R for Python Data Science: Learn Data Manipulation with R (Udemy.com)

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 producing meaningful outcomes.

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.