R ~ NOT JUST AN ALPHABET

R is a programming language and software environment for statistical analysis, graphics representation and reporting. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows and Mac. This programming language was named R, based on the first letter of first name of the two R authors (Robert Gentleman and Ross Ihaka), and partly a play on the name of the Bell Labs Language S.

Why use R for statistical computing and graphics?

1. R is open source and free!
   R is free to download as it is licensed under the terms of GNU General Public license.You can look at the source to see what’s happening under the hood. There’s more, most R packages are available under the same license so you can use them, even in commercial applications without having to call your lawyer.
2. R is popular – and increasing in popularity
   IEEE publishes a list of the most popular programming languages each year. R was ranked 5th in 2016, up from 6th in 2015. It is a big deal for a domain-specific language like R to be more popular than a general purpose language like C#.This not only shows the increasing interest in R as a programming language, but also of the fields like Data Science and Machine Learning where R is commonly used.
3. R runs on all platforms
   You can find distributions of R for all popular platforms – Windows, Linux and Mac.
   R code that you write on one platform can easily be ported to another without any issues. Cross-platform interoperability is an important feature to have in today’s computing world – even Microsoft is making its coveted .NET platform available on all platforms after realizing the benefits of technology that runs on all systems.
4. Learning R will increase your chances of getting a job
   According to the Data Science Salary Survey conducted by O’Reilly Media in 2014, data scientists are paid a median of $98,000 worldwide. The figure is higher in the US – around $144,000.Of course, knowing how to write R programs won’t get you a job straight away, a data scientist has to juggle a lot of tools to do their work. Even if you are applying for a software developer position, R programming experience can make you stand out from the crowd.
5. R is being used by the biggest tech giants
   Adoption by tech giants is always a sign of a programming language’s potential. Today’s companies don’t make their decisions on a whim. Every major decision has to be backed by concrete analysis of data.

Should you choose R?

Data scientist can use two excellent tools: R and Python. You may not have time to learn them both, especially if you get started to learn data science. Learning statistical modeling and algorithm is far more important than to learn a programming language. A programming language is a tool to compute and communicate your discovery. The most important task in data science is the way you deal with the data: import, clean, prep, feature engineering, feature selection. This should be your primary focus. If you are trying to learn R and Python at the same time without a solid background in statistics, its plain stupid. Data scientist are not programmers. Their job is to understand the data, manipulate it and expose the best approach. 

 Advantages and Disadvantages of R

There are various benefits of R as well as limitations of R language. Let us discuss them-

i. Advantages of R Programming

• R is the most comprehensive statistical analysis package as new technology and ideas often appear first in R.
• R is open-source software. Hence anyone can use and change it.
• R is an open source. We can run R anywhere and at any time, and even sell it under conditions of the license.
• R is good for GNU/Linux and Microsoft Windows. R is cross-platform which runs on many operating systems.
• In R, anyone is welcome to provide bug fixes, code enhancements, and new packages.

ii. Disadvantages of R Programming

• In R, quality of some packages is less than perfect.
• In R, no one to complain, if something doesn’t work.
• R is a software Application that many people devote their own time to developing.
• R commands give little thought to memory management, and so R can consume all available memory.

List of Big Companies using R

Google

Google uses R in many ways. They use R to make online advertising more effective. They also use for economic forecasting and and Big-data statistical modeling.

Facebook

Facebook uses R for Exploratory Data Analysis, Experimental Analysis, Big-Data Visualization, Human Resources, and user behaviour analysis related to status updates and profile pictures. 

Twitter

Twitter uses R for Data Visualization and Semantic Clustering. Twitter has created some super impressive projects using R.  They also has created open-source packages for anomaly and breakout detection, which has helped improve their customer experience.

Airbnb

Most of the Airbnb data scientists use R and they also have an internal R package called Rbnb. They use R to scale data science. They use R to predict re-booking rates using past guest ratings and to automate guest or host matching.

Microsoft

Microsoft acquired Revolution Analytics in 2015 and they use R in many ways. Microsoft Azure uses R for understanding user behavior (how users configure monitoring platform), Visualizing infrastructure utilization data,
Abnormal login detection, Custom R packages to analyze monitoring data (time series anomaly detection), Forecasting hardware purchase requirements (forecast package). Also Xbox uses R for a great gaming experience. They also have packages RHadoop and ParallelR for Big Data.

