SAS Training

R Training

With this comprehensive R training learn hand-on skills on Data Science with R - the golden boy of Data Science! Over past several years R has garnered immense popularity among Data Science practitioners and it is no surprise that R language is often as referred as lingua franca of Data Science! This Data Science R course effectively covers basic data analytics, statistical predictive modelling and machine learning through various practical examples and projects.


Best R training in Bangalore and Delhi NCR, for candidates who do not have programming background but want to acquire job oriented practical skills on a prominent open source Data Science platform. This Data Science R course is also available through live online and self-paced video based mode as well. 


You may also check for amazing value combo course Data Science Specialization to learn Data Science using R & R.


Data Science R course duration: 180 hours (At least 72 hours live training + Practice and Self-study, with ~8hrs of weekly self-study)

Who Should do this course?

Candidates from various quantitative backgrounds, like Engineering, Finance, Maths, Statistics, Business Management who want R training with detailed focus on Data Science and Machine Learning applications.

 

1. INTRODUCTION TO R
1.1. Why use R?
1.2. Obtaining and installing R
1.3. Working with R
1.4. Packages
1.5. Batch processing
1.6. Using output as input—reusing results

2. CREATING A DATASET
2.1. Understanding datasets
2.2. Data structures
2.3. Data input
2.4. Annotating datasets
2.5. Useful functions for working with data objects

3. GETTING STARTED WITH GRAPHS

3.1. Working with graphs
3.2. A simple example
3.3. Graphical parameters
3.4. Adding text, customized axes, and legends
3.5. Combining graphs

4. BASIC DATA MANAGEMENT

4.1. A working example
4.2. Creating new variables
4.3. Recoding variables
4.4. Renaming variables
4.5. Missing values
4.6. Date values
4.7. Type conversions
4.8. Sorting data
4.9. Merging datasets
4.10. Subsetting datasets
4.11. Using SQL statements to manipulate data frames

 

5. ADVANCED DATA MANAGEMENT

5.1. A data management challenge
5.2. Numerical and character functions
5.3. A solution for our data management challenge
5.4. Control flow
5.5. User-written functions
5.6. Aggregation and restructuring

6. BASIC GRAPHS
6.1. Bar plots
6.2. Pie charts
6.3. Histograms
6.4. Kernel density plots
6.5. Box plots
6.6. Dot plots

7. BASIC STATISTICS

7.1. Descriptive statistics
7.2. Frequency and contingency tables
7.3. Correlations
7.4. t-tests
7.5. Nonparametric tests of group differences
7.6. Visualizing group differences

8. REGRESSION
8.1. The many faces of regression
8.2. OLS regression
8.3. Regression diagnostics
8.4. Unusual observations
8.5. Corrective measures
8.6. Selecting the "best" regression model
8.7. Taking the analysis further

9. ANALYSIS OF VARIANCE
9.1. A crash course on terminology
9.2. Fitting ANOVA models
9.3. One-way ANOVA
9.4. One-way ANCOVA
9.5. Two-way factorial ANOVA
9.6. Repeated measures ANOVA
9.7. Multivariate analysis of variance (MANOVA)
9.8. ANOVA as regression

10. POWER ANALYSIS
10.1. A quick review of hypothesis testing
10.2. Implementing power analysis with the pwr package
10.3. Creating power analysis plots
10.4. Other packages

11. INTERMEDIATE GRAPHS
11.1. Scatter plots
11.2. Line charts
11.3. Correlograms
11.4. Mosaic plots

12. RESAMPLING STATISTICS AND BOOTSTRAPPING
12.1. Permutation tests
12.2. Permutation test with the coin package
12.3. Permutation tests with the lmPerm package
12.4. Additional comments on permutation tests
12.5. Bootstrapping
12.6. Bootstrapping with the boot package
12.7. Summary

13. GENERALIZED LINEAR MODELS
13.1. Generalized linear models and the glm() function
13.2. Logistic regression
13.3. Poisson regression

14. PRINCIPAL COMPONENTS AND FACTOR ANALYSIS

14.1. Principal components and factor analysis in R
14.2. Principal components
14.3. Exploratory factor analysis
14.4. Other latent variable models

15. ADVANCED METHODS FOR MISSING DATA

15.1. Steps in dealing with missing data
15.2. Identifying missing values
15.3. Exploring missing values patterns
15.4. Understanding the sources and impact of missing data
15.5. Rational approaches for dealing with incomplete data
15.6. Complete-case analysis (listwise deletion)
15.7. Multiple imputation
15.8. Other approaches to missing data

16. ADVANCED GRAPHICS
16.1. The four graphic systems in R
16.2. The lattice package
16.3. The ggplot2 package
16.4. Interactive graphs