Top 5 Reasons Why Data Science is in Love with R Programming Language

Top 5 Reasons Why Data Science is in Love with R Programming Language

![This is image title](https://miro.medium.com/max/1040/1*IJaOVvKEEseucx63gHluIQ.jpeg "This is image title") The arrival of the programming language R has narrowed the scope of other programming languages since it is widely preferred by most data...

The arrival of the programming language R has narrowed the scope of other programming languages since it is widely preferred by most data scientists & researchers and statisticians. R came to view in late 1993 and is a GNU package that empowers statistical computing. Over the past few years, R's popularity has grown manifold, especially in Data analytics field. It's an era of data science, and business analytics is a cornerstone of it. Competition is growing like never before and one cannot afford to lose dollars in lieu of using a wrong tool.

With the availability of so many tools, techies, especially beginners can be confused with which programming tool to opt for. If you are miring yourself in finding the best programming language, stay tuned to this post and get to know why R programming is the lynchpin of Data Science Online Training.

R is for Non-Technicals

Sorting through high-end data science tools will introduce you to the top two tools R or Python. Software engineers with knowledge of math, stats, and Machine Learning prefer Python to any other language, but the problem arises when a developer needs library support for subjects such as Econometrics. Considering the non-technical background of most data science specialists, learning Python is one of the significant challenges for them. Moreover, weaker support of Python for Econometrics which is essential for businesses and finances for communication in form of reports adds one more point to its limitations. In the light of the above points, it is clear that Python is not a reliable solution, and we need to consider the next option-R.

R is used for statistical programming. It supports ML, Stats, and data science libraries to streamline your programming. R and data science share a good relationship that ultimately helps business as the language supports topic-specific packages and, moreover, the infrastructure of its communication is highly specific. Therefore, data scientists show a great interest in R as its libraries support Finance, Econometrics, etc., which makes sense for business analytics.

Tidyverse is a Savior of R

The birth of R brings along its complexity. As structuring and formality were not the first concern in the beginning of programming, R was considered highly inconsistent to learn. However, the advent of Tidyverse changed the scenario completely. Being a pack of packages and tools, Tidyverse provides you with a consistent structural programming interface. Moreover, dplyr and ggplot2 has reduced learning curve complexities greatly. Today, R has achieved the highest level of consistency, all thanks in part to the evolving nature of R. From visualization to iteration, to manipulation, Vidyverse supports everything, which makes R an easy language to learn. To get in-depth knowledge, enroll for live free demo on Data Science Cetification

R can Kickstart Business

What attracts data scientists towards R is its potential for providing business with ready reports and infographics, and ML powered web development. No other language can stand in front R on the grounds of ease and effectiveness. Let's take an example of RMARKDOWN and shiny. RMARKDOWN is a framework, which enables you to create reconstructable reports to build blogs, presentations, websites, books journals, etc. Organizations embrace this tool not only to prepare a business analytics report but also commercialize what this framework provides them with. Shiny is an R empowered framework that helps you create interactive web applications. It is a handy tool, and companies rely on it to achieve web development goals.

Higher Bandwidth for Libraries

R is a powerful business infrastructure that Excel on Steroids from a business perspective. It is capable of implementing various algorithms, for example, high-end Machine learning package (H2O), TensorFlow deep learning packages, xgboost the top Kaggle algorithm, etc., that is probably not possible for other languages to do. Tidyverse is the backbone of the language R. It structural approach triggers consistency during application development no matter how much complex it is. It encompasses an array of libraries like dpylr, tidyr, stringr, lubridate, forecast, etc., that takes development to the next level.

Community Support

Having a great community support is highly critical for any Programming language or interface to excel. R has a huge fan following due to tech enthusiasts that learn and provide beginners with the latest updates. The data science field has already acknowledged the importance of R for developing exclusive reports and streamlining communication.

Thus, we can see how R programming language is influencing modern development trends in data science. Complex business operations from a critical decision making to process optimization are achieved through the collected data from the historical data sets. R with the vast number of packages allows us to accomplish advanced computing tasks like regression, classification, and other scientific computations in just a few minutes. These algorithms lead to accurate results for predictions, modeling, pattern analysis, graphical or statistical representation. Maybe these cool features are the main reason behind the popular friendship between R and data science.

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Data Science vs Data Analytics vs Big Data

Data Science vs Data Analytics vs Big Data

When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them. In this article on Data science vs Big Data vs Data Analytics, I will understand the similarities and differences between them

When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them. In this article on Data science vs Big Data vs Data Analytics, I will understand the similarities and differences between them

We live in a data-driven world. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. Now that Hadoop and other frameworks have resolved the problem of storage, the main focus on data has shifted to processing this huge amount of data. When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them.

In this article on Data Science vs Data Analytics vs Big Data, I will be covering the following topics in order to make you understand the similarities and differences between them.
Introduction to Data Science, Big Data & Data AnalyticsWhat does Data Scientist, Big Data Professional & Data Analyst do?Skill-set required to become Data Scientist, Big Data Professional & Data AnalystWhat is a Salary Prospect?Real time Use-case## Introduction to Data Science, Big Data, & Data Analytics

Let’s begin by understanding the terms Data Science vs Big Data vs Data Analytics.

What Is Data Science?

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data.

[Source: gfycat.com]

It also involves solving a problem in various ways to arrive at the solution and on the other hand, it involves to design and construct new processes for data modeling and production using various prototypes, algorithms, predictive models, and custom analysis.

What is Big Data?

Big Data refers to the large amounts of data which is pouring in from various data sources and has different formats. It is something that can be used to analyze the insights which can lead to better decisions and strategic business moves.

[Source: gfycat.com]

What is Data Analytics?

Data Analytics is the science of examining raw data with the purpose of drawing conclusions about that information. It is all about discovering useful information from the data to support decision-making. This process involves inspecting, cleansing, transforming & modeling data.

[Source: ibm.com]

What Does Data Scientist, Big Data Professional & Data Analyst Do?

What does a Data Scientist do?

Data Scientists perform an exploratory analysis to discover insights from the data. They also use various advanced machine learning algorithms to identify the occurrence of a particular event in the future. This involves identifying hidden patterns, unknown correlations, market trends and other useful business information.

Roles of Data Scientist

What do Big Data Professionals do?

The responsibilities of big data professional lies around dealing with huge amount of heterogeneous data, which is gathered from various sources coming in at a high velocity.

Roles of Big Data Professiona

Big data professionals describe the structure and behavior of a big data solution and how it can be delivered using big data technologies such as Hadoop, Spark, Kafka etc. based on requirements.

What does a Data Analyst do?

Data analysts translate numbers into plain English. Every business collects data, like sales figures, market research, logistics, or transportation costs. A data analyst’s job is to take that data and use it to help companies to make better business decisions.

Roles of Data Analyst

Skill-Set Required To Become Data Scientist, Big Data Professional, & Data Analyst

What Is The Salary Prospect?

The below figure shows the average salary structure of **Data Scientist, Big Data Specialist, **and Data Analyst.

A Scenario Illustrating The Use Of Data Science vs Big Data vs Data Analytics.

Now, let’s try to understand how can we garner benefits by combining all three of them together.

Let’s take an example of Netflix and see how they join forces in achieving the goal.

First, let’s understand the role of* Big Data Professional* in Netflix example.

Netflix generates a huge amount of unstructured data in forms of text, audio, video files and many more. If we try to process this dark (unstructured) data using the traditional approach, it becomes a complicated task.

Approach in Netflix

Traditional Data Processing

Hence a Big Data Professional designs and creates an environment using Big Data tools to ease the processing of Netflix Data.

Big Data approach to process Netflix data

Now, let’s see how Data Scientist Optimizes the Netflix Streaming experience.

