R Programming:For Data Science With Real Exercises

Name: R Programming:For Data Science With Real Exercises
Rating: 3.9 (185 reviews)

Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2

Created byZulqarnain Hayat

Last updated 4/2020

English

What you'll learn

TO go through deep understanding of R language basics and packages and many hand on examples

Course content

6 sections • 27 lectures • 4h 53m total length

Introduction1:44
R in Context5:30

Creating bar charts for categorical variables20:28
Learn to create bar charts for categorical variables in R by building frequency tables from raw data, plotting with barplot, and customizing orientation and colors for clear visualization.
Creating histograms for quantitative variables8:42
Creating box plots for quantitative variables5:43
Create box plots for quantitative variables in R to inspect distributions, medians, and potential outliers. Use built-in functions with minimal preprocessing to customize labels and colors for clearer comparisons.
Calculating frequencies7:01
Calculating descriptives10:35
Explore descriptive statistics for quantitative variables using both built‑in functions and a specialized package, applied to the social_network_gutsiest dataset. Compute the five‑number summary (min, first quartile, median, third quartile, max), along with the mean and handling of missing values, and learn how to summarize all variables at once for quick data insight.

Calculating correlations10:02
Computing a regression10:02
Creating crosstabs for categorical variables10:36
Comparing means with the t-test3:52
Comparing means with an analysis of variance (ANOVA)8:22
Quick R Language Basics Commands22:56
Reading ,Accessing and Summarizing Data in R26:24
Read, access, and summarize data in R for data science, using real exercises from the course to reinforce practical data handling.
Quick Install R language on UBUNTU Lilnux18:09
Master quick installation of the R language on Ubuntu Linux and dive into data science with real exercises.

Requirements

Basic knowledge about any programming language like specifically graph,vectors,type of graph,median,means

Description

Learn R programming and unlock the power of statistical computing for data science, analytics, and visualization. This course introduces the R language, a popular open-source programming environment designed for statistical computing and graphics. Whether you are a beginner or an experienced data analyst, this course will help you gain practical skills to analyze, visualize, and present data effectively.

You’ll start by learning how to install R and RStudio on your computer and import data from SPSS, spreadsheets, and other sources. The course covers essential R commands and packages, enabling you to compute descriptive statistics, create new variables, and check the reliability of your data. You’ll also learn how to identify data outliers, test statistical assumptions, and perform key analyses, such as comparing means.

Hands-on examples guide you through data visualization, including bar charts for categorical variables, histograms, scatter plots, and more. You’ll also learn how to extract charts and tables from R and share results in presentations or web pages. For Linux users, the course includes guidance for quickly installing R on Ubuntu Linux.

By the end of this course, you’ll have the confidence to use R programming for real-world data analysis, create compelling visualizations, and make data-driven decisions. This course is ideal for data analysts, statisticians, students, and anyone interested in data science looking to leverage R’s powerful statistical and graphical capabilities.

Who this course is for:

Database Administrator, Database developer,Data scientist,Data Engineer

R Programming:For Data Science With Real Exercises

What you'll learn

Explore related topics

Course content

Introduction2 lectures • 7min

Getting Started6 lectures • 1hr 12min

Charts and Statistics for One Variable5 lectures • 52min

Modifying Data2 lectures • 23min

Charts for Associations4 lectures • 28min

Statistics for Associations8 lectures • 1hr 50min

Requirements

Description

Who this course is for: