Data Analytics and Knowledge Management

Explosion of the economic relevance of data is related to: first, technological progress that has drastically reduced the costs of collecting and storing data; second, the advances in analytic techniques that have allowed for more advanced processing to extract greater value from available data. So, more value is extracted from exhausted data.

Information generated for one purpose can and should be reused for another one, which means that data moves from primary to secondary uses . data are more valuable over time.

Explosion of the economic relevance of data is related to: first, technological progress that has drastically reduced the costs of collecting and storing data; second, the advances in analytic techniques that have allowed for more advanced processing to extract greater value from available data. So, more value is extracted from exhausted data.

Information generated for one purpose can and should be reused for another one, which means that data moves from primary to secondary uses . data are more valuable over time.

Data's true value is like an iceberg floating in the ocean: only a tiny part is visible, while much of it is hidden.
Innovative companies that understand this, can extract that hidden value and reap huge benefits.
Data's worth is its unlimited potential for reuse: the option value. What really matter for the value of data is its use and not its possession.
Data science can have incredible benefits for your business. With data science and predictive analytics, in particular, you can predict useful metrics and trends for your business. Such an approach can help you improve your ability to serve your customers or otherwise compete in the market.

The world is becoming more and more data-driven, with endless amounts of data available to work with. Big companies like Google and Microsoft use data to make decisions, but they're not the only ones.

Is it important? Absolutely!

Data analysis is used by small businesses, retail companies, in medicine, and even in the world of sports. It's a universal language and more important than ever before. It seems like an advanced concept but data analysis is really just a few ideas put into practice.

What Is Data Analysis?

Data analysis is the process of evaluating data using analytical or statistical tools to discover useful information. Some of these tools are programming languages like R or Python. Microsoft Excel is also popular in the world of data analytics.

Once data is collected and sorted using these tools, the results are interpreted to make decisions. The end results can be delivered as a summary, or as a visual like a chart or graph.

The process of presenting data in visual form is known as data visualization. Data visualization tools make the job easier. Programs like Tableau or Microsoft Power BI give you many visuals that can bring data to life. There are several data analysis methods including data mining, text analytics, and business intelligence.

How Is Data Analysis Performed? Read the book and learn!

How R is Used in Data Science?

R is one of the latest cutting-edge tools. Today, millions of analysts, researchers, and brands such as Facebook, Google, Bing, Accenture, Wipro are using R to solve complex issues. R applications are not limited to just one sector, we can see R programming in -- banking, e-commerce, finance and many more.

Basically, R language is an open source program maintained by the R core-development team. Also, the R language used for performing statistical operations. Moreover, R is a command line driven program.

Nowadays, R is considered as the most popular analytic tool in the world if we compare R vs SAS vs SPSS. Further, it estimates of some users range from 250000 to over 2 million.

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