed approach used mainly by those working in big data analytics. It's a method of integrating data from different sources into a single structure. Blending offers a relatively fast and straightforward way to access several disparate data sources and identify correlations between them without the trouble and costs of conventional data integration.

Usually, the generation of a result is influenced by many factors in a specific environment. The data collection of factors and results is the basis of analysis.

While practical benefits of the Real-time data analytics over historic data analytics are obvious, it is also essential to understand and cater for its risks, challenges and limitations.

Agree, with the time researchers has found various tools and techniques that today one can do real time data analysis, but again it depends on the person, how (s)he will be using the tools and techniques with respect to the creativity. As to achieve there could be multiple ways, to use right weights & parameters are the most effective part of it.Thanks for sharing this valuable information.

Good day!I would like to ask for some ideas related to Health Informatics that I can pursue for my capstone project for my graduate-level Health Informatics and Health Information Management. My ideal project will be data analytics, but open to any health informatics-related project ideas. I appreciate your input.Be safe and stay healthy!Dennis

The 1990s marked the beginning of internet with Hotmail being the first web-based email provider. Standards such as sender authentication, whitelisting, etc. were unknown. Marketers exploited this opportunity. The strategy was 'just send it‘. As a result, our inboxes get flooded with junk emails every day.

Go to the admin section in your Google Analytics (the gear icon at the bottom left corner), Under the View column (master view), click the button “Filters” (don’t click on “All filters“ in the Account column): Click the red button “+Add Filter” (if you don’t see it or you can only apply/remove already created filters, then you don’t have edit permissions at the account level. Ask your admin to create them or give you the permissions.): Then follow the specific configuration for each of the filters.

In today’s business environment, data rules. Because it is used to determine every strategic move at each level of an organization, it makes sense that you should collect as much information as possible right? I argue that this is actually not the case! Like most things in life, too much of anything is bad- data included. The two major reasons why I argue this position are the paradox of choice and its causal relationship to analysis paralysis. If you have found yourself overwhelmed with a data set before and thought, “Where in the world do I even begin?” then this is the article for you. So grab a seat, pour yourself a cup of coffee, and get ready to never look at data the same way again. This article explores the idea that too much data can actually be detrimental to analysts.

Can you have too much data? But the truth is you can have too much data. In fact, sometimes having more data can actually make things worse, leading us to act in ways that can be counterproductive. Sometimes having more data can actually make things worse, leading us to act in ways that can be counterproductive.

The proper handling and processing of data is at the core of the Insurance business, the process of underwriting is based on data analytics. Over the last few decades, developments in computing power and predictive algorithms, have allowed these companies to build more sophisticated Data Analytics solutions

The proper handling and processing of data is at the core of the Insurance business, the process of underwriting is based on data analytics - Quite agree its challenging phase

Understanding Analytics tools and their usage.

Understanding Analytics and Data analytics is also helping businesses to predict problems before they occur and map out possible solutions. - Quite a point

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

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

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.

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