Data Analytics
Data Analytics Data analytics is the process of
examining, cleaning, transforming, and modeling data to extract useful
information and insights that can be used to make data-driven decisions. R is a
popular programming language and software environment for statistical computing
and graphics that is widely used in data analytics.
Ø The key concepts and techniques used in data analytics using R:
a.
Data Manipulation:
R provides several packages, such as dplyr and tidyr, for manipulating and
transforming data. These packages allow you to filter, sort, group, and reshape
data in various ways.
b.
Data Visualization:
R has several packages, such as ggplot2, for creating high-quality data
visualizations. These packages allow you to create a wide range of charts and
plots, including histograms, scatter
plots,
and box plots,
that help you to explore and understand your data.
c.
Statistical Analysis:
R provides a vast array of statistical functions and packages for performing
various types of analyses, such as regression, hypothesis testing, and ANOVA.
These functions allow you to identify patterns and relationships in your data
and to make data-driven decisions.
d.
Machine Learning:
R provides several packages, such as CARET and MLR, for implementing machine
learning algorithms. These algorithms allow you to create predictive models
that can be used to make accurate predictions based on your data.
e.
Reporting and Sharing:
R provides several packages, such as R Markdown and Shiny, for creating
interactive reports and dashboards. These tools allow you to share your results
and insights with others in a visually appealing and interactive way.
No comments:
Post a Comment