R Programming Ninja Course 2026:Data Science with 5 Projects
About this course
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 PackageData Structures (Vectors, 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
62/100
CourseAsk score
- What the provider tells you
- 38/45
- Who stands behind it
- 8/35
- How complete the listing is
- 16/20
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What you'll learn
- understanding the fundamentals of R programming
- manipulating strings using the Stringr package
- working with various data structures including vectors, matrices, and data frames
- applying data transformation techniques for analysis
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