R for Python Data Science: Learn Data Manipulation with R

Welcome to R for Data Science: Learn Data Manipulation With R course.

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R, Python data science with R programming, handle with data, manipulate data and outcomes with R (programming language)

Machine learning and data analysis are big businesses. The former shows up in new interactive and predictive smartphone technologies, while the latter is changing the way businesses reach customers. Learning R from a top-rated Oak Academy's instructor will give you a leg up in either industry.

R is the programming language of choice for statistical computing. Machine learning, data visualization, and data analysis projects increasingly rely on R for its built-in functionality and tools. And despite its steep learning curve, R pays to know.
Data science application is an in-demand skill in many industries worldwide — including finance, transportation, education, manufacturing, human resources, and banking. Explore data science courses with Python, statistics, machine learning, and more to grow your knowledge. Get data science training if you’re into research, statistics, and analytics.
In this course, you will learn how to code with R Programming Language, manage and analyze data with R programming and report your findings.

R programming language is a leading data mining technology. To learn data science, if you don’t know which high return programming language to start with. The answer is R programming.

Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. If you want to advance in your career as a data scientist, R is a great place to start your data science journey.

R is not just a programming language, but it is also an interactive environment for doing data science. Moreover, R is a much more flexible language than many of its peers.

Throughout the course, you will learn the most important tools in R that will allow you to do data science. By using the tools, you will be easily handling big data, manipulating it, and producingmeaningfuloutcomes.

In this course, we will examine and manage data structures in R. You will also learn atomic vectors, lists, arrays, matrices, data frames, Tibbles, and factors and you will master these. So, you will easily create, subset, convert or change any element within a vector or data frame.

Then, we will transform and manipulate real data. For the manipulation, we will use the tidyverse package, which involves dplyr and other necessary packages.

At the end of the course, you will be able to select columns, filter rows, arrange the order, create new variables, group by and summarize your data simultaneously.

In this course you will learn;

• Examining and Managing Data Structures in R

• Atomic vectors for r programming language

• Lists in r shiny

• Arrays in r statistics

• Matrices in data analytics

• Data frames in r language

• Tibbles in machine learning

• Factors in r programming

• Data Transformation in R in data science

• Transform and manipulate a deal data

• Tidyverse and more

• Python and R

• R programming, R

• Data Science with R

• Python R

• R

Why would you want to take this course?

Our answer is simple: The quality of teaching.

When you enroll, you will feel the OAK Academy's seasoned instructors' expertise.

Fresh Content

It’s no secret how technology is advancing at a rapid rate and it’s crucial to stay on top of the latest knowledge. With this course, you will always have a chance to follow the latest trends.

Video and Audio Production Quality

All our content is created/produced as high-quality video/audio to provide you the best learning experience.

You will be,

· Seeing clearly

· Hearing clearly

· Moving through the course without distractions

You'll also get:

Lifetime Access to The Course

Fast & Friendly Support in the Q&A section

Dive in now!

We offer full support, answering any questions.

See you in the R for Data Science: Learn Data Manipulation With R course!

Reed courses certificate of completion

Digital certificate - Included

• Getting Started R (Programming Language) 06:01

  • 1: What We Will Learn 03:01
  • 2: FAQ about R for Python Data Science 03:00

• Environment Installation for R 08:04

  • 3: Downloading and Installing R RStudio 03:27
  • 4: R Console Versus R Studio 04:37

• Data Management in R 35:00

  • 5: Getting Data into R 06:46
  • 6: Data Manipulation 08:47
  • 7: Graphs and Charts 18:27
  • 8: quiz 01:00

• Examining and Managing Data Structures in R 31:51

  • 9: Vector Basics 06:06
  • 10: Atomic Vector Types 03:51
  • 11: Converting Data Types of Atomic Vectors 04:04
  • 12: Test Functions 01:32
  • 13: Vector Recycling and Iterations 04:53
  • 14: Naming Vectors 04:31
  • 15: Subsetting Vectors 05:54
  • 16: quiz 01:00

• Lists in R Programming Language 05:54

  • 17: Lists 05:54

• Arrays in R Programming 13:35

  • 18: Arrays 04:37
  • 19: Subsections of an Array 08:58

• Matrices in r programming language 19:05

  • 20: Matrices 06:55
  • 21: Naming Matrix Row and Columns 05:34
  • 22: Calculating With Matrices 06:36

• Data Frames for R programming 36:33

  • 23: Introduction to Data Frames 07:20
  • 24: Naming Variables and Observations in DF 02:30
  • 25: Manipulating Values in DF 13:57
  • 26: Adding and Removing Variables 03:58
  • 27: Tibbles in R 08:48

• Factors in R (programming language) 16:41

  • 28: Introduction to Factors 04:32
  • 29: Manipulating Categorical Data with Forcats 12:09

• Data Transformation in R 1:06:59

  • 30: Introduction to Data Transformation 08:07
  • 31: Select Columns with Select Function 07:06
  • 32: Filtering Rows with Filter Function 16:22
  • 33: Arranging Rows with Arrange Function 11:37
  • 34: Adding New Variables with Mutate Function 06:51
  • 35: Grouped Summaries with Summarize Function 16:56

Welcome to R for Data Science: Learn Data Manipulation With R course.

Python and r, r, r programming, r and python, python, r python, data science with r, python r, data science, data science course, data science with r and python, r for data science, python programming, data science r, data literacy, r data science

R, Python data science with R programming, handle with data, manipulate data and outcomes with R (programming language)

Machine learning and data analysis are big businesses. The former shows up in new interactive and predictive smartphone technologies, while the latter is changing the way businesses reach customers. Learning R from a top-rated Oak Academy's instructor will give you a leg up in either industry.

