Though the uses of MS Excel are far varied than that of R Programming when it comes to the world of Big Data, R outperforms Excel by leaps and bounds. Handling data as well as manipulating it, is done far more effectively when the tool of R Programming is used. Watch this presentation if you wish to know the exact reasons that give R Programming a competitive edge.
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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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The market for Business Analytics stood at $42.55 billion in 2014 and is all set to touch $70.11 billion by the year 2022, globally. The industry will witness a CAGR of a considerable 6.44% during the period of 2014 to 2022 for which the forecast is made. The factors that are fuelling the growth of the market include rising demand for analytics by organizations as more and more organizations embrace Big Data, changing the environment in which businesses operate and the choices made by customers with unprecedented swiftness. The things that stand as an obstacle to its growth are factors like the relatively high costs of execution and a general unwillingness to adopt Business Analytics. Other hindrances are severe shortages of skilled workers who have the technical ability to run applications related to Business Analytics.
The lion’s share of the market has been captured by financial services, insurance and banking sector. The ten top vendors of business analytics software together constituted for 70% of the market share all over the world as of 2013. In the year 2013, SAP, Oracle, IBM and Microsoft together sold more than 50% of all sales of software related to business analytics. Tableau earned the distinction as being the fastest growing software company in the category of business analytics in the same year, witnessing a growth of 80% in a single year.
The global market for business analytics is segmented based on application, deployment, end users, software as well as geography. If deployment is considered the market is further segregated to cloud and on-site deployment. If the market is viewed from the perspective of the end user it may be categorized medium and small businesses and large enterprises. From the point of view of application the market for business analytics may be segmented into IT and telecom, media and entertainment, retail, healthcare, manufacturing, energy and power, government, banking, education, insurance and financial services.
According to software the business analytics market globally may be segregated into search and alter, performance and management of big data, predictive analytics, discovery of data, software for visualization, business intelligence and analytics of content. According to geography the markets are North America, Asia Pacific, Rest of the world besides Europe.
The key players in this market are INFOR, IBM, Microsoft, Oracle, Microstrategy Incorporated, Inc., SAS Institute, Tableau, QLIK Technologies and Tibco Software.
Business Analyst Certification Training
If you are interested in this field and are contemplating a career here it is highly advisable that you sign up for a Business Analysis Training in Delhi.
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This post is targeted towards established R Programmers or those who are learning the basics of R Programming through proper R programming certification.
The top ten tips that R enthusiasts should follow while writing their code are as follows:
- You don’t have to tidy up things manually
Though it is indeed a best practice to keep your code neat and clean, you can prevent unnecessary waste of time by letting linters like formatR do the trick for you. After its job has been done you can just lay back and relax with a few minor tweaks here and there.
- Make Use of an IDE
Though you most certainly can write code on a text editor or even the R graphical user interface but Rstudio like Interactive Development Environments make the process of writing code hassle free and so much easier. The code completion hints is sure to save you much time.
- Get To Know the Hotkeys
IDEs quite akin to the OS comes with its share of hotkeys. This saves loads of time as you accomplish more without ever taking your hands off the keyboard.
- Plan Before Coding
If you are sure of the direction the practice of your coding may take you will find the task of coding far easier. And doing things like commenting makes things even more easier.
- If unsure about something make sure to just Google it
If you start from scratch you are sure to learn a lot of things but there is a better more sensible way. Just Google the problem in search of canonical solutions, some of the more common pitfalls or perhaps simply some things that you should take into consideration.
- Avoid repetition
In all probability this tip is one of those that you are pretty much sure to have heard befory but nonetheless is worthwhile to mention. R has the potential to create functions, split the codes that need to be repeated into a set of functions.
- Select the appropriate tool in your context
Avoid relying on R as the primary hammer tool of your choice. Make evaluations of project needs and make use of appropriate languages. If you learn a bit at the outset you are spared of whole lot of pain later.
