R Programming Training Course

R Programming Training Course

Courses Info

R programming training institute in Delhi ncr is a quality oriented enterprise in IT industry and software development. R programming training institute offers many administrations. R Programming is a powerful statistical programming dialect which is utilized for prescient modelling and other data mining related techniques. R programming can be utilized for data aggregation Creating charts and plots, data manipulation, statistical Modelling. R programming is turning into the most sought after ability in the field of investigation for its open source credibility.

There are numerous spectacular packages accessible in R that will help in a concise data analysis. There is a colossal lack in the market for experts with skills in R programming which makes it all the more fascinating to seek after. The R programming training institute in Delhi offered a wide assortment of Training Courses in corporate and Industrial Training. Since R is a free programming it is being generally utilized which makes a sort of chances for proficient who are hoping to seek after a profession in R Programming.

What we do at R programming training institute in Delhi ncr for R Programming?
Keeping in mind the end goal to become a successful expert in the field of analytics real time application ought to be examined in detail. Hands on Experience with the blend of statistical concept will be given by just specialists who are managing genuine situations in R programming consistently in their respective industry.

We are here to trained you in R Programming or R Analytics. We have professional experts working in MNCs and have more than 10 years experience in the analytics field.

R Analytics

Course Highlights :-

  • All the training would be provided by Industry Experts who already works on R Analytics
  • Backup Class in case you miss any session.
  • Theory + Practical Training along with case studies in order to get better understanding of concepts.
  • Complete course material with no extra cost.
  • Free doubt clearing session after completion of the training.
  • Resume building by experts.
  • Feedback form filled by candidates after every class in order to maintain highest level of quality standards.

Overview of the R language

  1. Defining the R project
  2. Obtaining R
  3. Using the R console
  4. A sample R session

Generating R code

  1. Basic programming concepts
  2. Scripts
  3. Text editors for R
  4. Graphical User Interfaces (GUIs) for R
  5. Packages

Objects and data structures

  1. Variable classes (factor, numeric, logical, complex, missing)
  2. Vectors and matrices
  3. Data frames and lists
  4. Data sets included in R packages
  5. Summarizing and exploring data

Dealing with data

  1. Reading data from external files
  2. Storing data to external files
  3. Creating and storing R workspaces
  4. Basic exploratory graphics

Manipulating objects

  1. Mathematical operations
  2. Basic matrix computation
  3. Textual operations
  4. Searches, strings, and pattern matching
  5. Regular sequences
  6. Random sequences
  7. Sampling from distributions

Graphics

  1. More slicing and extracting data
  2. Basic plots
  3. Adding overlaid lines, text, etc.
  4. Graphical parameters
  5. Data exploration

Summary graphics, Graphics, (advance), Basic graphical troubleshooting

– Brief introduction to regression graphics

– Generating data

Programming (basic)

– Functions

– Control structures

– Debugging

Hypothesis testing and data handling

– t-tests

– ANOVA

– Sorting/rearranging data structures

Linear & logistic regression

– General modeling syntax

– Extracting model results

– Confidence intervals

– Graphics for regression

– Tabular displays

– Extracting model results

– Confidence intervals

– Regression diagnostics

Graphics (intermediate)

– 3D graphics

– Graphics presentation

– Interactive graphics

Graphics (advanced)

– Animations

– High-density data displays

– Heatmaps

– Partitioning graphics

Functions & resampling methods for model validation

– Applying functions

– Writing your own functions

– Modifying existing functions

– Permutation testing

– Bootstrapping

– Cross-validation methods