IBM

IBM Invests in R Programming Language for Data Science; joins R Consortium. The company has relied on R, among other data languages, to help create innovative solutions like IBM Watson, an open, cognitive computing technology platform that represents a new era in computing where systems understand the world like humans do: through senses, learning, and experience. As a member of the R Consortium, IBM will collaborate with the R user community and support the project’s mission to identify, develop and implement infrastructure projects that drive standards and best practices for R code.

New York Times

The New York Times uses R for interactive features (such as the Dialect Quiz and the Election Forecast); for data journalism (including these stories on NFL draft picks and wealth distribution in the USA); and for data visualization (such as in articles on the Facebook IPO and baseball pitching legends).

Novartis

R is well-known for its use in pre-clinical data analysis in drug research. And Swiss multinational pharmaceutical company Novartis Relies on analyzing clinical trial data for FDA submissions with R.

To Know which book to Refer go to-
https://www.r-bloggers.com/best-books-to-learn-r-programming/
REFERENCES-

https://www.tutorialspoint.com/r/index.htm

https://www.guru99.com/r-programming-introduction-basics.html

https://www.datamentor.io/r-programming/

https://data-flair.training/blogs/why-learn-r/
http://makemeanalyst.com/companies-using-r/

ALL ABOUT KOTLIN

WHAT IS KOTLIN?

Kotlin is a cross-platform, statically typed, general-purpose programming language with type inference. Kotlin is designed to interoperate fully with Java, and the JVM version of its standard library depends on the Java Class Library, but type inference allows its syntax to be more concise. Kotlin mainly targets the JVM, but also compiles to JavaScript or native code (via LLVM). Kotlin is sponsored by JetBrains and Google through the Kotlin Foundation.

Kotlin is officially supported by Google for mobile development on Android. Since the release of Android Studio 3.0 in October 2017, Kotlin is included as an alternative to the standard Java compiler. The Android Kotlin compiler lets the user choose between targeting Java 6 or Java 8 compatible bytecode.

KOTLIN EVOLUTION:

                   " Language design is cast in stone,

                    but this stone is reasonably soft,

                    and with some effort we can reshape it later. "

              ~Kotlin Design Team

Kotlin is designed to be a pragmatic tool for programmers. When it comes to language evolution, its pragmatic nature is captured by the following principles:

Keep the language modern over the years.

Stay in the constant feedback loop with the users.

Make updating to new versions comfortable for the users.

As this is key to understanding how Kotlin is moving forward, let's expand on these principles.

1.Keeping the Language Modern.

We acknowledge that systems accumulate legacy over time. What had once been cutting-edge technology can be hopelessly outdated today. We have to evolve the language to keep it relevant to the needs of the users and up-to-date with their expectations. This includes not only adding new features, but also phasing out old ones that are no longer recommended for production use and have altogether become legacy.

2.Comfortable Updates.

Incompatible changes, such as removing things from a language, may lead to painful migration from one version to the next if carried out without proper care. We will always announce such changes well in advance, mark things as deprecated and provide automated migration tools before the change happens. By the time the language is changed we want most of the code in the world to be already updated and thus have no issues migrating to the new version.

3.Feedback Loop.

Going through deprecation cycles requires significant effort, so we want to minimize the number of incompatible changes we'll be making in the future. Apart from using our best judgement, we believe that trying things out in real life is the best way to validate a design. Before casting things in stone we want them battle-tested. This is why we use every opportunity to make early versions of our designs available in production versions of the language, but with experimental status. Experimental features are not stable, they can be changed at any time, and the users that opt into using them do so explicitly to indicate that they are ready to deal with the future migration issues. These users provide invaluable feedback that we gather to iterate on the design and make it rock-solid.

ADVANTAGES OF KOTLIN:

1. Language and environment are mature:

Unlike other languages like Swift, the Kotlin release has gone through many stages before releasing the final 1.0 release.This means that there are hardly any problems when working with Kotlin, everything works as you would expect

2. It makes Android development much easier:

If you’ve been reading this series of articles about Kotlin, you’ve probably seen it crystal clear many times.Kotlin is simplicity mixed with power, so you have an new world of possibilities if you’re coming from Java that you couldn’t even imagine.

3.  Shrinks the Base code to Enhance Productivity:

Developers often make certain shortcuts while programming to reach a deadline. This is called technical debt that generally reduces productivity and slows down the development.

Kotlin helps developers to reduce technical debt to a certain extent. It shrinks the base code and which further improves the code quality. If you shift to Kotlin, you will see a reduction in the number of lines of code. This makes code maintenance a lot easier.

