Business Analytics With R

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Business Analytics With R Online Training Courses

Business Analytics Online Training and Certification course provided by Easylearning.guru imparts advance skills & knowledge required to become a Business Analyst.

Get In-depth Knowledge -  http://goo.gl/ssxKwC

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Business Analytics with R Programming Online Training

Business Analytics Online Certification course offered by Easylearning.guru confers advance aptitudes and information required to wind up a Business Analyst.

Get Details of Business Analytics with R Training

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Advantages of doing R Programming Course from Easylearning.guru

This blog informs you about why one should go for R Programming Certification and the benefits of getting certified in R Programming Course from easylearning.guru Why one should go for R Programming

DATA IS GENERATING RAPIDLY

As the depravity of data is on the boom, there is an urgency for methods to get beneficial insights on it. As a result, there is a desperate need for professionals with appropriate skills such as R Programming.

R programming is the Stepping stone for Data Science

• R Programming training is imperative for professionals to learn Big Data analytics and fresher folks who are looking for a career as a Data Scientist. • To handle and analyze extremely large datasets using sophisticated data analysis algorithms, one has to be properly trained in R Programming. Benefits of doing R Programming from easylearning.guru

• R Programming Online Certification course propounded by Easylearning.guru disseminates advance skills & knowledge needed to become a Business Analyst. • This course provides you a better perception to "R" programming language in order to upskill you more potently and efficiently. • With the help of this course, participants will be professionally competent using R programming language in various fields like data visualization, exploration and statistical computations using concepts such as linear & logistic regression, cluster analysis and predictive modelling.

 Lifetime Access of Learning Management System (LMS)

• There is no efficacy or deadline to the LMS access once the participant has enrolled into the course. • Participant will have lifetime access to the course and contents, even after completing the course. • Also, separate doubt clearing sessions are provided in case the participant is facing any problem in any topic during the course as well as after completion of the course.

 40 hours of online classes

• Interactive Online Classes are conducted by certified instructors at scheduled timings. • Impressionable rescheduling of classes on participant request. • Class recordings for all classes are provided if participant is unable to attend the class. • 20 hours of hands on sessions on all the concepts of R Programming like Data Manipulation, Data Visualization, Regression Techniques, Predictive Modelling, etc. is also provided • Projects consisting of case studies on the topics covered in the course

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How Intel Xeon chips for Business Analytics is a game changer

This blog introduces you to Xeon chips significance in terms of Business Analytics. After reading this blog, you will get to know about the features of Intel Xeon chips that are specifically designed for Business Analysis or Research.

Intel has realized the need of something that can become a game changer for Business Analysis and it has developed Xeon chips E7 v3 Processor. Now, you might be thinking that how a chip like Xeon E7 v3 can help in getting insights from the business data. So, here are some features and workings of this chip:

Features of Xeon E7 v3 which helps Business Analytics:

• Used to get more critical insights from huge amount of data • 40% average improved chip than previously launched chips • 20% more speed and broke all the speed records in processing the massive amount of Data • Having up to 18 cores for parallel processing • Having 12 different models which accelerates fast processing • Top system manufacturers including Lenovo, HP, Cisco, Dell and Oracle are planning to include Xeon in new machines

As computer’s memory prices have reduced to a very nominal cost -- and urging demand for Big-Data Analytics has expanded – the result is organizations have started acquiring in-memory computing for fast-speed data processing. If we compare traditional business intelligence software with in-memory computing, we find that memory computing is allowing users to analyze large amount of data quickly for real-time decision-making. Gartner has evaluated that the market for in-memory computing will expand beyond $9.5 billion by the end of year 2018.

Xeon for handling Large Data

If we look forward, Intel and Cloudera have collaborated and announced an enhancement to the Hadoop distribution that will be nurtured by Intel Xeon Processors to provide 25% better performance (off-load). Now think about this, it can be like an entire Hadoop data can be encrypted or decrypted as small as one percent of the CPU’s performance.

With this chip implanted in your machine will make Big Data Analytics more comfortable and fast for you and you can get better insights of the Big Data in a few seconds because in this case Xeon is helping you.

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HOW NETFLIX INCREASED ITS REVENUE USING DATA ANALYSIS

For the past decade, DVD revenue has been shrinking rather immediately, due to piracy and changing consumer viewing habits. The industry is hoping that the new player on the block, Video on Demand (VOD), can fill this loss. In the year 2013, VOD provided 7.9% of all profit generated by films in the UK, up from just 2.7% in 2008.

The biggest player in the VOD market is Netflix. It started in 1997 by Reed Hastings as a mail-based DVD rental service, adding internet streaming (VOD) services in 2007 and by the start of 2014 they were providing over 1 billion hours of films and TV programs every month. In 2002 the company has move to public, which means that legally it is required to publish a whole host of data.  I have given some time going through 10 years’ worth of annual reports in order to pick out useful data points on Netflix.

In summary..

• In the year 2000, Blockbuster rejected to buy Netflix for $50m. • In 2013, Netflix generated $4.4 billion in credit. • In just 3 months (July to Sept 2014) Netflix spent $1.2 billion acquiring new content for its streaming service and a further $16 million on new DVDs. • The average Netflix subscriber streams 45 GB of films and TV shows per month.

Netflix has a rough start to 2014 as the stock has fallen 9.4% since the start of the year, but the company continues to roll out internationally, and shares could have almost 60% upside to them.

As Netflix continues to gain scale the company could raise prices, given the value customers are getting. "The estimate is the average U.S. Netflix subscriber is paying $0.16 per hour viewed, v/s approximately $0.50 for the average pay TV subscriber, which suggests significant latent pricing power," Given how strong Netflix is with U.S. consumers and the value it provides, Analyst Wilson estimates that there's the potential for Netflix to boost prices significantly over the next few years, bringing it into parity with how much conventional pay TV costs. Wilson said if Netflix came into parity with pay TV, Netflix would cost $25 a month, way more than the usual $8 a month subscription most people have.

Though much has been made about Netflix's content costs, the company has a structural advantage in how it buys content. The company has precise data on what each user watches, an on-demand schedule that caters to users, not Netflix itself, and it has the compliance to buy separated content. Wilson said that "We believe this combination, along with the scale of Netflix, allows the company to purchase content more efficiently than its traditional linear competitors and its smaller Internet competitors".

Aside from being the dominant OTT(Over-The-Top) player, Netflix's cost of content(coc) per hour viewed is substantially less than Comcast's, which is the largest cable operator in the U.S. Wilson estimates that Netflix pays only 10 cents an hour viewed to get its streaming content, v/s 23 cents an hour viewed for Comcast, and this gap is spreading. This may be part of the reason why Comcast essentially bound Netflix to pay for direct access to Comcast's broadband network.

So, Netflix has a massive opportunity to expand internationally, as developed countries in Europe present the next big area of growth for the company, in a meaningful way and it help to increase its revenue.

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Essential Packages in R Programming Language

R is one of the trending programming language for Data Analysis with enormous amount of packages contributed by developers from all prospects and background. Around 4000 packages are listed on CRAN website itself, but are they all determined? Certainly not. This blog familiarizes you with some of the essential packages in R programming language from different domains that are most extensively used during Data Analysis.

sqldf

It is one of the core package used by the Analysts to perform SQL queries on R data frames. Sqldf uses SQLite syntax. If you want to load data from an external source or databases, then R has connecting drivers to most of them. Some of the instances for this are:- • RODBC, RMySQL, RPostgresSQL, RSQLite for reading data from the database. • XLConnect, xlsx for reading and writing Microsoft Excel files from R. • foreign reads SAS and SPSS datasets in R. it also helps you load data files from other programs in R.

ggplot2

Most essential package among all data visualization packages widely used by the R programmers. It is fundamentally an application of the grammar of graphics in R to present your results in more understandable way by building customizable plots. 

