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Course Description

Data Science Course Training In Delhi

ExcelR offers Data Science course in Delhi, the most comprehensive Data Science course in the market, covering the complete Data Science lifecycle concepts from Data Collection, Data Extraction, Data Cleansing, Data Exploration, Data Transformation, Feature Engineering, Data Integration, Data Mining, building Prediction models, Data Visualization and deploying the solution to the customer. Skills and tools ranging from Statistical Analysis, Text Mining, Regression Modelling, Hypothesis Testing, Predictive Analytics, Machine Learning, Deep Learning, Neural Networks, Natural Language Processing, Predictive Modelling, R Studio, Tableau, Spark, Hadoop, programming languages like R programming, Python are covered extensively as part of this Data Science training. ExcelR is considered as the best Data Science training institute in Delhi which offers services from training to placement as part of the Data Science training program with over 400+ participants placed in various multinational companies including E&Y, Panasonic, Accenture, VMWare, Infosys, IBM, etc. ExcelR imparts the best Data Science training in Delhi and considered to be the best in the industry.

Tools and Topics covered

Data Analytics course Key Benefits

Course Curriculum

  • Recap of Demo
  • Introduction to Types of Analytics
  • Project life cycle
  • An introduction to our E learning platform

 

Description:Learn about the other moments of business decision as part of Statistical Analysis. Learn more about Visual data representation and graphical techniques. Learn about Python, R programming with respect to Data Science and Machine Learning. Understand how to work with different Python IDE and Python programming examples.

Topics

  • Data Types
  • Measure Of central tendency
  • Measures of Dispersion
  • Graphical Techniques
  • Skewness & Kurtosis
  • Box Plot
  • R
  • R Studio
  • Descriptive Stats in R
  • Python (Installation and basic commands) and Libraries
  • Jupyter note book
  • Set up Github
  • Descriptive Stats in Python
  • Pandas and Matplotlib / Seaborn

Topics

  • Random Variable
  • Probability
  • Probility Distribution
  • Normal Distribution
  • SND
  • Expected Value
  • Sampling Funnel
  • Sampling Variation
  • CLT
  • Confidence interval
  • Assignments Session-1 (1 hr)
  • Introduction to Hypothesis Testing
  • Hypothesis Testing with examples
    • 2 proportion test
    • 2 sample t test
  • Anova and Chisquare case studies
  • Visualization
  • Data Cleaning
  • Imputation Techniques
  • Scatter Plot
  • Correlation analysis
  • Transformations
  • Normalization and Standardization

Description: Learn about Linear Regression, components of Linear Regression viz regression line, Linear Regression calculator, Linear Regression equation. Get introduced to Linear Regression analysis, Multiple Linear Regression and Linear Regression examples.

Topics

Description: Learn about the Multiple Logistic Regression and understand the Regression Analysis, Probability measures and its interpretation. Know what is a confusion matrix and its elements. Get introduced to “Cut off value” estimation using ROC curve. Work with gain chart and lift chart.     

Topics

  • Multiple Logistic Regression
  • Confusion matrix
    • False Positive, False Negative
    • True Positive, True Negative
    • Sensitivity, Recall, Specificity, F1 score
  • Receiver operating characteristics curve (ROC curve)

Description: Learn deployment using Rshiny and streamlit in R and python

Topics

  • R shiny
  • Streamlit

Description:As part of Data Mining Unsupervised get introduced to various clustering algorithms, learn about Hierarchial clustering, K means clustering using clustering examples and know what clustering machine learning is all about.

