R Programming 2021:Ninja Course With 5 Real World Projects!

Complete R Programming for Data Science, Data Analytics and Visualization

What you'll learn
R Programming
R Datatypes
R Data Structures
Vectors, Matrices, Arrays, Lists
Data analysis
Data Visualization using GGPLOT2
Case Studies on Data analysis using R
Projects on Data analysis using R
Data Cleaning
Data Transformations using tidyr, Dplyr
String Manipulations using Stringr
Handling Date and Time using Lubridate
Projects on Data Visualization using R

Description
Data Science and Analytics is a highly rewarding career that allows you to solve some of the world’s most interesting problems. The field of data science has exploded in the past two decades and shows no signs of stopping any time soon. Many big or small businesses and companies wish to make use of the insights gained through the big data.

Due to its open-source nature and its extreme versatility, R has become the primary tool for statistical analysis and data science. With the industry facing a shortage of data scientists all over the world, both novice and professional R programmers can enter. R community represents the cutting-edge in the field of data science.

This course is made to give you all the required knowledge at the beginning of your journey, so that you don’t have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips and trick you would require to start your career.

This course provides Full-fledged knowledge of R, we cover it all.

Our exotic journey will include the concepts of:

What’s and Why’s of R programming Language – Understanding the need for Statistics, difference between Population and Samples, various Sampling Techniques.

Core knowledge for DataTypes.

String Manipulation and handling using Stringr Package

Data Structures (Vactors, Matrices, Arrays, List)

Loops and Conditions and Functions for programming skills in R.

Dataframes explained in detail and perspective for Data Analysis Process and Concepts.

Most importantly Data Transformations have been covered to make you comfortable with how data should be handled and transformed for analysis.

Date Time Module helps to understand and handle date and time in R.

Descriptive Statistics allows to explore the data summaries for statistics.

Data Visualization using GGPLOT2 used for simple and complex visual analysis.

All the modules include practice questions and case studies to give you idea on the real world problems and enhancing problem solving skills.

5 Projects allow you to perform analysis on datasets with scope for further exploring and enhancing skills while building confidence.