plyr Data manipulation in R is the most essential step for reforming your data according to your requirements. It is manifested that almost 80% of the time is devoted in data preparation however data manipulation is one of the step incorporated while preparing data. Plyr package widely helps in manipulation of data by contributing essential functions that it contains for repositioning, subsetting, combining datasets together, summarizing etc. it is recommended using plyr if you are dealing with apply family of functions for data manipulation in R. Some of the other essential packages used for data manipulation are:- • lubridate mainly deals with dates and times handling. • stringr works with regular expression and characters.

randomForest

• One of the major package used for building non-linear models. It is simple to use and works on peculiar types of datasets. • Added advantage about this package is it can be used as a feature reduction algorithm. • For instance when your dataset has more than 200 variables and you need to find the most remarkable ones, randomForests package has a variable importance function which will only list out the important variables in the dataset. If you are willing to start working on non-linear models, you must start with this package initially.

caret

• caret package is used for building better predictive models it deals with data handling, feature selection, building multiple predictive models using various techniques. • Performs validation checks and prints out the model performance diagnostics. • This assuredly looks like a lot and getting used to all these functions would take some time too, but once you are through with that it will make your model building skills more enjoyable. And due to this fact, caret has become popular in recent years amongst R programmers especially in Predictive Analytics field.

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TOP 5 BUSINESS ANALYTICS SOFTWARE

Business analytics is a fast growing field and there are many tools available in the market to serve the needs of organizations. The scope of analytical software goes from relatively simple statistical tools in spreadsheets (e.g-MS Excel) to statistical software packages (e.g-KXEN, Statistica) to practical business analytical tools (e.g-SAS, Oracle, SAP, IBM among the big players). Open source tools such as R and Weka are also gaining popularity. Apart from these, companies develop in-house tools designed for specific purposes.

Here is a list of 5 most popular analytic tools used in the business world.

SAS is the largest independent vendor in the business analytics market. It is the most frequently used software in the Indian analytics market despite its monopolistic cost. SAS software has extensive ranging efficiencies from data management to advanced analytics.

STATISTICA

Statistica is a statistics and analytics software package. It is developed by StatSoft. It provides data mining, data management, data visualization and data analysis procedures. Statistica supports an ample variety of analytic techniques and is capable of meeting most needs of the business users. The GUI (Graphical User Interface) is not so much user-friendly and it take more time to learn than some tools but it is a competitively priced product that is value for money.

R is a programming language and software environment for statistical computing and graphics. The R programming language is widely used in the midst of statisticians and data miners for developing statistical software and data analysis. R and its libraries implement a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, clustering, classical statistical tests, classification, time-series analysis and many more.

MATLAB is a high-level language and collective environment for numerical calculations, visualization, and programming. It is developed by MathWorks in early 1980s. It allows plotting of functions and data, implementation of algorithms, matrix manipulations, interfacing with programs and creation of user interfaces. More than a million scientists and engineers in industry and academics use MATLAB, which is the language of technical computing.

Almost every business user has access to MS Office suite and Excel. It is used for Statistical analysis. While most people are aware of its excellent reporting and graphing skills, excel can be a powerful analytic tool in the hands of an experienced user. Current versions of Excel can handle tables up to 1 million rows making it a powerful and versatile tool.

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HOW BUSINESS ANALYTICS IS USED IN HEALTHCARE INDUSTRY

With the growing challenges faced by the healthcare industries, providers now stay one step ahead of the competition. Business analytics can help healthcare industry, payers and hospitals to increase competitive advantage by formulating smart business solutions and enabling better business decisions every day.

When properly applied to data within healthcare industries, business analytics provides the insight needed to reduce costs, increase revenue and satisfy with industry regulations and standards. In addition, industries are given the tools they need to make the best use of the large amount of information available to them, allowing them to act even more efficiently and improve patient’s health.

Here, we are discussing few ways how healthcare providers have advantage on a business analytics platform to make smart decisions across infinite challenges in the healthcare industry.

Safety analysis and Quality performance 

• Business analytics can evaluate an individual's history and risk profile to determine the possibility of increased resource consumption based on information such as rapid use of healthcare services, patient demographics and drug use. 

