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R Data Science training Bangalore

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R Data Science Training Bangalore

“The ability to take data – to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it’s going to be a hugely important skill in the next decades, not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids. Because now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand that data and extract value from it.
I think statisticians are part of it, but it’s just a part. You also want to be able to visualize the data, communicate the data, and utilize it effectively. But I do think those skills – of being able to access, understand, and communicate the insights you get from data analysis – are going to be extremely important. Managers need to be able to access and understand the data themselves.”
Daryl Pregibon, a research scientist at Google said- “R is really important to the point that it’s hard to overvalue it. It allows statisticians to do very intricate and complicated analyses without knowing the blood and guts of computing systems.”
R data science language is the magic wand for 21st century data scientists to handle and analyse huge amounts of complex and unstructured data productively. The popular saying “Patience is the mother of all virtues” holds good for R language because to learn R programming is definitely a challenge. The commands that work theoretically might not work practically and being a beginner it would be difficult to know what is exactly is going wrong with the code. Thus, students or professionals who want to make a career progression in data science should learn through a comprehensive training that offers hands-on working experience on various projects.

R machine learning training Bangalore

R Data Science training Bangalore


A common use of data mining is to detect patterns or rules in data.
The points of interest are the non-obvious patterns that can only be detected using a large dataset. The detection of simpler patterns, such as market basket analysis for purchasing associations or timings, has been possible for some time. Our interest in R programming is in detecting unexpected associations that can lead to new opportunities.
Some patterns are sequential in nature, for example, predicting faults in systems based on past results that are, again, only obvious using large datasets.
Our experts here on R data science training in Bangalore will cover
the use of R to discover patterns in datasets’ various methods:
  • Cluster analysis: This is the process of examining your data and establishing groups of data points that are similar. Cluster analysis can be performed using several algorithms. The different algorithms focus on using different attributes of the data distribution, such as distance between points, density, or statistical ranges.
  • Anomaly detection: This is the process of looking at data that appears to be similar but shows differences or anomalies for certain attributes. Anomaly detection is used frequently in the field of law enforcement, fraud detection, and insurance claims.
  • Association rules: These are a set of decisions that can be made from your data. Here, we are looking for concrete steps so that if we find one data point, we can use a rule to determine whether another data point will likely exist. R Machine learning training BangaloreRules are frequently used in market basket approaches. In data mining, we are looking for deeper, non-obvious rules that are present in the data.
Our experts here on R data science training in Bangalore will cover the clustering tools for:
  • K-means clustering
  • K-medoids clustering
  • Hierarchical clustering
  • Expectation-maximization
  • Density estimation
We can use R programming to detect anomalies in a dataset. Anomaly detectionR Data Science training Bangalore can be used in a number of different areas, such as intrusion detection, fraud detection, system health, and so on. In R programming, these are called outliers. R programming allows the detection of outliers in a number of ways, as listed here:
  • Statistical tests
  • Depth-based approaches
  • Deviation-based approaches
  • Distance-based approaches
  • Density-based approaches
  • High-dimensional approaches
Association rules describe associations between two datasets. This is most commonly used in market basket analysisR Visualization Training Bangalore. Given a set of transactions with multiple, different items per transaction (shopping bag), how can the item sales be associated? The most common associations are as follows:
  • Support: This is the percentage of transactions that contain A and B.
  • Confidence: This is the percentage (of time that rule is correct) of cases containing A that also contain B.
  • Lift: This is the ratio of confidence to the percentage of cases containing B. Please note that if lift is 1, then A and B are independent.
we discussed cluster analysis, anomaly detection, and association rules. In cluster analysis, we use k-means clustering, k-medoids clustering, hierarchical clustering, expectation-maximization, and density estimation. In anomaly detection, we found outliers using built-in R functions and developed our own specialized R function. R Data Science training BangaloreFor association rules, we used the apriori package to determine the associations amongst datasets.
R is one of the most popular programming languages used in computation statistics, data visualization, and data science. With the increasing number of companies becoming data-driven, the user base of R is also increasing fast. R is supported by over two million users worldwide.
In this book, you will learn how to use R to load data from different sources, carry out fundamental data manipulation techniques, extract the hidden patterns in data through exploratory data analysis, and build complex predictive as well as forecasting models. Finally, you will learn to visualize and communicate the data analysis to the audience. This book is aimed at beginners and intermediate users of R, taking them through the most important techniques in data science that will help them start their data scientist journey.
Our experts here on R data science training in Bangalore will cover will be covering the basic concepts of R such as reading data from different sources, understanding the data format, learning about the preprocessing techniques, and performing basic arithmetic and string operations.
Our experts here on R data science training in Bangalore will cover will essentially be exploring the standard techniques that will be used to convert the raw data into a usable format.
We shall gloss over the following topicsr:
  • Reading data from different sources
  • Discussing data types in R
  • Discussing data preprocessing techniques
  • Performing arithmetic operations on the data
  • Performing string operations on the data
  • Discussing control structures in R
  • Bringing the data into a usable format
R data Science Training BangaloreExploratory Data Analysis
Exploratory data analysis is a very important topic in the field of data analysis. It is an approach of analyzing the data and summarizing the main characteristics of the dataset. The main objective of exploratory data analysis is to check various hypotheses in order to get a better understanding about the dataset.
Exploratory data analysis includes many statistical techniques and visual and nonvisual analysis. When your study has to be communicated with peers as well as with other audience with non-data science backgrounds, it is advisable to use a lot of visual techniques that help in better communications.
Some of the expectations out of exploratory data analysis are getting insights out of the data, extracting the important variables in the dataset (depending on the problem to be solved), identifying the outliers in the data, and getting results of various testing hypotheses. These results play a very important role in how to solve the business problems, and if it is a modeling problem, then deciding on which model to use and how to apply it to the dataset for enhanced accuracy.
Our experts here on R data science training in Bangalore will cover how to perform exploratory data analysis starting with getting a generalized view on the data, analysis of one variable at a time, then bi-variable analysis, and finally, analyzing multiple variables to get a better understanding on interdependencies.
The topics that will be covered are as follows:
  • Titanic dataset
  • Descriptive statistics
  • Inferential statistics
  • Univariate analysis
  • Bivariate analysis
  • Multivariate analysis (scatter plot with segments, heatmap, and tabulation)
The Titanic dataset
let’s use the Titanic dataset, which is available on the Internet and also hosted on GitHub, to implement various techniques. Place the dataset in the current working directory in R; before this, first set the working directory accordingly using the setwd() command. The setwd() function is used to specify the location that should be considered as the current working directory. Now, read the data using the read.csv function and store it in a data frame. In this book, we have named the data frame tdata. The various details that are present in the dataset, which is hosted on GitHub, are as follows:
tdata<- read.csv(“titanic.csv”)


