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R Programming Analytics Data Science Course

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What is R programming?

R programming is the most widely and very famous programming language. It is specifically used for

Machine learning

Data science

Data analysis

Data visualization

R is known as the best programming language in the field of Data sciences and is commonly used for any type of statistician. It possesses a complete catalog of statistical and graphical methods. The extensive library of tools of the R catalog serves as the best way to analyze data and visualize it in any complex graph. For most of the complex and informative graphs you see, there is a huge chance that R is behind them.

Benefits of R programming:

R is an extensive library of various statistical and graphical approaches. It offers Linear, non-linear modeling, Time-series, spatial analysis, Statistical tests, Clustering platforms, classification, and much more techniques.

The most beneficial function of R programming is the graphic analysis of data and its representation in high-end graphs. Those graphs are of publication quality which represents many mathematical symbols also. Many interactive graphics are offered through additional packages.

R is also a very useful programming language and offers many extensible features and techniques to help developers in programming.

R programming plays an important role in

Data processing

Data cleaning

Data preparation

Machine learning

Most of the foreign and big companies like Google, Uber, Facebook, etc who handle a lot of data use R programming language for many functionalities.

What is Data Science?

To extract value from data, data science combines multiple domains such as statistics, scientific methodologies, artificial intelligence (AI), and data analysis. Data scientists are individuals that use a variety of talents to analyze data acquired from the web, cellphones, consumers, sensors, and other sources in order to generate actionable insights. Data science is a branch of study that tries to extract meaning and insights from data using a scientific method.

Using R for Data Science

Learning the R programming language may be of interest to anyone interested in data research. R may be used for statistical analysis and other purposes in data science. There are a few different ways to get started learning R. Continue reading to learn more about R in data science, R vs. Python, R in the real world, the best R add-on packages, and more.

What is the Future of Data Science?

Without data science, no company, or industry for that matter, will be able to keep up. A substantial number of transformations have already occurred throughout the world, with firms seeking more data-driven choices, and more are on the way. Data science has been termed the oil of the twenty-first century for its infinite possibilities across sectors. If you are serious about following this road, your efforts will be rewarded not just with a rewarding profession and large paychecks, but also with a high level of job stability.

What is Data analysis?

Data analysis is the act of analyzing, cleansing, manipulating, and modeling data in order to identify usable information, generate conclusions, and help decision-making. Data analysis has several dimensions and approaches, including a wide range of techniques known by various names and applied in a variety of business, science, and social science sectors. In today's corporate world, data analysis plays an important part in making decisions more scientific and assisting firms in operating more efficiently.

R programming in Data analysis:

R programming is a powerful tool for maximizing data reporting. Instead of using a separate development tool like R Studio or Jupyter Notebooks to use programming languages, you can integrate R directly into your analytics stack, allowing you to predict critical business outcomes, create interactive dashboards using practical statistics, and easily build statistical models. By using R, complex analyses may be performed more quickly and with more accurate and up-to-date data.

R is used in a variety of businesses and disciplines nowadays. Building custom data collecting, grouping, and analytical models are frequent use of R for business analytics.

Rather than using a pre-packaged strategy, R data analysis enables businesses to build statistics engines that can provide better, more relevant insights due to more precise data gathering and storage.

As a result, organizations may swiftly custom-build analytical systems that can integrate with existing statistical analyses while giving considerably deeper and more accurate insights.

Even with social media or web data, R can typically produce models that yield better or more particular insights than common measurements such as page views or bounce rates.

Why should you learn R?

An R programmer in India earns Rs 5 million per anum as an average base salary and can also earn up to Rs 9 million per anum.

Many big companies like Google, Facebook, and uber also hire R programmers and offers very high pay. If you are an R programmer you can be hired as a Data Scientist and Business Analyst. R programmers are in very high demand in Startup companies.

R programming has a wide range scope as well as it can also be used by business experts to grow their business.

The best feature of R programming is Graphics which can be used in any field to get benefits. It is very easy to use and gives you a high output. R programming language is a very flexible and best scripting language and offers you the best resource bank. R has a big community that helps programmers and data scientists a lot.

R is the best language for data analysis because it offers programmers an enormous number of packages and many usable tests and formulas for Data analysis. R is also useful for parallel computation.

What will you learn in this course?

This course will provide you with in-depth teaching on the following topics to enhance your performance in this field:

Course Introduction

Data Preparation

Lists in R

Apply Family of Functions

Requirements

Basic R knowledge is required.

It is recommended that you are familiar with the GGPlot2 program.

Data frame knowledge

Understanding of vectors and vectorized operations

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Project management entails a wide range of abilities, including interpersonal skills, managerial capability, and leadership abilities, which are distinct from technical project expertise. It is not required that you have excellent technical abilities to be a successful project manager. This course (leading to certification) has taken care of all of these crucial issues, and it boasts the following advantages:

No other courseware can deliver such in-depth and well-researched course material, which has gone to great lengths to incorporate even the tiniest details to present you with a-z information on the subject.

The goal of Brainmeasures' project management course is to teach managers about project management. It clearly teaches the fundamentals of project management, from project definition to helping managers comprehend the future of project management.

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Course Syllabus

Getting Started 11 lectures 17 mins
Introduction Preview 01:42
Welcome guide document 10 Pages
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Welcome guide document 10 Pages
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2 Some title goes here 07:42
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Welcome guide document 10 Pages
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2 Some title goes here 07:42
Hello Some title goes here 07:42
This is Some title goes here 07:42
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Welcome guide document 10 Pages
Some title goes here 07:42
2 Some title goes here 07:42
Hello Some title goes here 07:42
This is Some title goes here 07:42
Some title goes here Preview 01:42
Welcome guide document 10 Pages
Some title goes here 07:42
2 Some title goes here 07:42
Hello Some title goes here 07:42
This is Some title goes here 07:42
Some title goes here Preview 01:42
Welcome guide document 10 Pages
Some title goes here 07:42
2 Some title goes here 07:42
Hello Some title goes here 07:42
This is Some title goes here 07:42
Course

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I am very happy with the guidance and help provided by experienced and helpful tutors from Brainmeasures and this video online course is very straightforward and explains all the topics in detail thereby enabling you to understand the subject and gain an in-depth knowledge about all the concepts of Six Sigma.

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