• Performance correlation across patient access, quality, patient satisfaction, and financials are made possible by observing the quality of their care according to the Health policy.

Financial analysis

Healthcare industries need clarity into the full scope of financial operations. • Business analytics facilitate healthcare providers to drill from reports into   detailed analysis of costs and interest, compare planned versus actual income and margin and view data underlying cash flow statements.

• Reporting capabilities enable healthcare industries to meet legal reporting requirements and ensure accountability from financial analysts to business unit management, directors and executives.

Marketing analysis

Reporting on patient satisfaction supports the target within healthcare industries for increased accountability among healthcare providers, and it can also be advantageous for marketing purposes. 

• Providers use business analytics to evaluate the success of marketing efforts designed to demonstrate the clinical efficiency of their products and services. • Healthcare providers use panels to show which products and services are profitable in specific patient segments and measure how their marketing efforts and corporate communications revise brand approach and affect brand performance.

Patient care analysis 

Industries using business analytics and allow patients to take control of their care, leading to revised outcomes.

• Business analytics enables the right people to access the right information at the right time, delivering a single platform to healthcare providers for sharing information with patients for better decision-making and connecting patients across hospital, nursing home and community social support settings. 

• Providers have the ability to monitor and estimate patient diagnoses and use of healthcare services in order to improve patient care, and administer more effective treatments and reduce waiting times.

The applications of business analytics in the healthcare industry are far-reaching and can become the platform upon which the industries assesses performance and makes critical decisions. Those industries that influence effective platforms for superior analytics, flexibility and impressive information delivery potential, are definitely ahead of the game.

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Why go for Business Analytics R Programming?

With all of the data that existed after 2003 were the recorded information which we generate now in two days. There are terabytes of data at hand, every business is trying to figure out the best way to understand information about their customers and themselves.

But simply using Microsoft Excel pivot tables to analyse such amount of information is inappropriate or unreasonable. There are so many companies that are using commercially available tools like SAS, JMP, Minitab, Statistica, Stata etc. to cull business intelligence.

In business environment R is the open source analytical tool that is used by 70 percent as compared to other analytical tool.  It lists the various reasons that R should be chosen for learning by business analysts

It offers the best data visualization tools in analytical software. The extensive data visualization available in R comprises a wide variety of customizable graphics as well as animation.

 Deals with Tons of Business Data

It is free for academics and thus budget friendly for small and large analytical teams. This includes around more than 4000 packages and all R programming libraries are free, still Revolution analytics makes its business from services packages.

Some of the R packages like ScaleR, helps businesses go through all of their data by scaling it to work on parallel processors. Using standard R packages, we deal with large amount of datasets

According to the Solomon Messing, data scientist at Facebook, we use R Programming language to move fast when we get a new data set, with the help of R programming language, we don’t need to develop custom tools or write a bunch of code.

Business Analytics with 'R' will prepare you to perform analytics on business and build models for real world data science problems.

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R Programming: The Stepping Stone for Data Science

Discussion of what language is best for learning Analysis to reach a position of Data Scientist.

Repeatedly, when discussing with people who are starting to learn Data Science, there’s a harshness that comes up which is:

“I don’t know which programming language to start with.”

And it’s not just about programming languages, it’s also about software tools like SAS, R, Rstudio, Minitab, Tableau, SPSS, JMP, etc. There is an ever widening range of tools and programming languages and it’s difficult to know which one to prefer. There are, innumerable material, numberless suggestions, numerous alternatives, and it becomes complex to be enlightened what to learn first as a first step. There’s a hill of content, and it’s difficult to get where to find the “gold treasure;” the articles to learn that will lead you to the lofty repay on time investment.

The reality is, your time is short. Intellection of a new programming language is a gigantic investment of time and efforts, so you need to be specific about which one you opt. To be analytical, some languages will generate a very supreme return on your investment (your investment of both money and time). Other languages are purely fallback tools that you might operate only a few times over a time period.