- Write code that facilitates tests
Make it a second nature to test your code and make the whole procedure quick and one that may be conducted with ease. While writing code incorporate validation and avoid using functions that have negative effects.
- Make proper documentation
Make this a regular part of your code writing practice that is write documentation as you go along the process of writing your code. If you leave the task till the end not only will the task be harder to complete but the final documentation will rarely be completed.
- Make Proper Source Control
Make use of source control like Git or SVN that lets you regularly maintain code versions and lets you make your development more conducive to collaboration.
The value of a code lies in a great part on whether it is documented, tracked and may be tested easily. If you are undergoing a course in a proper R programming training institute these tips are sure to separate you from your peers.
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If you still are unaware of the power of data analytics even after the success of the movie Moneyball, then you might as well be living under a rock. Yes the book by Michael Lewis transformed the way sports administrators can identify low-performing players by using data analysis rather than the conventional techniques. This was the first time something as analytical and core-academic was used in the field of sports and that too for something as substantial as selecting players.
But today data analysis is used in almost every aspect of our lives than just the way we like our favorite teams in popular sports. The basic idea behind statistical analysis is to determine meaningful patterns within a given set of data. This discipline is slowly edging its way into every conceivable business avenue these days whether big or small. The main elements in this field are data (that are available easily these days) and highly powerful computer programs that are used to analyze it and draw valuable insights from it. This field has completely changed the way we perceive information and make decisions. Historically these analysis tools have helped companies take decisions on how they should position themselves in new market ventures as well as place their new products.
For instance, ecommerce start-ups that house some of the smartest minds in the business are using expert statisticians to analyze Big Data with state-of-the-art data analysis tools to generate useful deductions from otherwise dull numbers and decimal points. Whether it is the heavy shifters in the logistics market or simply consumer durables and other fast selling products, companies are using data analysis to predict trends, deduce pricing patterns and are leveraging themselves with a revolutionary use of data analysis tools. Also in the sports world data analysis is behind big decisions like who to draft in a team to what should be the in-game strategy to marketing budget. Movie studios also employ analysis tools to calculate box-office generations and to even predict which movies will be a hit and which ones will be a flop.
Therefore, it is highly unlikely that data analytics would not make its way into the field that generates the highest number of data i.e. finances. The worlds of finances are all about numbers and so data analysis is an integral tool that has been used as an operational instrument in this field for a long-time now. While data analysis has been used in the financial world for several years globally, it is still fairly new to the Indian markets. Until now Indian markets only used data analysis tools in brokerage firms for slicing orders and for minimizing the impact cost of sales and purchases. But data analysis can also be used for mitigating frauds in money laundering, risk analysis and management and for rogue trading.
But currently the stock market is also on the verge of boarding the Big Data analysis train which is known as ‘Quants’ amongst the industry insiders. The basis of decision making in the stock trading market holding the hands of Data Analysis is to choose on the basis of history repeats itself theory. So the analysis tools are used more for technical analysis. The trend of analyzing data on the verge of the result season is slowly transforming as realizing valuable insights and patterns in data has somewhat a knee-jerk effect for the analysts and experts associated with the field. Hence, it is understandable that data analysis is making its way into the core practices of how we trade finances. So, it is understandable that the new key player in the market is data analysis or in other words also known as power computing. As the market experts have realized that using data analysis tools with precision will only increase their trading capabilities while enriching their funds in the process, so it is highly recommended for market participants to join the coveted team of data analysts to harness the true potentials of data.
In India the leading data analyst training institute in Delhi, NCR is DexLab Analytics which offers industry-oriented training courses in data analysis, Big Data Hadoop and other statistical analysis tools used in the market currently.
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As per a recent survey, the training wheels of data science mostly turn to Python. Most industry experts revealed that Python remains to be the no. 1 tool in data science. Many even suggest that R programming skill is turning out to be the top dog in the data industry today after Python.
