Huge amounts of data are being generated daily. However traditional systems of managing
data fall short when it comes to analyze such huge datasets. The answer to this riddle lies in Big Data. According to Data Scientists working for IBM, Big Data may be broken up into four dimensions and this presentation brings forth to you some startling and astonishing figures concerning each.
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The kind of data that you want to import in to R may come in formats of various sorts like those of statistical software, flat files, web data and databases.
In R, it is often found that the various data types require varied approaches. Through this post we list how the more common file types may be imported into R programming.
Typically flat files may said to be simply text files containing table data. R has through its standard distribution the ability to import such a file in to the R environment through the aid of functions like read.table() as well as read.csv() from the package referred to as utils. Also, you may import files like these through readr which is a package famed for its ease of use and swiftness.
If you want to import excel files in to R, one need to carefully examine the readxl package. As an alternative you may also use the gdata package which includes in its functionality importing Excel data and also the XLConnect package. The XLConnect package is more of a real bridge between R and Excel. This basically means that any action that might be done with Excel might very well be done from R.
Other packages of software like SPSS, SAS and STATA are used to produce their own formats of file. This is best handled with the Haven package created by Hadley Wickham. Besides its ability to import such files it is also characterized by its ease of use. As an alternative there is packages like foreign which has the ability to import more esoteric formats like Weka and Systat. It comes with the added functionality to export data to a large number of formats as well.
The database type that you wish to connect to determines the package which is to be used to import from and connect to a relational database. MySQL databases may be connected to through the means of the RMySQL package. Other examples are RpostgreSQL and ROracle. Then you must make use of another R package like DBI in order to manipulate and access the required database.
One may also harvest web data using the R Programming language. This may be done by connecting online resources to R through the use of API or scrape with the help of packages like rvest.
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NewVantage Partners have over the course of the last four years which has found that Big Data has quickly become the part and parcel of the day to day activities of most of Fortune 1000 companies.
The opinion of the crucial decision makers within organizations was taken into account. Financial led the way in investing in Big Date infrastructure. Also of note is the fact in the field of life sciences too usage of big data is increasing by the day.
Other findings of the survey are:
- In 2012 just a small group of 5% of firms had in place a system of Big Data analytics which in 2015 rose to 63%.
- In 2012 only 24% of firms claimed to have expectations to invest $10 million or more in Big Data by the year 2017 which rose to 63% in the year 2015.
- At present a majority of 54% of companies have in place a Chief Data Officer which rose from 12% in the year 2012.
- In 2012 only 21% of firms reported that their firms held Big Data to be critically important which rose to 70% in the last year.
During the starting year of the survey the executives of organizations were struggling to come in terms to properly perceive the impact and opportunity that Big Data might potentially hold. But today it has become the standard norm for corporate organizations and the focus is rapidly shifting to results produces and the business capabilities enabled by the same.
There is also a need to develop the appropriate metrics. While indeed the majority of Fortune 1000 companies’ implemented capabilities in Big Data, few have demonstrated the business value derived from the investments over time. Organizations with responsible executives for data who report to a Chief Financial Officer are often more likely to have developed financial measurements that are precise.
Innovation remains a source of much promise in the field of Big Data and innovation opportunities need to be identified. There is a dearth of success stories in innovation in things that are enabled by Big Data. Till now its success has largely been limited to savings on cost of operations or being able to analyze data sets that are larger or more diverse. Innovation in Big Data led applications need to be funded more and its practitioners need to exemplify imagination as well as boldness.
Businesses and the analytics industry needs to prepare for both business and cultural change as a new generations take their place in the workplace who have grown up on tools Like R Programming and Hadoop. There is great need for organizations to realize that Big Data is more about cultural change rather than solely technical change.
If you too want a piece of the bigger pie of Big Data as a practitioner, then you should try to train yourself for a promising career through proper Big Data Courses in Delhi as conducted by a leading institute like DexLab Analytics.
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MS Excel is part and parcel of the workforce and work tools. It is one of those things that have acquired a permanent place in our work habits. This presentation will get you a glimpse of all the things are required to be known in order to be proficient at its basics. But Excel has several advanced uses that are best explored with a course from MS Excel Training Centre of repute like DexLab Analytics.
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DexLab Analytics as outperformed many of its more illustrious peers in India as it bagged the fourth spot in the rankings recently released by Analytics Vidhya. This comes close on the heels of the success enjoyed by MS Excel Dashboards Bootcamp organized by DexLab Analytics on the 26th of January.
Analytics Vidhya is a blog dedicated to the Analytics run and overseen by Kunal Jain who happens to be an aerospace engineering alumnus from IIT Bombay with more than six years of experience as Business Analytics. It aims at creating a vibrant and passionate community dedicated to analytics study and has enjoyed a considerable amount of success too in meeting its stated goals.
The rankings of these courses have had four factors as their basis. They happen to be the Quality Score, Value Course, Coverage Score in addition to Industry Recognition. All of the courses examined, including the ones that were not ranked, were evaluated intensely on the basis of these factors. To delve into the details of the ranking basis:
- The Quality Score(0.4): This score indicates several aspects that are inclusive of how well is the training material presented in addition to the support provided by the respective institutes for the candidates.
- Value Score (0.2): The value score indicates the value for money as provided by that particular score.
- Coverage Score (0.2)- The comprehensiveness of the material covered the course is taken into account in this part of the score factor.
- Industry Recognition (0.2): This is representative of the recall and recognition of the training course platform or institute amongst professionals and employers. Institutes that have high recognition in the industry ate better placed to get placements for their students.
As the review of the MS Excel Course included in the rankings concluded- “This course is best suited for candidates aspiring for MIS analyst roles.”
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