4. Kotlin is Concise:

Kotlin is a way more concise than Java. It doesn’t only save time but also promotes readability and code maintainability.

If you are a developer, you won’t have to deep dive into the sea of logic and code to solve a simple bug. With Kotlin, you can read, write and change code more efficiently. Kotlin brings many useful features like smart casts, data classes, type interference, etc.

DISADVANTAGES OF KOTLIN:

Although Kotlin is great, it’s not perfect. Here are a few aspects of the language that I’m not in love with.

1. No namespaces:

Kotlin allows you to define functions and properties at the top level of your files. That’s a great feature, but it can cause some confusion when combined with the fact that all top level declarations are referenced unqualified from Kotlin. This can sometimes make it difficult to tell what a function is when reading one of its usages

2. No static modifier:

Following on the previous point, Kotlin has unusual handling of static function and property declarations that are called from Java. It’s not bad, but it feels dirtier than necessary.

3. Automatic conversion of Java to Kotlin:

This was the first topic in the list of things I like about Kotlin, and it works well. But because it work so well 80% of the time, many of the cases where it fails can be frustrating.

4.Closed by default:

Every downside to Kotlin I’ve talked about so far are mostly small syntax details that are not quite as clean I’d like, but aren’t a big deal overall. But there’s one design decision that is going to cause a huge amount of pain in the future: All classes and functions in Kotlin are closed by default. It’s a design decision pushed by Effective Java, and it might sound nice in theory, but it’s an obviously bad choice to anyone who’s had to use a buggy or incomplete third-party library.

WHY YOU SHOULD SWITCH TO KOTLIN:

Why would a Java Developer switch to Kotlin?

Java is a widely used programming language and forms the basis of Android development. There can be many reasons why Java cannot always be considered the best choice for Android development.

Reasons can be:

o   Java is not a modern language.

Android only supports a subset of Java 8 features. In a way, Android developers are not able to reap full benefits of Java. In case they require making use of Java, then they will have to stick to Java 7.

o   Java comes with some well-documented language issues like endless try-catch blocks, null-unsafety, NullPointerExceptions and lack of extendability.

o   Java is still a procedural language although it is starting to add lambda expressions and functional interfaces recently

o   The syntax of Java is very complex and writing longer code can always be prone to more errors and bugs

o   Java can create issues with Android API design

o   Do you still need to remember most of the code while you code in Java? It can be creepy and messy sometimes!

WHY IS GOOGLE SUPPORTING KOTLIN FOR ANDROID DEVELOPMENT?

1. The Modernity:

Having Java integrated in your work life is a good thing, but more than often you look at a problem, stop for a second to think to yourself : “Is this really the best way I can do it in Java?”.

Well, Kotlin has all the features of modern languages you’d want! Kotlin has a safe Nullability system, Lambdas and Method references for clean connections between parts of your App, a built in Mutability/Immutability property system, a clean and easy to use Stream operators(filter, map, reduce..), to improve your development life and productivity!

2.  The Interop:

Yeah all the features above sound pretty decent, and one might think, okay, but how will this affect my legacy code, or current state of a newly started project? You don’t have to worry a single bit, Kotlin and Java are 100% interoperable, meaning you can have any percentage of code written in Kotlin, and the rest in Java, and it will all just work together!

Even better, there is a tool in AS that allows you to instantly “translate” Java into Kotlin, which works quite well most of the time, so you can have a glimpse into what does a method in Java look like in Kotlin.

3. The Community:

Java is great, it has a large number of users, the tutorial sea is vast, and you can pretty much find an answer to any problem on SO right? But the language itself is very closed, it’s development is hardly influenced by the needs of Java developers. Kotlin is quite the opposite! Jetbrains team takes every idea and proposal from the community and keeps them in mind while developing the language.

The language is also Open sourced on Github, so you can check it out anytime!

4.  The Native:

This might not be an Android specific reason, but it’s definitely worth the mention. Kotlin Native is a milestone Jetbrains is working on reaching, with the idea of running Kotlin code in mutliple platforms, without having to compile it down to JVM, making it much faster, and not requiring Java. The platforms to be are Web (both backend and frontend), iOS (yes!) and Game development.

