Certificate in R Programming

Course details

R is a statistical programming language that allows you to build probabilistic models, perform data science, and build machine learning algorithms. R has a great package ecosystem that
enables developers to conduct data visualization to data analysis.

Why should one take R Programming Certification?
This online course covers advanced-level concepts in R programming and demonstrates industry best practices. This is an advanced R course with an intensive focus on machine learning concepts in depth and applying them in the real world with ; 

The course starts off with pre-model-building activities such as univariate and bivariate analysis, outlier detection, and missing value treatment featuring the mice package. Then we take a look at linear, non-linear regression modeling and classification models, and will check out the math behind the working of classification ;

Who will benefit from taking this certification?
Job seekers looking for employment in various IT companies, PSUs or MNCs. Certification in R Programming will benefit data science professionals, data analyst and students who want to learn R programming.

By the end of the course, you will have a solid knowledge of machine learning and the R language itself. You’ll also solve numerous coding challenges throughout the course.
 
Benefits of Certification

• Government certification
• Certification valid for life
• Lifelong e-learning access
• Learning Hours: 18+ hrs

 
How It Works

1. Select Certification & Register
2. Receive Online e-Learning Access (LMS)
3. Take exam online anywhere, anytime
4. Get certified & Increase Employability

Test Details

• Duration: 60 minutes
• No. of questions: 50
• Maximum marks: 50, Passing marks: 25 (50%).
• There is NO negative marking in this module.
• Online exam.

 
TABLE OF CONTENT

R Language Basics

• Overview of R Programming Language
• Installing R
• R Interactive Command Line
• R Scripts
• Hello World
• R Data types
• Comments
• Constants and Variables
• Operators
• R Conditional Statements
• R Functions
• R Arrays
• R Strings
• R Lists
• Vectors in R
• R Matrices
• Factors in R
• Data Frames in R
• R Package and Repositories

Introduction to Statistics

• Terminologies
• Inferential Statistics
• Descriptive Statistics
• Frequency Distribution
• Cumulative Frequency Distribution
• Central Limit Theorem
• Valid Statistical Conclusions
• Graphical Methods
• Univariate Analysis
• Bivariate Analysis
• Regression

Introduction to Probability

• Concepts
• Permutation and Combination
• Probabilistic Distributions
• Continuous Variables PDFs
• Discrete Variables PDFs

Regression Modelling

• Regression Modeling Basics
• Correlation and Regression Coefficient
• Linear Regression
• Interpreting Regression Results
• Interaction Effects
• Residual Analysis
• Cook’s Distance
• Hypothesis Testing
• k-Fold Cross Validation
• GAM - Generalized Additive Models

Classification Models

• Naive Bayes Classifier
• k-Nearest Neighbors Classifier
• RPart, cTree, and
• caret Package
• Variable selection with RFE, varImp, and Boruta

Machine Learning

• Random Forest Classifier
• Gradient Boosting and GBM
• Regularization - Ridge, Lasso, and Elasticnet
• XGBoost

Unsupervised Learning

• Clustering
• k-means Clustering
• Principal Components Analysis
• Determining Optimum Number of Clusters
• Hierarchical Clustering
• Affinity Propagation
• Building Recommendation Engines

Time Series Analysis and Forecasting

• Stationarity, De-Trend, and De-Seasonalize
• Lags, ACF, PACF, and CCF
• Moving Average and Exponential Smoothing
• Double Exponential and Holt Winters
• ARIMA Modelling

Text Analytics

• Corpus, TDM, TF-IDF, and Word Cloud
• Cosine Similarity and Latent Semantic Analysis
• Extracting Topics with Latent Dirichlet Allocation
• Sentiment Scoring with tidytext and Syuzhet
• Classifying Texts with RTextTools

ggplot2

• Installation
• Usage
• ggplot df
• The Layers
• The Labels
• The Theme
• The Facets
• Plot multiple timeseries on same ggplot
• Bar charts
• Adjust X and Y axis limits
• Legend - Deleting and Changing Position
• Plot margin and background
• Annotation

Speeding Up R Code

• doParallel and foreach
• DPlyR
•  
• RCpp

Updated on 05 June, 2024