Which online provider platform offers data science using the R online course?

Analytixlab has developed and provides data science using the R program.

How many hours will the data science using R training take to be completed by the students?

The course is scheduled for a total of 124 hours.

Does the provider platform Analytixlab offer student loan facilities for the students?

Yes, the candidates who can’t afford to pay the data science using the R program amount should apply for the student loan.

How many projects does the data science using R course training include?

The training includes ten capstone projects.

What are the prerequisites for data science using R certification?

Submission and evaluation of projects and assignments within the mentioned period will help the students receive the certificates.

Data Science Using R by Analytixlabs: Fee, Duration, How to Apply

Data Science Using R

Analytixlabs

Learn about the features of the R programming language and its significance in data science with the data science using R course.

Online

₹ 20000

Inclusive of GST

particular	details
Collaborators IBM	Medium of instructions English	Mode of learning Self study, Virtual Classroom	Mode of Delivery Video and Text Based	Learning efforts 8-10 Hours Per Week

particular

details

                                    Collaborators
                                    
                                        IBM

                                    Medium of instructions
                                    English

                                    Mode of learning
                                    Self study, Virtual Classroom

                                    Mode of Delivery
                                    Video and Text Based

                                    Learning efforts
                                    8-10 Hours Per Week
                                

Building Blocks

Introduction to Bridge Course & Analytics Software’s Basic Excel
Basic Programming Elements
Introduction to Basic Statistics
RDBMS & SQL (Basics)
Introduction to Analytics & Data Science
Introduction to Mathematical Foundations

R for data science

Data importing or exporting

Introduction R/R-Studio - GUI
Concept of Packages - Useful Packages (Base & Other packages)
Data Structure & Data Types (Vectors, Matrices, factors, Data frames, and Lists)
Importing Data from various sources
Exporting Data to various formats
Viewing Data (Viewing partial data and full data)
Variable & Value Labels – Date Values

Data manipulation

Creating New Variables (calculations & Binning)
Dummy variable creation
Applying transformations
Handling duplicates/missing's
Sorting and Filtering
Sub setting (Rows/Columns)
Appending (Row/column appending)
Merging/Joining (Left,right,inner,full,outer)
Data type conversions
Renaming
Formatting
Reshaping data
Sampling
Operators
Control Structures (if, if else)
Loops (Conditional, iterative loops)
apply functions
Arrays
R Built-in Functions
Text, Numeric, Date, utility
R User Defined Functions
Aggregation/Summarization

Data analysis

Introduction exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Uni-variate Analysis (Distribution of data)
Bivariate Analysis (Cross Tabs, Distributions & Relationships)

Using R with Databases

R and Relational Databases
Connecting to Relational Databases using RJDBC and RODBC
Database Design and Querying Data
Modifying Data and Using Stored Procedures
In-Database Analytics with R

Data Visualization with R

Basic Visualization Tools
- Bar Charts/Histograms/Pie Charts
- Scatter Plots
- Line Plots and Regression
Specialized Visualization Tools
- Word Clouds/ Radar Charts
- Waffle Charts/ Box Plots
How to create Maps
- Creating Maps in R
How to build interactive web pages
- Introduction to Shiny
- Creating and Customizing Shiny Apps
- Additional Shiny Features

Predictive Modeling in R

Dimensionality Reduction & Collaborative Filtering (e-learning)

Dimensionality Reduction: Feature Extraction & Selection
Collaborative Filtering & Its Challenges

Introduction to Statistics (e-learning)

Basic Statistics - Measures of Central Tendencies and Variance
Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem
Inferential Statistics -Sampling - Concept of Hypothesis Testing
Statistical Methods - Z/t-tests (One sample, independent, paired), Anova, Correlations and Chi-square

Linear Regression: Solving regression problems (e-learning)

Introduction - Applications
Assumptions of Linear Regression
Building Linear Regression Model
Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis, etc)
Assess the overall effectiveness of the model
Validation of Models (Re running Vs. Scoring)
Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
Interpretation of Results - Business Validation - Implementation on new data

Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning (e-learning)

Machine Learning Languages, Types, and Examples
Machine Learning vs Statistical Modelling
Supervised vs Unsupervised Learning
Supervised Learning Classification
Unsupervised Learning

Supervised Learning I (e-learning)

K-Nearest Neighbors
Decision Trees
Random Forests
Reliability of Random Forests
Advantages & Disadvantages of Decision Trees

Supervised Learning II (e-learning)

Regression Algorithms
Model Evaluation
Model Evaluation: Overfitting & Underfitting
Understanding Different Evaluation Models

Unsupervised Learning (e-learning)

K-Means Clustering plus Advantages & Disadvantages
Hierarchical Clustering plus Advantages & Disadvantages
Measuring the Distances Between Clusters - Single Linkage Clustering
Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering
Density-Based Clustering

Advance Analytics and Machine Learning in R (e-learning)

Important R packages for Machine Learning (caret, H2O, Randomforest, nnet, tm etc)

Fine tuning the models using Hyper parameters, grid search, piping etc.

