## Live Project Based Sas R-Programming training in Noida

4.9 out of 5 based on 8415 ratings.**KVCH** is the pioneer of education providing the **best Sas R-Programming training in Noida** as per the current industry requirement that enables candidates to land on their dream jobs in companies worldwide. KVCH Provides **best Sas R-Programming training course in Noida**. KVCH is a renowned training company providing the best training service and also being the **best Sas R-Programming training institute in Noida** rendering practical knowledge through training on projects and a dedicated placement assistance for all. The course curriculum for **Sas R-Programming training course** is designed to provide in-depth knowledge that covers all the modules for the training ranging from basic to advanced level. At KVCH **Sas R-Programming training in Noida** is supervised and managed by industrial experts having more than 10 years of experience in handling Sas R-Programming projects. KVCH training comprises of both classroom as well as practical sessions to deliver an ideal environment for students that will enable them to handle difficult and complex situation when they would step into the reality of IT sector.

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### Benefits

Accredited Curriculum

Learn from the Experts

Professional Certificate

Guranteed Career Growth

Placement Assistance

Earn a Valuable Certificate

- History of S and R
- Introduction to R
- The R environment
- What is Statistical R-Programming?
- Your first R session
- Starting and quitting R
- Recording your work
- Basic features of R
- Calculating with R
- Named storage
- Functions
- Exact or approximate?
- R is case-sensitive
- Listing the objects in the workspace
- Vectors
- Extracting elements from vectors
- Vector arithmetic
- Simple patterned vectors
- Missing values and other special values
- Character vectors
- Factors
- More on extracting elements from vectors
- Matrices and arrays
- Data frames
- Dates and times
- Built-in functions and online help
- Built-in examples
- Finding help when you don’t know the function name
- Built-in graphics functions
- Additional elementary built-in functions
- Logical vectors and relational operators
- Boolean algebra
- Logical operations in R
- Relational operators
- Data input and output
- Changing directories
- dump() and source()
- Redirecting R output
- Saving and retrieving image files
- Data frames and the read.table function
- High-level plots
- Bar charts and dot charts
- Pie charts
- Histograms
- Box plots
- Scatterplots
- QQ plots
- Choosing a high-level graphic
- Low-level graphics functions
- The plotting region and margins
- Adding to plots
- Setting graphical parameters
- Flow control
- The for() loop
- The if() statement
- The while() loop
- Newton’s method for root finding
- The repeat loop, and the break and next statements
- Managing complexity through functions
- What are functions?
- Scope of variables
- Miscellaneous programming tips
- Using fix()
- Documentation using
- Some general programming guidelines
- Top-down design
- Debugging and maintenance
- Recognizing that a bug exists
- Make the bug reproducible
- Identify the cause of the bug
- Fixing errors and testing
- The browser() and debug()functions
- Efficient programming
- Learn your tools
- Use efficient algorithms
- Measure the time your program takes
- Be willing to use different tools
- Optimize with care
- Monte Carlo simulation
- Generation of pseudorandom numbers
- Simulation of other random variables
- Bernoulli random variables
- Binomial random variables
- Poisson random variables
- Exponential random numbers
- Normal random variables
- Monte Carlo integration
- Advanced simulation methods
- Rejection sampling
- Importance sampling
- Vectors and matrices in R
- Constructing matrix objects
- Accessing matrix elements; row and column names
- Matrix properties
- Triangular matrices
- Matrix arithmetic
- Matrix multiplication and inversion
- Matrix inversion
- The LU decomposition
- Matrix inversion in R
- Solving linear systems
- Eigenvalues and eigenvectors
- Advanced topics
- The singular value decomposition of a matrix
- The Choleski decomposition of a positive definite matrix
- The QR decomposition of a matrix
- The condition number of a matrix
- Outer products
- Kronecker products
- apply()
- The golden section search method
- Newton–Raphson
- The Nelder–Mead simplex method
- Built-in functions
- Linear programming
- Solving linear programming problems in R
- Maximization and other kinds of constraints
- Special situations
- Unrestricted variables
- Integer programming
- Alternatives to lp()
- Quadratic programming
- Modes and Classes
- Data Storage in R
- Testing for Modes and Classes
- Structure of R Objects
- Conversion of Objects
- Missing Values
- Working with Missing Values
- Reading Vectors and Matrices
- Data Frames: read.table
- Comma- and Tab-Delimited Input Files
- Fixed-Width Input Files
- Extracting Data from R Objects
- Connections
- Reading Large Data Files
- Generating Data
- Sequences
- Random Numbers
- Permutations
- Random Permutations
- Enumerating All Permutations
- Working with Sequences
- Spreadsheets
- The RODBC Package on Windows
- The gdata Package (All Platforms)
- Saving and Loading R Data Objects