Let me make this effortless for you: learn R first.

Vogue of R has increased over the time, it is now becoming the most popular and most commonly used language over all because R developers tend to use it as a primary tool for data science projects, also it incorporates vectorized operations that are important to the linear algebra principles intrinsic in many machine learning algorithms.

  Erudition the “skills of data science” is easiest in R

The admiration of R isn’t the only sense to learn R, however.

Ultimately, to really learn data science, one needs to have authority over “fundamental” skill areas: data visualization, data manipulation and machine learning. The language must be the one that has remarkable capabilities in each of these areas, accomplish each of these tasks with negligible complexity. Hence making command on these skills will be easier in R unlike other languages.

So to recapitulate, select one language. If you’re initializing, R is almost certainly the best choice, Devoting 100 hours on R will yield vastly better returns than spending 10 hours on 10 different tools. In the end, your time Rate of Investment will be higher by concentrating your efforts. 

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R Programming: The Past

This blog introduces you to the world of ‘R’. R is just not a word rather R is marvelous language and an amazing tool used for Data analysis and Statistical Graphics. It adds an easy visualization to your data, it is free, and widely used by professional statisticians and market analysts. So, before starting with something amazing you should be aware about its past. In this blog you’ll get to know how R has developed. R has transformed from S language and S language itself had few phases in its whole existence. Regardless S is not with us now, still you should know a bit about the versions of S language as R is evolved from S. S VERSION 1 S VERSION 2 S VERSION 3 S VERSION 4 •Implementation nearly all FORTRAN based, via preprocessing tools • Only for our (bizarre) operating system • Adopted our existing graphics & data structure software • Portability via a Unix implementation • Most features carried over from V. 1 • Device-independent graphics • Merged some new ideas with S • “Everything is an object” (including functions) • No direct back compatibility with S2 • ‘Computing with data’ distinguished from statistical computing • Extensions to the S programming model • Today we have the S language, implemented in R HISTORY OF • The R language is a terminology of S language which was designed in the 1980s by John Chambers at Bell labs and has been in widespread use in the statistical community since. • It was grown up by Robert Gentleman and Ross Ihaka of the University of Auckland. R has been with us since 1993. • After the four versions of ‘S’ language we came up with R tool and R language. • During a chance they both encounter in the corridor, the following conversation took place: Gentleman: “Let’s write some software.” Ihaka: “Sure, that sounds like fun.” • It is concluded to adopt the syntax of the S language which has developed at Bell Laboratories. – As a joke, the name “R” is punched for the language (putting for Robert and Ross). LICENSING When an initial version of R language has completed then colleagues of Robert and Ross encouraged them to release the language as “free software”. Finally colleagues made them convinced and they adopted the Free Software Foundation GPL as their license and began to make releases via Internet. They started a small email list so that they and the users can discuss R. So this was all about history and past of R language and in my next blog I will be discussing the present status of R.

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BUSINESS ANALYTICS WITH R TRAINING

 Enhance your career With  Online BUSINESS ANALYTICS WITH R TRAINING 

http://easylearning.guru/Business-Analytics-With-R-Training

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MACHINE LEARNING WITH R Programming

Machine learning is concerned with transforming data into triable knowledge. This certainty makes machine learning pertinent to the present-day span of "big data" and "data science". R gives you admittance to the forefront software to formulate data for machine learning. Machine learning has come up with enormous applications since last few years. “Machine Learning” is a entitle drifting not only within the technology industry but also in industries like government, healthcare, marketing, researches and education, etc. We suppose that Machine Learning is the foundation of a better and smarter future.

THE UNIQUENESS OF MACHINE LEARNING

Machine Learning is unique about:

·         Serving a computer to learn, instead of helping a human to understand

·         Precisely focus on predicting the future or the unspecified

·         Enhancing accomplishment as a greater extent of data is analyzed

TRAINED STATISTICS

Statistics is the familiar property, knowledge or experience that the computer memorizes from. Data/Statistics used for training prediction model in Machine Learning is called trained statistics. Data used for assessing performance during the training stage is called validation Statistics. Those used for assessing the final accomplishment is called test statistics.