Project - Consolidate Learnings

Applying different algorithms to solve the business problems and bench mark the results

Time Series Forecasting: Solving forecasting problems

Introduction - Applications
Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
Classification of Techniques(Pattern based - Pattern less)
Basic Techniques - Averages, Smoothening, etc
Advanced Techniques - AR Models, ARIMA, etc
Understanding Forecasting Accuracy - MAPE, MAD, MSE, etc

Machine Learning -Predictive Modeling – Basics

Introduction to Machine Learning & Predictive Modeling
Types of Business problems - Mapping of Techniques - Regression vs. classification vs. segmentation vs. Forecasting
Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
Overfitting (Bias-Variance Trade off) & Performance Metrics
Feature engineering & dimension reduction
Concept of optimization & cost function
Overview of gradient descent algorithm
Machine Learning -Predictive Modeling – Basics
Overview of Cross validation(Bootstrapping, K-Fold validation etc)
Model performance metrics (R-square, Adjusted R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )

Segmentation: Solving segmentation problems

Introduction to Segmentation & Role of ML
Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
Behavioral Segmentation Techniques (K-Means Cluster Analysis)
Cluster evaluation and profiling - Identify cluster characteristics
Interpretation of results - Implementation on new data

Unsupervised Learning: Segmentation

Concept of Distance and related math background
Expectation Maximization
Hierarchical Clustering
Spectral Clustering (DBSCAN)
Principal component Analysis (PCA)

Supervised Learning: Decision Trees

Decision Trees - Introduction - Applications
Types of Decision Tree Algorithms
Construction of Decision Trees through Simplified Examples; Choosing the "Best" attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
Decision Trees - Validation
Overfitting - Best Practices to avoid

Supervised Learning: Artificial Neural Networks (ANN)

Motivation for Neural Networks and Its Applications
Perceptron and Single Layer Neural Network, and Hand Calculations
Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
Neural Networks for Regression
Neural Networks for Classification
Interpretation of Outputs and Fine tune the models with hyper parameters
Validating ANN models

Supervised Learning: Ensemble Learning

Concept of Ensembling
Manual Ensembling Vs. Automated Ensembling
Methods of Ensembling (Stacking, Mixture of Experts)
Bagging (Logic, Practical Applications)
Random forest (Logic, Practical Applications)
Boosting (Logic, Practical Applications)
Ada Boost
Gradient Boosting Machines (GBM)
XGBoost

Supervised Learning: Support Vector Machines

Motivation for Support Vector Machine & Applications
Support Vector Regression
Support vector classifier (Linear & Non-Linear)
Supervised Learning: Support Vector Machines
Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
Interpretation of Outputs and Fine tune the models with hyper parameters
Validating SVM models

Supervised Learning: KNN

What is KNN & Applications?
KNN for missing treatment
KNN For solving regression problems
KNN for solving classification problems
Validating KNN model
Model fine tuning with hyper parameters

Supervised Learning: Naïve Bayes

Concept of Conditional Probability
Bayes Theorem and Its Applications
Naïve Bayes for classification
Applications of Naïve Bayes in Classifications

Text Mining & Analytics

Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
Text Mining & Analytics
Finding patterns in text: text mining, text as a graph
Natural Language processing (NLP)
Text Analytics – Sentiment Analysis using R
Text Analytics – Word cloud analysis using R
Text Analytics - Segmentation using K-Means/Hierarchical Clustering
Text Analytics - Classification (Spam/Not spam)
Applications of Social Media Analytics
Metrics(Measures Actions) in social media analytics
Examples & Actionable Insights using Social Media Analytics

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Data Science Using R

Online

₹ 20000

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The syllabus

Building Blocks

Building Blocks

R for data science

Data importing or exporting

Data manipulation

Data analysis

Using R with Databases

Data Visualization with R

Predictive Modeling in R

Dimensionality Reduction & Collaborative Filtering (e-learning)

Introduction to Statistics (e-learning)

Linear Regression: Solving regression problems (e-learning)

Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning (e-learning)

Supervised Learning I (e-learning)

Supervised Learning II (e-learning)

Unsupervised Learning (e-learning)

Advance Analytics and Machine Learning in R (e-learning)

Important R packages for Machine Learning (caret, H2O, Randomforest, nnet, tm etc)

Fine tuning the models using Hyper parameters, grid search, piping etc.

Project - Consolidate Learnings

Time Series Forecasting: Solving forecasting problems

Machine Learning -Predictive Modeling – Basics

Segmentation: Solving segmentation problems

Unsupervised Learning: Segmentation

Supervised Learning: Decision Trees

Supervised Learning: Artificial Neural Networks (ANN)

Supervised Learning: Ensemble Learning

Supervised Learning: Support Vector Machines

Supervised Learning: KNN

Supervised Learning: Naïve Bayes

Text Mining & Analytics

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Which online provider platform offers data science using the R online course?

How many hours will the data science using R training take to be completed by the students?

Does the provider platform Analytixlab offer student loan facilities for the students?

How many projects does the data science using R course training include?

What are the prerequisites for data science using R certification?

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