- Working with Binary Files
- Writing R Objects to Files in ASCII Format
- The write Function
- The write.table function
- o Reading Data from Other Programs
- A Brief Guide to SQL
- Navigation Commands
- Basics of SQL
- Aggregation
- Joining Two Databases
- Subqueries
- Modifying Database Records
- ODBC
- Using the RODBC Package
- The DBI Package
- Accessing a MySQL Database
- Performing Queries
- Normalized Tables
- Getting Data into MySQL
- More Complex Aggregations
- as Date
- The chron Package
- POSIX Classes
- Working with Dates
- Time Intervals
- Time Sequences
- Using Factors
- Numeric Factors
- Manipulating Factors
- Creating Factors from Continuous Variables
- Factors Based on Dates and Times
- Interactions
- Basics of Subscripting
- Numeric Subscripts
- Character Subscripts
- Logical Subscripts
- Subscripting Matrices and Arrays
- Specialized Functions for Matrices
- Lists
- Subscripting Data Frames
- Basics of Character Data
- Displaying and Concatenating Character
- Working with Parts of Character Values
- Regular Expressions in R
- Basics of Regular Expressions
- Breaking Apart Character Values
- Using Regular Expressions in R
- Substitutions and Tagging
- Table
- Road Map for Aggregation
- Mapping a Function to a Vector or List
- Mapping a function to a matrix or array
- Mapping a Function Based on Groups
- There shape Package
- Loops in R
- Modifying Data Frame Variables
- Recoding Variables
- The recode Function
- Reshaping Data Frames
- The reshape Package
- Combining Data Frames
- Under the Hood of merge
- An overgrown calculator
- Assignments
- Vectorized arithmetic
- Procedures
- Graphics
- R language essentials
- Functions and arguments
- Vectors
- Quoting and escape sequences
- Missing values
- Functions that create vectors
- Matrices and arrays
- Factors
- Lists
- Data frames
- Indexing
- Conditional selection
- Indexing of data frames
- Grouped data and data frames
- Implicit loops
- Sorting
- Session management
- The workspace
- Textual output
- 3 Scripting
- Getting help
- Packages
- Built-in data
- attach and detach
- subset, transform, and within
- The graphics subsystem
- Plot layout
- Building a plot from pieces
- Using par
- Combining plots
- R programming
- Flow control
- Classes and generic functions
- Data entry
- Reading from a text file
- Further details on read.table
- The data editor
- Interfacing to other programs
- Random sampling
- Probability calculations and combinatorics
- Discrete distributions
- Continuous distributions
- The built-in distributions in R
- Densities
- Cumulative distribution functions
- Quantiles
- Random numbers
- Summary statistics for a single group
- Graphical display of distributions
- Graphical Histograms
- Empirical cumulative distribution
- Q–Q plots
- Boxplots
- Summary statistics by groups
- Graphics for grouped data
- Graphics Histograms
- Parallel boxplots
- Stripcharts
- Tables
- Generating tables
- Marginal tables and relative frequency
- Graphical display of tables
- Barplots
- Dotcharts
- Piecharts
- One-sample t test
- Wilcoxon signed-rank test
- Two-sample t test
- Comparison of variances
- Two-sample Wilcoxon test
- The paired t test
- The matched-pairs Wilcoxon test
- Simple linear regression
- Residuals and fitted values
- Prediction and confidence bands
- Correlation
- Pearson correlation
- Spearman’s ρ
- Kendall’s τ
- One-way analysis of variance
- Pairwise comparisons and multiple testing
- Relaxing the variance assumption
- Graphical presentation
- Bartlett’s test
- Kruskal–Wallis test
- Two-way analysis of variance
- Graphics for repeated measurements
- The Friedman test
- The ANOVA table in regression analysis
- Single proportions
- Two independent proportions
- k proportions, test for trend
- r × c tables
- The principles of power calculations
- Power of one-sample and paired t tests
- Power of two-sample t test
- Approximate methods
- Power of comparisons of proportions
- Two-sample problems
- One-sample problems and paired tests
- Comparison of proportions
- Recoding variables
- The cut function
- Manipulating factor levels
- Working with dates
- Recoding multiple variables
- Conditional calculations
- Combining and restructuring data frames
- Appending frames
- Merging data frames
- Reshaping data frames
- Per-group and per-case procedures
- Time splitting
- Plotting multivariate data
- Model specification and output
- Model search
- Polynomial regression
- Regression through the origin
- Design matrices and dummy variables
- Linearity over groups
- Interactions
- Two-way ANOVA with replication
- Analysis of covariance
- Graphical description
- Comparison of regression lines
- Diagnostics
- Generalized linear models
- Logistic regression on tabular data
- The analysis of deviance table
- Connection to test for trend
- Likelihood profiling
- Presentation as odds-ratio estimates
- Logistic regression using raw data
- Prediction
- Model checking
- Essential concepts
- Survival objects
- Kaplan–Meier estimates
- The log-rank test
- The Cox proportional hazards model
- Basic ideas
- The Poisson distribution
- Survival analysis with constant hazard
- Fitting Poisson models
- Computing rates
- Models with piecewise constant intensities
- Basic usage
- Finding starting values
- Self-starting models
- Profiling
- Finer control of the fitting algorithm