While many argue that there is no reason to believe that Python’s reign on the data world will not last. But also these are the same people who only thought so, when data science was a place for PhD-holding propeller-heads. Now data science is a mainstream industry, with fresh recruits from a varied range of fields. Initially Python was thought to have the widest range of utilities, but newer and more advanced technologies are cropping up every day.
With data science and management slowly emerging to be elemental in all industries, so is R programming.
Why Python is being swallowed?
It is a common notion that comparing two different programming languages is whimsical as each has their separate “use scenarios”. For instance, it might still seem plausible comparing C++and Swift, but it may not be very informative on the font of revealing new news.
Similar is the case for comparing Python with R programming; both are used by data scientists for data analysis. But R software was initially developed keeping the needs of statisticians. While on the other hand, Python has a more generalized purpose. Earlier in the industry Python had the most number of job opportunities, with its usability in web applications and other such similar uses.
But there has been a sudden change in preferences within the industry which is interesting to note, that led to the sudden rise in the popularity of R programming.
As per the multifaceted ranking from IEEE spectrum, the topmost 5 programming languages currently are – Java, C, C++, R and Python. Another interesting fact to note about R programming language is that it rose from the 9th to the 6th position within a single year. So, it is understandable that it is slowly emerging into the top ranking programming languages now.
Are experts snacking on Python first and then feasting on R?
Many experts expect that there will be an impending fusion in the realm of big data with the melding of R and Python. While Python is the generalist language for developers, but R is a data experts’ language, for those who know their way in data. Many had the question if both R and Python would be in use in the long-run, are both such languages useful in the future.
Today we have the answer to this question as the biggest experts in the field have claimed that more often than not R and Python are used together. But still there is ample reason to believe that R will soon take over the data science world over Python. Because with time, as data planning, management and analysis becomes invaluable in businesses regardless of the department of operation, R will also gain greater popularity rather than Python. And that is not only the forecast for data science industry but overall in the corporate world.
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We at DexLab Analytics conducted a survey, where we asked some of the tech savviest professionals associated with the ITES/Telecom/Marketing/BFSI fields, if they look to change their line of work in the near future and if they do, what will it be? Most of their response was to get into – Business Analytics.
But we have also noticed that although many are ready to take up Business Analytics for their career, when the time comes to take the big leap they are frazzled with a million questions that leave them confused. So, to help give such professionals some direction while taking such important decisions, here we have answered some of the most commonly arising question in the minds of students.
Firstly let us start with the very basics of the subject. What essentially is Business Analytics? Although this question has been answered by us numerous times; to put it simply it is the logical way we make decisions or the way we use logic.
For better understanding imagine a scenario, you are at the mall for shopping for a new dress. Some stores at the mal are offering a sale while some aren’t. And like all malls this one also has diverse range of retailers from budget-grade to superior high-quality ones. Now how do you decide on a dress?
You and almost everyone else take a decision based on the available information or data. How do you find data? You gather them by perusing around the mall visiting different shops and looking at the available options. But what if all the data is available, are there any risks associated with available data then? Is decision making completely risk-free? In such a scenario, even if information or data is available to us, they are usually not very conclusive or not present in their entirety. So, it might help to analyze the data to reduce the risk of taking wrong decisions.
While this might seem like an overly complex process and unnecessary burden to take care of when just shopping for a plain old dress, but imagine making high-stake decisions in similar or even more complex settings at business. Sounds daunting don’t it? That is the main use of Business Analytics. And as long as the mountainous heap of data keeps growing in the industry, the work of a Business Analyst will not dry-out.
The important tools in business analytics today are:
- R programming
- Advanced MS Excel (using macros and VBA)
- Tableau/Spotfire/Qlikview etc.
Where can you learn Business Analytics?
The analytics training institutes in Delhi are the leading organizations in this sector much like B-schools. And with the real estate boom in the NCR regions of Gurgaon many reputed organizations are expanding their chains to such locations. DexLab Analytics is a premium analytics training institute that caters industry-specific training to data driven minds with the best in class faculties.
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