Data Analytics with R Training

Data Analytics with R Training in Bangalore?

The Data Science with R training focuses on imparting in-depth knowledge of a range of techniques for data analytics by using R. The open source programming language R has increased in popularity in recent years, and is now universally accepted by statisticians and data miners as the number one language for data science. R uses cutting-edge technology to manipulate data and create statistical models and charts that can be used for predictive modeling. It gives sudden results and because it is open source, it is supported by the worldwide community of over 2 million users and developers. Trishana’s Data Analytics with R Courses will take you through the basics of this powerful language R in Bangalore.

What is Data Analytics?

Data science with R training in Bangalore

Data Analytics is the process of examining, cleaning and modelling data to obtain useful information that may suggest conclusions or support decision making. This is done with specialized systems and software. Data analytics is widely used in commercial business to obtain crucial information that will affect important decisions like where to invest and when to invest. It is also used by scientists and researchers to verify or disprove the scientific models and theories. With the increasing amount of data and the arrival of Big Data, Data analytics is becoming a Billion dollar industry with investments increasing many fold in last few years.

What is R?

R is an open source programming language used for statistical computing and data analysis. Though it was developed in 1995, only in the last few years it had gained momentum and used at many places. Source code for R is written on C, FORTRAN and R. Many of its standard functions are written in R itself. Only the computationally intensive tasks are written using C/C++ and FORTRAN. You can also write Java, DotNet or Python code to use R objects directly. R is highly extensible through the use of user-submitted packages for specific functions. R also has its own LaTeX-like documentation format called Rd which is used to supply ample documentation.

Why to enrol in Data Analytics with R course at Trishana Technologies, Bangalore?

We have the most comprehensive syllabus for Data analytics which was framed to prepare our students for both software field and research analysis, thus providing our students more choices to choose their career. We have expert working professionals as trainers who will teach you every concept in Data Analytics with practical examples. They will also help you to implement the concepts in R language. Our rigorous training sessions will help you master both R language and Data analytics. We will teach you everything related to Data Analytics including gathering Data requirements, Data collection, processing, integration and statistical analysis. And we will provide all the IDEs available for R to our students and prepare them to work in any environment. We will teach you all the useful user-packages in R language. We will also provide access to The R Journal, referred journal of the R Project.

Our Data Analytics with R course syllabus

Section 1

  • What is R?
  • Why R?
  • Installing R
  • R environment
  • How to get help in R
  • R console and Editor
  • Packages in R
  • CRAN
  • How to check package by date
  • Variables
  • Data Types
  • Data structure
  • Factors
  • Converting variable types
  • Missing values

Section 2
Importing and Exporting in R

  • Loading data from file(Text,Csv,Excel)
  • Loading data from clipboard
  • Connecting MySQL in R
  • How to remove lines while importing
  • Saving R data format
  • Exporting in R(Excel,Text)

Section 3 Data cleaning process

  • Concentrating strings
  • Find and replace
  • How to split string
  • Position based splitting
  • Semi matching condition
  • Condition based row/column selection
  • Renaming column names
  • Trim

Section 4
Data manipulation

  • Data sorting
  • Find and remove duplicates record
  • Recoding data
  • Merging data
  • Data aggregation
  • User defined functions
  • Local and global variables
  • Date and Time format in R
  • Table function

Section 5

  • For
  • If else
  • While
  • Break
  • Next
  • Return

Section 6
Visualization in R

  • Bar, stacked bar chart
  • Pie chart
  • Line chart
  • Scatter plot
  • Histogram
  • Column chart
  • Doughnut chart
  • Trending visualization charts in R

Section 7
Advanced concept

  • Social media analysis(Twitter) through API
  • Web apps in R

Section 8
Statistics and machine learning:

  • Standard deviation
  • Outlier
  • Linear regression
  • Multiple regression
  • Logistic regressions
  • Chi square
  • Anova
  • Clustering
  • Correlation
  • Decision tree
  • K-NN Algorithm

Career opportunities in Data Analytics with R

  • Business Analyst
  • Marketing Analytics Professional
  • Market Research Analyst
  • Software Engineer for Analytics
  • Predictive Analytics Engineer
  • Data Scientist with R language
  • Research Associate