TO KNOW WHICH BOOKS TO REFER GOTO:

https://kotlinlang.org/docs/books.html

REFERENCES:

https://kotlinlang.org/docs/reference/evolution/kotlin-evolution.html

https://www.technotification.com/2019/03/why-use-kotlin-over-java.html

https://antonioleiva.com/reasons-kotlin-android/

https://www.konstantinfo.com/blog/kotlin-vs-java/

https://medium.com/keepsafe-engineering/kotlin-the-good-the-bad-and-the-ugly-bf5f09b87e6f

https://medium.cobeisfresh.com/5-reasons-why-google-supporting-the-kotlin-programming-language-for-android-is-the-best-news-ever-e4c84be05382

BIG DATA- WHAT IS SO BIG ABOUT IT?

• BIG DATA

Big Data basically refers to, huge volume of data that cannot be, stored and processed using the traditional approach within the given time frame.The next big question that comes to our mind is?

How huge this data needs to be? In order to be classified as Big Data?

There is a lot of misconception, while referring the term Big Data. We usually use the term big data, to refer to the data, that is, either in gigabytes or terabytes or petabytes or exabytes or anything that is larger than this in size. This does not defines the term Big Data completely. Even a small amount of data can be referred to as Big Data depending on the context it is used.

•    TYPES OF BIG DATA

Big Data could be found in three forms:

1. Structured
2. Unstructured
3. Semi-structured

Structured data:-Any data that can be stored, accessed and processed in the form of fixed format is termed as a structured data.

Unstructured data:- Any data with unknown form or the structure is classified as unstructured data. In addition to the size being huge, un-structured data poses multiple challenges in terms of its processing for deriving value out of it.

Semi-structured data:- Semi-structured data can contain both the forms of data.

•     USES OF BIG DATA

Location tracking :-  

 Logistic companies have been using location analytics to track and report orders for quite some time. With Big Data in the picture, it is now possible to track the condition of the good in transit and estimate the losses. It is now possible to gather real-time data about traffic and weather conditions and define routes for transportation. This will help logistic companies to mitigate risks in transport, improve speed and reliability in delivery.

Advertising :-

Advertisers are one of the biggest players in Big Data. Be it Facebook, Google, Twitter or any other online giant all keep a track of the user behaviour and transactions. These internet giants provide a great deal of data about people to the advertisers so that they can run targeted campaigns. Take Facebook, for example, here you can target people based on buying intent, website visits, interests, job role, demographics and what not. All this data is collected by Facebook algorithms using big data analysis techniques. The same goes for Google, when you target people based on clicks you will get different results and when you create a campaign for leads that you will get different results. All this is made possible using big data.

Entertainment and media:- 

In the field of entertainment and media, big data focuses on targeting people with the right content at the right time. Based on your past views and your behaviour online you will be shown different recommendations. This technique is popularly used by Netflix and YouTube to increase engagement and drive more revenues.

Pros and cons of big data

PROS:

• Fraud detection: Another common use for big data analytics — particularly in the financial services industry — is fraud detection. One of the big advantages of big data analytics systems that rely on machine learning is that they are excellent at detecting patterns and anomalies. These abilities can give banks and credit card companies the ability to spot stolen credit cards or fraudulent purchases, often before the cardholder even knows that something is wrong.

• Savings – Even though implementation of real-time big data analytics can be expensive, the high value of immediate data analysis can make up for this expenditure.

• Strategies toward competitors – Competition scares many people in the market today, and big data analytics assists in providing a detailed picture of competitors, such as launching a new product, lowering/increasing prices for a particular duration or focusing on users from a specific location.

CONS:

·       Privacy:- De-identification or the process of anonymizing data by removing personally identifiable information as a way of justifying mass collection and use of personal data. Big Data can be compliant with the existing regulatory and legal frameworks on data protection.

·       Security:-The larger the quantities of confidential information stored by a certain company in their databases would specifically attract potential hackers. Also, it is a duplication of data to many locations to optimize data queries processing that may result in difficulty in locating and securing all items and personal information.

·       Distraction:At times, the Data Visualization apps create reports and charts laden with highly complicated and fancy graphics, which may be tempting for the users to focus more on form than on function.

 The Future of Big Data:

 

·        Privacy Will Be the Biggest Challenge.

·        Data Scientists Will Be In High Demand.

·        Businesses Will Buy Algorithms, Instead of Software.

·        More Developers Will Join the Big Data Revolution.

·        Big Data Will Help You Break Productivity Records.

Thank You for Visiting Techtical .That was all about Big data. Let us know in the comments section about your thoughts on Big Data. Stay tuned for more interesting blogs on popular topics.