
Master data science with r through five projects, hands-on case studies, and a rich ecosystem of packages. Learn why r powers statistical analysis, visualizations, and business and ai solutions.
Discover the history and use of the R language for data analytics, visualization, machine learning, and artificial intelligence, and learn to set up RStudio with cloud or standalone options.
Explore the RStudio interface, panels, and workflow, and learn to load and manage packages from CRAN, including installing, loading, updating, and using help functions.
Explore the basics of R datatypes, including numeric, character, and boolean, and learn to create and manipulate vectors and variables, with read line input, printing, and type casting.
Practice R datatypes with beginner exercises: add 25 and 74, store as numeric, then collect name, age, and city variables and print them in a formatted way.
Demonstrate constructing and modifying objects in R, exploring data types and logical operators, and mastering coercion and explicit conversions with as functions.
Explore strings in R programming by converting values to text with as.character, concatenating with paste, and manipulating substrings using sub, gsub, and length based methods to extract and replace text.
Practice sessions cover computing sum, mean, median, and sd for a numeric vector. They also cover min and max indices and creating a basic object in R.
Explore core R data structures—vectors, lists, matrices, arrays, and data frames—and learn how to concatenate with cat, understand data types, and apply indexing to access and modify values.
Learn to create and manipulate vectors in R with c, seq, and rep, access elements via indexing and logical conditions, and perform vector operations and sampling for reproducible results.
Explore vector indexing in R by comparing single and double brackets, access named and unnamed elements, handle NA with na.omit, and build conditional filters on height and weight for matrices.
learn to create and name matrices in r, build from data, shape rows and columns, convert between matrices and vectors, and perform transpose and matrix multiplication.
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 (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 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.