Data Science With R: Tidyverse Assignment Help

Data Science skills remain among the most in-demand on the job market today. Many people only see the enjoyable aspects of data science, such as “searching for data insight,” “revealing the hidden truth behind the data,” “building predictive models,” “applying machine learning algorithms,” and so on. Most data scientists are aware that when dealing with real data, the most time-consuming operations of any data science project are “data importing,” “data cleaning,” “data wrangling,” “data exploring,” and so on. As a result, having an appropriate tool for addressing specific data-related tasks is required. What if I told you there is a freely available tool that fits the above-mentioned description?
When it comes to Data Science With R: Tidyverse, data science, and data exploration, R is one of the most popular programming languages. When you combine R with R’s collection of libraries known as tidyverse, you get one of the most lethal tools designed for data science-related tasks. Each tidyverse library has its own philosophy, grammar, and data types. As a result, libraries can be used concurrently, allowing you to write more efficient and optimised R code, allowing you to complete projects faster

KEY FEATURE

This Specialization is designed for data scientists who are already familiar with the R programming language and want to do data science with the Tidyverse family of packages

You will learn how to import, wrangle, visualize, and model data using the powerful Tidyverse framework over the course of five courses.

The Tidyverse packages offer a straightforward but powerful approach to data science that scales from simple analyses to massive data deployments.

Data science is working to automate transportation, such as creating a self-driving car, which is the transportation of the future. It can aid in various predictions.

Data Science With R: Tidyverse Assignment Help

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Key Topics

    Data Science With R: Tidyverse

    This course is divided into chapters, and each chapter introduces a different aspect of data-related tasks, along with the appropriate tidyverse tool to assist you with a given task. In addition, the course incorporates theory related to the topic as well as practical examples that are covered in R. If you take the course, you will be faced with a variety of data science challenges. Here are a few examples from the course:
    • Tidy data, how to clean your data with tidyverse?
    • Grammar of data wrangling.
    • How to wrangle data with dplyr and tidyr.
    • Create table-like objects called tibble.
    • Explore data and draw statistical plots using ggplot2.
    • Use concepts of functional programming, and map functions using purrr.
    • Efficiently deal with lists with the help of purrr.
    • Practical applications of relational data.
    • Use dplyr for relational data
    • Tidy evaluation inside tidyverse.
    • Apply tidyverse tools for the final practical data science project.
    • Import and parse data with readr and other libraries.
    • Deal with strings in R using stringr.
    • Apply Regular Expressions concepts when dealing with strings.
    • Deal with categorical variables using forcats.
    • Grammar of Data Visualization.

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    Things You Will Learn From Our Experts:

    • How to use R’s tidyverse libraries in your data science projects
    • How to write efficient R code for data science related tasks
    • What are Regular Expressions
    • How to use stringr library with Regular Expressions
    • How to use forcats library to manipulate categorical variables
    • What is Grammar of Graphics
    • How to visualize data with ggplot2 library
    • What is functional programing
    • What is clean data
    • How to clean your data with R
    • What is grammar of data wrangling
    • How to wrangle data with dplyr and tidyr
    • How to import data into R
    • How to properly parse imported data
    • How to chain R’s functions into a pipeline
    • How to manipulate strings
    • How to use purrr library for mapping functions, nesting data, manipulating lists, etc.
    • What is relational data
    • How to use dplyr library for relational data
    • What is tidy evaluation
    • How to use tidyverse tools to finish a practical project

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