Role of Data Scientist in Optimizing the Netflix streaming experience

1. Understanding the impact of QoE on user behavior

User behavior refers to the way how a user interacts with the Netflix service, and data scientists use the data to both understand and predict behavior. For example, how would a change to the Netflix product affect the number of hours that members watch? To improve the streaming experience, Data Scientists look at QoE metrics that are likely to have an impact on user behavior. One metric of interest is the rebuffer rate, which is a measure of how often playback is temporarily interrupted. Another metric is bitrate, that refers to the quality of the picture that is served/seen — a very low bitrate corresponds to a fuzzy picture.

2. Improving the streaming experience

How do Data Scientists use data to provide the best user experience once a member hits “play” on Netflix?

One approach is to look at the algorithms that run in real-time or near real-time once playback has started, which determine what bitrate should be served, what server to download that content from, etc.

For example, a member with a high-bandwidth connection on a home network could have very different expectations and experience compared to a member with low bandwidth on a mobile device on a cellular network.

By determining all these factors one can improve the streaming experience.

3. Optimize content caching

A set of big data problems also exists on the content delivery side.

The key idea here is to locate the content closer (in terms of network hops) to Netflix members to provide a great experience. By viewing the behavior of the members being served and the experience, one can optimize the decisions around content caching.

4. Improving content quality

Another approach to improving user experience involves looking at the quality of content, i.e. the video, audio, subtitles, closed captions, etc. that are part of the movie or show. Netflix receives content from the studios in the form of digital assets that are then encoded and quality checked before they go live on the content servers.

In addition to the internal quality checks, Data scientists also receive feedback from our members when they discover issues while viewing.

By combining member feedback with intrinsic factors related to viewing behavior, they build the models to predict whether a particular piece of content has a quality issue. Machine learning models along with natural language processing (NLP) and text mining techniques can be used to build powerful models to both improve the quality of content that goes live and also use the information provided by the Netflix users to close the loop on quality and replace content that does not meet the expectations of the users.

So this is how Data Scientist optimizes the Netflix streaming experience.

Now let’s understand how Data Analytics is used to drive the Netflix success.

Role of Data Analyst in Netflix

The above figure shows the different types of users who watch the video/play on Netflix. Each of them has their own choices and preferences.

So what does a Data Analyst do?

Data Analyst creates a user stream based on the preferences of users. For example, if user 1 and user 2 have the same preference or a choice of video, then data analyst creates a user stream for those choices. And also –
Orders the Netflix collection for each member profile in a personalized way.We know that the same genre row for each member has an entirely different selection of videos.Picks out the top personalized recommendations from the entire catalog, focusing on the titles that are top on ranking.By capturing all events and user activities on Netflix, data analyst pops out the trending video.Sorts the recently watched titles and estimates whether the member will continue to watch or rewatch or stop watching etc.
I hope you have *understood *the *differences *& *similarities *between Data Science vs Big Data vs Data Analytics.

Data Science Tutorial Using R

Data Science Tutorial Using R

This video on "Data Science Tutorial Using R" will give you an in-depth understanding of Data Science and you’ll also learn how Data Science is used in the real world to solve data-driven problems.

Below are the topics covered in this Data Science Course for Beginners:

  • Need for Data Science
  • Walmart Use case
  • What is Data Science?
  • Who is a Data Scientist?
  • Data Science – Skill set
  • Data Science Job roles
  • Data Life cycle
  • Introduction to Machine Learning
  • K- Means Use case
  • K- Means Algorithm
  • Hands-On
  • Data Science certification

R Programming For Beginners - R Language Tutorial - R Tutorial For Beginners

R Programming For Beginners - R Language Tutorial - R Tutorial For Beginners

What you’ll learn

  • You will learn how to navigate in the RStudio interface
  • You will learn how to make basic graphs
  • You will learn about the basic structure of R including packages
  • You will learn how to perform basic commands in the R programming language
  • You will also learn how to handle add on packages, how to use the R help tools and generally how to find your way in the R world.


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