R Data Science Training Bangalore

Descriptive statistics

Descriptive statistics is a method of summarizing a dataset quantitatively. These summaries can be simple quantitative statements about the data or a visual representation sufficient enough to be part of the initial description about the dataset.
To get a basic understanding about the dataset, we can use the built-in function summary. This function quickly scans the dataset and provides the following information about the dataset. This will really help in getting a first-cut understanding about the data. This will be useful for numerical as well as categorical data.
The output is as follows:
R data Science training Bangalore
We can represent the data presented by summary in a graphical format using the boxplot function.
We will now explore the gender of the passengers who travelled in the Titanic. We can get the count easily through the summary function, but we will plot this using a pie chart:
pieChart<- ggplot(tdata, aes(x = factor(1), fill = factor(tdata$Sex))) + geom_bar(width = 1)
pieChart + coord_polar(theta = “y”) +
ggtitle(“Male and female”)R data Science training Bangalore
The output of the preceding code snippet is as follows:


R Overview,History

R was designed by statisticians and was specialized for statistical computing, and thus is known as the lingua franca of statistics. As technology improves, the data companies or research institutions collect has become more and more complex, and R has been adopted by many as the language of choice to analyze data.
Named from the initials of the two men who first developed the language at the University of Auckland, Robert Gentleman and Ross Ihaka, R has become very popular in recent years and is continuing to become more so, due to the explosion in analytic activities being carried out by business.
R is great for machine learning, data visualization and analysis, and some areas of scientific computing.
R is Becoming the Standard
In a world in which time is limited and that involved in learning a statistical package is nontrivial, learning to program in a system that is unpopular or unsustainable can be futile and frustrating.  I will not make any predictions as to the life expectancy of any propriety software options out there except to say, there are a lot of expensive options in a market in which the most competitive option (R) is free.  I do not know how long proprietary options will be around, but some version of R is likely to remain popular for the indefinite future.  R is well maintained by an active and highly talented community.
Thus, as the emerging standard for statistical programming, it is likely to be a highly rewarding process (both fiscally and in terms of opportunities) to learn to use R.