### 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 :

### Subscripting :

### Character Manipulation :

### Data Aggregation :

### Reshaping Data :

### Statistical Applications using R programming

### Basics : First steps

### 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 :

### Top Reasons to Choose KVCH for Sas R-Programming Training in Noida

**Sas R-Programming training in Noida**is constructed as per the IT industry standard.- We Offer the best SAS R-Programming training and dedicated placement assistance in Noida with properly planned training modules and course content.
- Regular and Weekends classes for SAS R-Programming training in Noida is provided.
- One of the biggest team of Certified Expert Trainers with 5 to 15 years of Real Industry Experience.
- Mentors of SAS R-Programming training in Noida helps in major project training, minor project training, live project preparation, interview preparation and job placement support.
- Smart Labs with Real Latest Equipment’s.
- 24x7 Lab Facilities. Students are free to access the labs for unlimited number of hours as per their own preferred timings.
- Silent and Discussion Zone areas in Labs to enhance Self Study and Group Discussions.
- Free of Cost Personality Development sessions including Spoken English, Group Discussions, Mock Interviews, Presentation skills.
- Free of Cost Seminars for Personality Development & Personal Presentation.
- Varity of Study Material: Books, PDF’s, Video Lectures, Sample questions, Interview Questions (Technical and HR), and Projects.
- Hostel Facilities available at Rs. 5,500/month for SAS R-Programming Training in Noida students.
- Free Study Material, PDFs, Video Trainings, Sample Questions, Exam Preparation, Interview Questions, Lab Guides.
- Globally Recognized Course Completion Certificate.
- Extra Time Slots (E.T.S.) for Practical’s (Unlimited), Absolutely Free.
- The ability to retake the class at no-charge as often as desired.
- One-on-One attention by instructors.
- Helps students to take knowledge of complex technical concepts.
- Payment options: Cheque, Cash, Credit Card, Debit card, Net Banking.

### KVCH Trainer's Profile for SAS R-Programming Training in Noida

- Are experts in their field of domain and constantly upgrade themselves with new tools to impart the best training of a real working environment
- Have been carefully selected by our training partners and recognized over the years by various organizations for their field of work.
- Have years of experience in working in Big corporation and MNC’s like IBM, HCL Technologies, Sapient, Birla soft, TCS, Accenture etc.
- Certified Industry Professionals with more than 10+ years of experience in Itindustry.
- Connected with placement cells of various companies to help and support students for placement

#### KVCH'S SAS R-Programming Trainers are:

### Placement Assistance after SAS R-Programming Training in Noida

- KVCH is the world leader in rendering placement assistance to students with the help of a dedicate placement cell that supports and assists students during the time of placement.
- KVCH also provide the best Resume Building Service, by helping students to design their resume as per the latest industry trend.
- KVCH regularly organizes Personality Development sessions including Group Discussions, Mock Interviews, Presentation skills that help students in overall personality development and to present themselves confidently at the time of interview.
- KVCH has helped students to grab their dream jobs in companies like IBM , HCL , Wipro , TCS , Accenture ,etc.

#### KVCH'S Placement Assistance

### KVCH Course duration for SAS R-Programming Training in Noida

**Regular Classes**– 5 days a week (Morning, Day time & Evening)**Weekend Classes**(Saturday, Sunday & Holidays)**Fast-track Classes**