Advance Data science certification using R

10000 Learners

Course Objective:

Prerequisite: No expertise is required; however basic knowledge High School Mathematics and Statistics will be required.

Course Content:


Introduction to Data Science
  • What is data science
  • Different fields of data science
  • The ven diagram for data science
  • Tools of data science
  • Introduction to R for data science
Introduction to R programming
  • Introduction to R and its features
  • Install R and RStudio
  • Different data types and functions in R
  • Control statements and loops in R
  • Vectors, lists, matrix, factors in R
  • R data frames
  • Data reshaping and merging
  • Reading data from external file (csv/excel)
R packages for Data manipulation
  • What is data exploration and data wrangling
  • R packages for data exploration
  • Installing and using the packges
  • Dplyr package and its functions
  • Chaining of different functions
  • Data.table package for fastest manipulation in data
Generating plots in R
  • Install ggplot2 visualization package
  • Concepts of geom() function
  • Different charts like, bar plot, histogram, pie chart etc
  • Box plot and its uses
  • Draw density plot and frequency polygon plot
  • Set axis labels and title of R chart
  • Draw plot with png in background
  • Save plots as an image in disc
Statistical inference
  • Why we need statistics
  • Terminology used in statistics
  • Measure of central tendency
  • Measure of spread
  • Correlation and covariance
  • Standard deviation
  • Standardization and normalization
  • Normal and binary distribution
  • Probability and types of probability
  • Hypothesis testing
  • ANOVA (Analysis of Variance)
Introduction to Machine learning
  • What is machine learning
  • Types of machine learning
  • Concepts of supervised and unsupervised learning
  • Predictive modeling using Linear Regression
  • Simple and multiple linear regression
  • Assumptions of linear regression
  • Accuracy test for the model
Classification Technique
  • What is classification and its uses
  • Logistic regression to solve classification problem
  • Maths behind logistic regression
  • Difference with linear regression
  • Accuracy test for classification
  • Confusion matrix and classification report
  • True positive, false positive rate
  • Decision tree and random forest for classification
  • Pruning of tress
  • Mathematics behind decision tree
  • Entropy and information gain
Advanced classification Technique
  • Ensemble method and their advantages
  • Random forest classifier
  • Implement random forest in R
  • What is naïve bayes
  • Implement naïve bayes in R
  • SVM (support Vector Machine)
  • Kernel tricks in SVM
  • Solving non linear problem using SVM
Unsupervised learning
  • Understanding unsupervised learning
  • Clustering and its uses
  • Different types of clustering
  • K means, canopy clustering and hierarchical clustering
  • Mathematics behind K means
  • K menas process flow
  • Implement K means in R
Dimensionality reduction (PCA)
  • What is dimensionality reduction
  • Feature extraction and feature elimination
  • Principal Component Analysis (PCA)
  • Apply PCA in R
  • Advantages of PCA using example
Text mining
  • Introduction to concepts of Text Mining
  • Text Mining use cases.
  • Understanding and manipulating text with ‘tm’ & ‘stringR’
  • Text Mining Algorithms, Quantification of Text
  • Term Frequency-Inverse Document Frequency (TF-IDF)
  • After TF-IDF.
Association Rule & Recommendation Engines
  • Introduction to association rule
  • Measure of association rule mining
  • Apriori algorithm and implement in R
  • Introduction to Recommendation Engine
  • User-based collaborative filtering & Item-Based Collaborative Filtering,
  • Implementing Recommendation Engine in R
  • User-Based and item-Based
  • Recommendation Use-cases.
Time Series Analysis
  • What is time series data
  • Components of time series
  • Visualize the data to identify Time Series Components
  • Implement ARIMA model for forecasting
  • Exponential smoothing models
  • Identifying different time series scenario based on which different Exponential Smoothing model can be applied
  • Implement respective ETS model for forecasting
Introduction to Artificial Intelligence
  • Introducing Artificial Intelligence and Deep Learning
  • What is an Artificial Neural Network, TensorFlow
  • Computational framework for building AI models
  • Fundamentals of building ANN using TensorFlow
  • Working with TensorFlow in R.
  • Bigmart sale analysis
  • House price prediction
  • Analysis of stock market
  • Youtube data analysis
  • Augmenting retail sales with Data Science
  • Prediction on Pokeman dataset
  • Analyzing pre-paid model of stock broking
  • Email spam filtering
  • Market basket analysis
  • recommendation for movie
  • Book recommender system
  • Customer churn analysis

Key Features

  • Gain skills and competencies required in Industry by Experts.
  • Work on Real-time Projects depending upon the course you select.
  • Students work in a professional corporate environment.
  • Get a globally recognized Certificate form WebTek with our partner logos.
  • Global Brand recognition for Placements.


  • Course Duration: 6 Months
  • Suitable For:2nd Yr / 3rd Yr / 4th Yr B.Tech. / Diploma / MCA / BCA students

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