R Language Training Overview
R programming language is one the most powerful tool for computational statistics, visualization and data science. Data scientists and statisticians use R for solving many complex problems in their industry. R is extensively used in companies like Bing, Google, Facebook, Twitter and Uber. As R is used in various domains like Social media companies, Banks, Insurance companies, Car manufacturers, R is one of the most sought data analytics skill that is in high demand. R Programming is a powerful statistical the programming language which is used for predictive modeling and other data mining related techniques. R programming can be used for data manipulation, data aggregation, statistical Modelling, Creating charts and plots. R programming is becoming the most necessary skill in the field of analytics for its open source credibility.
Objectives of the Course
- Understand programming fundamentals of R language
- Understand various data import methods in R
- Understand the Data Manipulation in R
- Create visualizations and Plots using R
- Understand and Implement Linear Regression
- Perform Text Analysis
- Understand Machine Learning concepts
- Real-time implementation of R on a live project and provide Business Insights
Pre-requisites of the Course
- Programming background like C, C++, Python will be an added advantage but not mandatory to learn R, but introductory statistics is a prerequisite.
Who should do the course
- Software engineers and data analysts
- Business intelligence professionals
- SAS developers wanting to learn open source technology
- Those aspiring for a career in data science
- Professionals and Students looking to enter the Data Science industry
R Language Course Content
Essential to R programming
- An Introduction to R
- Introduction to the R language
- Programming statistical graphics
- Programming with R
- Simulation
- Computational linear algebra
- Numerical optimization
Data Manipulation Techniques using R programming
- Data in R
- Reading and Writing Data
- R and Databases
- Dates
- Factors
- Subscribing
- Character Manipulation
- Data Aggregation
- Reshaping Data
Statistical Applications using R programming
- Basics
- The R Environment
- Probability and distributions
- Descriptive statistics and graphics
- One- and two-sample tests
- Regression and correlation
- Analysis of variance and the Kruskal–Wallis test
- Tabular data
- Power and the computation of sample size
- Advanced data handling
- Multiple Regression
- Linear models
- Logistic regression
- Survival analysis
- Rates and Poisson regression
- Nonlinear curve fitting