R Data Science Training Bangalore

R Community

Thanks to this huge user base, just about every function that you might need for data analysis is available, often through open source extensions (known as packages) made available by the community. It is also capable of executing code written in other languages such as C++ or Java, so resources coded in those languages can be made available. Because it can be compiled to run on any major operating system, R code can easily be ported between Unix, Windows or Mac environments.
Python is probably R’s biggest rival – but as both are non-commercial entities (as are most languages, computer or otherwise!) it’s not necessarily a rivalry in the traditional sense. However coders will often argue vociferously for their favorite of the two. Python, having more in common with more traditional, longer established programming languages, is often cited as being easier to learn, particularly for someone with prior experience of different high-level programming languages. The R environment, on the other hand, is likely to be more familiar to someone with an academic background in statistics.
It’s worth noting that Python tends to have a wider range of uses outside of the world of statistics and analytics, whereas R is generally exclusively used for those purposes.
R Data Science training Bangalore
With a reported two million users worldwide, and thousands of deployed applications created using it,R is undoubtedly one of the backbone technologies of the Big Data revolution. If you are thinking of getting involved with the techie end of data analysis, then a thorough grounding in the language should be considered an essential element of your toolbox. If you want to learn more, or have a go at creating your own code in R to see what it can do, there are plenty of great resources online, such as those at CourseraCode School and R Studio .
The R community is diverse, with many individuals coming from unique professional backgrounds. This list includes academics, scientists, statisticians, business analysts and professional programmers, among others. CRAN ,R Machine learning training Bangalore the comprehensive R Archive Network, maintains packages created by community members that reflect this colorful background. Packages exist to perform stock market analysis, create maps, engage in high-throughput genomic analysis and do natural language processing.  This is only the tip of the iceberg; over 7000 packages are available on CRAN as of this writing. Additionally, R-Bloggers is a blog-aggregation site that serves as a hub for news related to the R community.
R Job Prospects

Technology is fun, sure, but most of us who enjoy it also do it for a living. Fortunately, R is not only a pleasure to use, but its demand in business often equates to higher salaries for its practitioners. The Dice Technology Salary Survey conducted last year ranked R as a highest-paying skill. The most recent O’Reilly Data Science Salary Survey also includes R among the skills used by the highest paid data scientists.
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R is Popular with Employers
In two recent studies including one of over 17,000 technology professionals, R was the highest paid technical skill with an average salary of 115,531 (Read more on this here).
R job prospects are rapidly increasing comparing R against a host of alternative software.  The search for R is complicated by the difficulty of its ambiguous name.  Graph (borrowed from  Robert A. Meuchen’s blog).R Data Science machine learning training Bangalore
But why do we care how popular R is?  Programming languages (which all statistical software worth their salt have) are highly dependent upon their user base in order to develop.  How fast they develop, how powerful they are, and how long they expect to be supported is entirely based on how widely they are used.
R is fun

And, of course, R is FUN! Initially, I was drawn to R for its ability to generate charts and plots in very few lines of code.; tasks that would require several hundred lines of code in another language could be accomplished in only a few lines. While it’s considered quirky when you compare it with many popular languages, it includes powerful features specifically geared toward data analysis. For example, if you run the following snippet at the R prompt:
R is worth learning for these reasons and more. Its growth and maturity have led to widespread adoption and many resources for learning. And now with Microsoft stepping up and including R in more of its offerings, you can expect to hear more about R in the months and years to come.
Programmers are usually attracted to learn R programming because of its extraordinary capabilities to generate plots and charts with just few lines of code which would otherwise require several 100’s of lines of code in any other language. R language does have a steep learning curve but when programmers start learning R they really enjoy the powerful features it provides which are geared towards complex data analysis.
Who uses R?

R is in heavy use at several of the best companies who are hiring data scientists. Google and Facebook – who I consider to be two of the best companies to work for in our modern economy – both have data scientists using R.
“R is also the tool of choice for data scientists at Microsoft, who apply machine learning to data from Bing, Azure, Office, and the Sales, Marketing and Finance departments.”
Beyond tech giants like Google, Facebook, and Microsoft, R is widely in use at a wide range of companies including Bank of America, Ford, TechCrunch, Uber, and Trulia.
R isn’t just a tool for industry. It is also very popular among academic scientists and researchers, a fact attested to in a recent profile of the R programming language in the prestigious journal Nature.
R’s popularity in academia is important because that creates a pool of talent that feeds industry.
With the growing popularity and functionality of R language, it is going stay for long as organizations like Google, Pfizer, Bank of America, Merck, Oracle widely adopt its usage for complex business analytics. A powerful community, strong partners and a promise of providing easy-to-integrate solutions, R language is capitalizing big data analytics revolution.
R in business
R originated as an open-source version of the S programming language in the 90s. Since then, it has gained the support of a number of companies, most notably RStudio and Revolution Analytics which created tools, packages, and services related to the language. But it isn’t limited to these more specialized companies; R also has support from large companies that power some of the largest relational databases in the world. Oracle, for one, has incorporated R into its offerings . Earlier this year Microsoft acquired Revolution Analytics and is including the language in SQLServer 2016.  SQLServer administrators and .NET developers now have R at their fingertips, installed with their standard platform tools.
R in higher education
Here’s a fun fact: R originated in academia. Ross Ihaka and Robert Gentleman at the University of Auckland in New Zealand created it, and it’s been widely adopted in graduate programs that include intensive statistical study. R has also been used in Massive open online courses (MOOCs) such as the Coursera Data Science Program. Folks taking graduate studies that involve crunching data are bound to encounter R, and like many other technologies, its introduction in schools leads naturally to its wider adoption in industry. R’s presence in higher education is confirmation of the demand for these skills in business settings.

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