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R Programming Training in Jaipur

  • R Programming

    Zeetron Networks delivers an in-depth best R Programming training in Jaipur. Students having R Programming certification are able to secure a quick job in an MNC. Participants need to enroll in the best R Programming training institute to attain the skills involved in technology.

    Our core strength is our trainers, who are expert professionals and veterans from the industry. Here our R Programming trainers have developed practical modules combining the syllabus (industry compliance). The biggest advantage of R- Programming language offers is that it doesn’t require any technical background; any non-technical person can easily learn R Programming.

  • Reasons To Choose Zeetron Networks?

    • Our R Programming training in Jaipur adheres to international industry standards.
    • We facilitate students with modern I.T infrastructure and learning environments during the R Programming training in Jaipur.
    • Trainers in R Programming training classes combine the self-developed practice session module with the current syllabus.
    • Being responsible, we provide students the R Programming course with placement assistance.
    • Our certificates are globally recognized provided after completion of course.
    • Our R Programming trainers are analysts, researchers, consultants and managers possessing a decade experience in coaching R Programming course
    • Hands-on Mode of Teaching as well as an opportunity to work on live projects.
    • 100% Placement Support for all Students
    • Proficient and Skilled Trainers from the Industry
    • Flexible Batch Timings
    • Small Batch Size for individual attention

What is R Programming?

R Programming is a powerful statistical programming language. You can evaluate large datasets in a shorter period with R programming. It is becoming the most sought after skill in the area of analytics for its open-source credibility.

The amazing packages present in R will assist in making out short data analysis. R can also be coordinated with other data management tools including Excel, Oracle, SQL Server, etc.

Any technical student or corporate person can join this training. This training is especially beneficial for-

COURSE DESCRIPTION

  • What is R?
  • Why R?
  • Installing R
  • R environment
  • How to get help in R
  • R console and Editor
  • Variables in R
  • Scalars
  • Vectors
  • Matrices
  • List
  • Data frames
  • Using c, Cbind, Rbind, attach and detach functions in R
  • Factors
  • Reading Tabular Data files
  • Reading CSV files
  • Importing data from excel
  • Importing data from SAS
  • Accessing database
  • Saving in R data
  • Loading R data objects
  • Writing to files
  • Selecting rows/observations
  • Selecting columns/fields
  • Merging data
  • Relabelling the column names
  • Converting variable types
  • Data sorting
  • Data aggregation
  • Commonly used Mathematical Functions
  • Commonly used Summary Functions
  • Commonly used String Functions
  • User-defined functions
  • local and global variable
  • While loop
  • If loop
  • For loop
  • Arithmetic operations
  • Box plot
  • Histogram
  • Pareto charts
  • Pie graph
  • Line chart
  • Scatterplot
  • Developing graphs

 

Python for Data Science

  • Why Python, its Unique Feature and where to use it?
  • Python Environment Setup/shell
  • Installing Anaconda
  • Understanding the Jupyter notebook
  • Python Identifiers, Keywords
  • Discussion about installed modules and packages
  • Python Data Types and Variable
  • Condition and Loops in Python
  • Decorators
  • Python Modules & Packages
  • Python Files and Directories manipulations
  • Use various files and directory functions for OS operations
  • Built-in modules (Library Functions)
  • Numeric and Math’s Module
  • String/List/Dictionaries/Tuple
  • Complex Data structures in Python
  • Python built-in function
  • Python user-defined functions
  • Array Operations
  • Arrays Functions
  • Array Mathematics
  • Array Manipulation
  • Array I/O
  • Importing Files with Numpy
  • Data Frames
  • I/O
  • Selection in DFs
  • Retrieving in DFs
  • Applying Functions
  • Reshaping the DFs - Pivot
  • Combining DFs
  • Merge
  • Join
  • Data Alignment
  • Matrices Operations
  • Create matrices
  • Inverse, Transpose, Trace, Norms, Rank etc
  • Matrices Decomposition
  • Eigenvalues & vectors
  • Basics of Plotting
  • Plots Generation
  • Customization
  • Store Plots

 

Machine Learning

  • Data Exploration
  • Missing Value handling
  • Outliers Handling
  • Feature Engineering
  • Importance of Feature Selection in Machine Learning
  • Filter Methods
  • Wrapper Methods
  • Embedded Methods
  • Introduction to Machine Learning
  • Logistic Regression
  • Naïve Bays Algorithm
  • K-Nearest Neighbor Algorithm
  • Decision Trees (SingleTree)
  • Support Vector Machines
  • Model Ensemble
    • - Bagging
    • - Random Forest
    • - Boosting
    • -Gradient Boosted Trees
  • Model Evaluation and performance
    • - K-Fold Cross-Validation
    • - ROC, AUC, etc...
  • Simple Linear Regression
  • Multiple Linear Regression
  • Decision Tree and Random Forest Regression
  • Similarity Measures
  • Cluster Analysis and Similarity Measures
  • Principal means Clustering
  • HierarComponents Analysis
  • Association Rules Mining & Market Basket Analysis
  • Basics
  • Term Document Matrix
  • TF-IDF
  • Twitter Sentiment Analysis