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SodaPDF-converted-Learn Data Science with R Programming

Learn Data Science with R Programming
You would invariably find a lot of ways to learn R programming for data science
from the courses floating in the market. But what is it that makes this course
stand apart from the rest. I will give you certain points about this course and its
features which will help you decide.
Welcome to R programming. R is an open-source programming language used
for statistical computing and graphics supported by the R Foundation for
Statistical Computing. The R language is widely used among statisticians and
data miners for developing statistical software and data analysis. R and its
libraries are used for implementing statistical and graphical techniques,
including linear and nonlinear modeling, classical statistical tests, time-series
analysis, classification, clustering, and others. R is easily extensible through
functions and extensions, and the R community is noted for its active
contributions in terms of packages
Data Science with R certification course has been designed keeping in mind
about learners who have zero to some level of exposure to R. Any ideal session
in this course would dedicate a good amount of time understanding the
theoretical part after which we will be moving on to the application of
theoretical concepts by doing hands-on these statistical techniques. You would
be provided with a lot of data set to practice and implement statistical
techniques during the session and also to practice later on in the form of selfstudy which will help you in your journey to learn data science with R
The three main pillars to learn data science with R programming are
1. Application of mathematical and statistical concepts
2. Expressing them using a programming language or a tool/platform
3. Particular
When learners learn data science with R programming modules, they will
understand the number of focuses that have been put on various use cases,
some of the very famous applications/services which use R, and then we
gradually move to understand data science workflow using R theoretically. We
will help you understand the basic components of any data science model, right
from fetching your data from your database to building a model that is in a
deployable form.
What are the key deliverables
As you will progress in the Data Science with R certification program, you will
acquire the below skills
Introduction and implementation of Statistical techniques
Understanding the data with respect to a business problem
Data wrangling techniques
Data representation/visualization for insight generation
Understanding and building machine learning workflows
Understanding various model parameters and their role
Hyper tuning statistical models
Deploying statistical models
With respect to the above steps, you will also learn how to use data science
specific libraries in R e.g. Frequently used libraries in data cleaning like plyr,
dplyr, tidyr, stronger, etc; data plotting libraries like ggplot2, lattice; machine
learning-based modules for building various regression and classification based
algorithms like CART, randomForest, e1071, Rpart, etc. These will help learners
to learn data science with R programming.