TYPES OF LEARNING

Machine learning is concised into two categories of learning. Each learning classification has pointedly different algorithms.

Ø  Supervised Learning

Supervised Learning is one of the most accepted task types in Machine Learning. The mission/task for the computer is to grasp an input and then predict an output build on what it has learnt from the trained statistics. Trained statistics consists of pairs of input and accurate output, which are called flagged data. When the outcome is a discrete variable, e.g. predict whether match will be 1) win-win; 2) lost; or 3) draw, it is called a Classification problem. When the out turn is a continuous variable, e.g. predict the sales of tomorrow, it is called a Regression problem.

Ø  Unsupervised Learning

Unsupervised Learning is another popular mission/task type in Machine Learning. The task for the computer is to recognize structures or patterns in the trained statistics, and then to predict which one the input it is associated with. The trained statistics consists of only input but not any example out turn, which is called unflagged data. The searching for structure with Unsupervised Learning in Machine Learning is called Density Estimation in statistics.

In summary, Machine Learning is all about endeavor to teach computers to predict future, or otherwise unspecified events by applying computer science, data science or statistics techniques to analyze historical data. It can be seen as a variation from existing data to improve perception about the unspecified. In R we have lots of packages and algorithms using which we can reach to the world of machine learning.

Machine Learning is ordinarily associated with other continually used terms such as business intelligence, data science, predictive analytics, big data, data mining, etc. Big data whereas handles volume, velocity and variety of data, on the other hand machine learning predicts the accurate out turns dependent on existing data.

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IMPORTANCE OF DATA SCIENTIST….!!

Data Science involves using mathematic and algorithmic techniques to solve some of the most analytically complex business problems. Techno giants such as Google, Amazon and Facebook have data science groups, but the use of the data science is making its way into non-techno companies that are looking to discover information to fuel new revenue streams and streamline business.

The trend is to grow in the data driven analytics area based on big data platforms. As a benefit, organizations do not need to depend on an "intuitive" decision-making process, which might not always incapable of failure or error.

  Data scientists matters because they enables companies to operate and strategize more intelligently. As the name 'Big data' suggests, the worth of data has the highest priority. On the other hand, people with expertise in data science are become an integral part of big data analytics. Data Scientists usually have the following skills:

Has statistical modelling and technical engineering skills

Is practical and not just theoretical about data

Can use analytics to drive key success in generating profitable results

Information architecting skills

  RESPONSIBILITIES OF DATA OFFICER/SCIENTIST/ANALYST

  ü  The data officer/scientists cover all areas such as data collection, data cleaning, data processing, extracting meaningful information by applying statistical algorithms/models, etc.

ü  Explore and examine data from various sources, winnowing through and analyses data for patterns and insights to solve business challenges and develop business strategy, determine what data means, and recommends ways to apply that data.

ü  Good data scientists will not just provide solutions of business problems, they will pick the right problems that have the most value to the organization.

ü  A data scientist does not just collect and report on data, but also looks at it from many angles, determines what it means, then recommends ways to apply the data.

ü  Creating new theories and predictions based upon the data.

ü  Setting information goals what they expect at the end of the project.

    WHAT THEY DON’T DO?

People generally interpret that if a person is scientist, this means he must be always surrounded with chemicals, test tubes, and a lot of experiments in chemistry lab. But no, a Data Scientist plays with data only. Some are the examples which makes you understand that what they don’t.

  They do notWork with test tubes and Bunsen burners.

They do not work with chemicals.

They do not make experiments on animals.

    This data processing is continuous as the input data sources changes frequently. The properties of data, its form and volume all have a significant impact on the statistical analysis. So the data scientists should pass judgment on a regular basis and provide input to the organization.

It's very important to properly separate the meaningful data from the huge volume of input and discard the rest because processing data is costly and time consuming. In the few coming years, data scientists will have in high demand and organizations should invest in resources with an excellent understanding of data science.