R for Python Data Science: Learn Data Manipulation with R

Description

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.

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.

What is data science?

We have more data than ever before. But data alone cannot tell us much about the world around us. We need to interpret the information and discover hidden patterns. This is where data science comes in. Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction. Data science seeks to find patterns in data and use those patterns to predict future data. It draws on machine learning to process large amounts of data, discover patterns, and predict trends. Data science includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a science, it progresses by creating new algorithms to analyze data and validate current methods.

What does a data scientist do?

Data Scientists use machine learning to discover hidden patterns in large amounts of raw data to shed light on real problems. This requires several steps. First, they must identify a suitable problem. Next, they determine what data are needed to solve such a situation and figure out how to get the data. Once they obtain the data, they need to clean the data. The data may not be formatted correctly, it might have additional unnecessary data, it might be missing entries, or some data might be incorrect. Data Scientists must, therefore, make sure the data is clean before they analyze the data. To analyze the data, they use machine learning techniques to build models. Once they create a model, they test, refine, and finally put it into production.

What are the most popular coding languages for data science?

Python is the most popular programming language for data science. It is a universal language that has a lot of libraries available. It is also a good beginner language. R is also popular; however, it is more complex and designed for statistical analysis. It might be a good choice if you want to specialize in statistical analysis. You will want to know either Python or R and SQL. SQL is a query language designed for relational databases. Data scientists deal with large amounts of data, and they store a lot of that data in relational databases. Those are the three most-used programming languages. Other languages such as Java, C++, JavaScript, and Scala are also used, albeit less so. If you already have a background in those languages, you can explore the tools available in those languages. However, if you already know another programming language, you will likely be able to pick up Python very quickly.

How long does it take to become a data scientist?

This answer, of course, varies. The more time you devote to learning new skills, the faster you will learn. It will also depend on your starting place. If you already have a strong base in mathematics and statistics, you will have less to learn. If you have no background in statistics or advanced mathematics, you can still become a data scientist; it will just take a bit longer. Data science requires lifelong learning, so you will never really finish learning. A better question might be, "How can I gauge whether I know enough to become a data scientist?" Challenge yourself to complete data science projects using open data. The more you practice, the more you will learn, and the more confident you will become. Once you have several projects that you can point to as good examples of your skillset as a data scientist, you are ready to enter the field.

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.

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