R is the programming language of choice for statistical computing. Machine learning, data visualization, and data analysis projects increasingly rely on R for its built-in functionality and tools. And despite its steep learning curve, R pays to know.
Data science application is an in-demand skill in many industries worldwide — including finance, transportation, education, manufacturing, human resources, and banking. Explore data science courses with Python, statistics, machine learning, and more to grow your knowledge. Get data science training if you’re into research, statistics, and analytics.
In this course, you will learn how to code with R Programming Language, manage and analyze data with R programming and report your findings.

R programming language is a leading data mining technology. To learn data science, if you don’t know which high return programming language to start with. The answer is R programming.

Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. If you want to advance in your career as a data scientist, R is a great place to start your data science journey.

R is not just a programming language, but it is also an interactive environment for doing data science. Moreover, R is a much more flexible language than many of its peers.

Throughout the course, you will learn the most important tools in R that will allow you to do data science. By using the tools, you will be easily handling big data, manipulating it, and producingmeaningfuloutcomes.

In this course, we will examine and manage data structures in R. You will also learn atomic vectors, lists, arrays, matrices, data frames, Tibbles, and factors and you will master these. So, you will easily create, subset, convert or change any element within a vector or data frame.

Then, we will transform and manipulate real data. For the manipulation, we will use the tidyverse package, which involves dplyr and other necessary packages.

At the end of the course, you will be able to select columns, filter rows, arrange the order, create new variables, group by and summarize your data simultaneously.

In this course you will learn;

• Examining and Managing Data Structures in R

• Atomic vectors for r programming language

• Lists in r shiny

• Arrays in r statistics

• Matrices in data analytics

• Data frames in r language

• Tibbles in machine learning

• Factors in r programming

• Data Transformation in R in data science

• Transform and manipulate a deal data

• Tidyverse and more

• Python and R

• R programming, R

• Data Science with R

• Python R

• R

Why would you want to take this course?

Our answer is simple: The quality of teaching.

When you enroll, you will feel the OAK Academy's seasoned instructors' expertise.

What is R and why is it useful?

The R programming language was created specifically for statistical programming. Many find it useful for data handling, cleaning, analysis, and representation. R is also a popular language for data science projects. Much of the data used for data science can be messy and complex. The programming language has features and libraries available geared toward cleaning up unorganized data and making complex data structures easier to handle that can't be found in other languages. It also provides powerful data visualization tools to help data scientists find patterns in large sets of data and present the results in expressive reports. Machine learning is another area where the R language is useful. R gives developers an extensive selection of machine learning libraries that will help them find trends in data and predict future events.

What careers use R?

R is a popular programming language for data science, business intelligence, and financial analysis. Academic, scientific, and non-profit researchers use the R language to glean answers from data. R is also widely used in market research and advertising to analyze the results of marketing campaigns and user data. The language is used in quantitative analysis, where its data analysis capabilities give financial experts the tools they need to manage portfolios of stocks, bonds, and other assets. Data scientists use R in many industries to turn data into insights and predict future trends with its machine learning capabilities. Data analysts use R to extract data, analyze it, and turn it into reports that can help enterprises make better business decisions. Data visualization experts use R to turn data into visually appealing graphs and charts.

Is R difficult to learn?
Whether R is hard to learn depends on your experience. After all, R is a programming language designed for mathematicians, statisticians, and business analysts who may have no coding experience. For some beginning users, it is relatively simple to learn R. It can have a learning curve if you are a business analyst who is only familiar with graphical user interfaces since R is a text-based programming language. But compared to other programming languages, users usually find R easier to understand. R also may have an unfamiliar syntax for programmers who are used to other programming languages, but once they learn the syntax, the learning process becomes more straightforward. Beginners will also find that having some knowledge of mathematics, statistics, and probabilities makes learning R easier.

Fresh Content

It’s no secret how technology is advancing at a rapid rate and it’s crucial to stay on top of the latest knowledge. With this course, you will always have a chance to follow the latest trends.

Video and Audio Production Quality

All our content is created/produced as high-quality video/audio to provide you the best learning experience.

You will be,

· Seeing clearly

· Hearing clearly

· Moving through the course without distractions

You'll also get:

Lifetime Access to The Course

Fast & Friendly Support in the Q&A section

Dive in now!

We offer full support, answering any questions.

See you in the R for Data Science: Learn Data Manipulation With R course!

• Anyone interested in data sciences
• Anyone interested in statistical courses
• Statisticians, academic researchers, economists, analysts and business people
• Anyone who want to make inferences based on their financial data
• Professionals working in analytics or related fields
• Anyone who is particularly interested in big data, machine learning and data intelligence
• Specialists in various area who need to develop sophisticated graphical presentations of data
• Students who need R for their courses
• Anyone who plans a career in data scientist
• Anyone who wants to learn r shiny projects.
• Anyone who wants to learn r programming language
• Anyone who is particularly interested in big data, machine learning and data intelligence
• Anyone interested in data sciences
• Anyone eager to learn r statistics with no coding background
• People who want to learn data science with R, python R

• No Previous Knowledge about R (programming language) is needed!
• Desire to learn R in Data Science
• A Windows PC, Mac or Linux Computer
• Be able to download and install all the free software and tools needed to practice,
• A strong work ethic, willingness to learn and plenty of excitement about the data mining
• Nothing else! It’s just you, your computer and your ambition to get started today
• Curiosity for r programming
• Desire to work on r
• Desire to learn R programming with python, Data manipulation

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