Topics

  • Supervised vs Unsupervised learning
  • Data Mining Process
  • Hierarchical Clustering / Agglomerative Clustering
    • Measure of distance
      • Numeric - Euclidean, Manhattan, Mahalanobis
      • Categorical - Binary Euclidean, Simple Matching Coefficient, Jaquard’s Coefficient
      • Mixed - Gower’s General Dissimilarity Coefficient
    • Types of Linkages
      • Single Linkage / Nearest Neighbour
      • Complete Linkage / Farthest Neighbour
      • Average Linkage
      • Centroid Linkage
  • Visualization of clustering algorithm using Dendrogram

K-Means

Description:In this continuation lecture learn about K means Clustering, Clustering ratio and various clustering metrics. Get introduced to methods of making optimum clusters.

Topics

  • Non-Hierarchial
  • Measurement metrics of clustering - Within Sum of Squares, Between Sum of Squares, Total Sum of Squares
  • Choosing the ideal K value using Scree plot / Elbow Curve

DBSCAN

Description:Introduction to Density based clustering method

Topics

  • A geneal intuition for DBSCAN
  • Different parameters in DBSCAN
  • Metrics used to evaluate the performance of model
  • Pro's and Con's of DBSCAN

Description:Learn to apply data reduction in data mining using dimensionality reduction techniques. Gain knowledge about the advantages of dimensionality reduction using PCA and tSNE

Topics

  • PCA and tSNE
  • Why dimension reduction
  • Advantages of PCA
  • Calculation of PCA weights
  • 2D Visualization using Principal components
  • Basics of Matrix algebra

Description:Learn one of the most important topic Association rules in data mining. Understand how the Apriori algorithm works, and the association rule mining algorithm.

Topics

  • What is Market Basket / Affinity Analysis
  • Measure of association
  • Support
  • Confidence
  • Lift Ratio
  • Apriori Algorithm

Description:Learn how online recommendations are made. Get insights about online Recommender System, Content-Based Recommender Systems, Content-Based Filtering and various recommendation engine algorithms. Get to know about people to people collaborative filtering and Item to item collaborative filtering.

Topics

  • User-based collaborative filtering
  • Measure of distance / similarity between users
  • Driver for recommendation
  • Computation reduction techniques
  • Search based methods / Item to item collaborative filtering
  • Vulnerability of recommender systems
  • Workflow from data to deployment
  • Data nuances
  • Mindsets of modelling

Description:Decision Tree and is one of the most powerful classifier algorithms today. Under this tutorial learn the math behind decision tree algorithm with a case study

Topics

  • Elements of Classification Tree - Root node, Child Node, Leaf Node, etc.
  • Greedy algorithm
  • Measure of Entropy
  • Attribute selection using Information Gain
  • Implementation of Decision tree using C5.0 and Sklearn libraries

Description: Learn about how to handle categorical data using different methods

Topics

  • Encoding Methods
    • OHE
    • Label Encoders
    • Outlier detection-Isolation Fores
  • Predictive power Score

Description: It helps in reducing overfitting , training time and it improves accuracy

Topics

  • Recurcive Feature Elimination
  • PCA

Description:Here you are going to learn what are they ways to improve the models interms of accuracy and reducing overfitting ( Bias vs Variance )

Topics

  • Splitting data into train and test
  • Methods of cross validation
  • Accuracy methods

Description:Rather working on a single model we can work on a diverse set of models it can achieved by using Ensemble learning

Topics

  • Bagging
  • Boosting
  • Random Forest
  • XGBM
  • LGBM

Description:KNN and SVM: KNN algorithm is by far one of the easiest algorithms to learn and interpret. SVM is another most popular algorithm best part is it can be used for both classification and regression purpose, learn these two by using simple case studies

Topics

  • Deciding the K value
  • Building a KNN model by splitting the data
  • Understanding the various generalization and regulation techniques to avoid overfitting and underfitting
  • Kernel tricks
  • Lasso Regression
  • Ridge Regression

Description: Neural Networks: It is a supervised machine learning algorithm which mimics our human brain and it is foundation for Artificial Intelligence and Deep Learning. Here you learn the operation of neural networks using R and Python.

Topics

  • Artificial Neural Network
  • Biological Neuron vs Artificial Neuron
  • ANN structure
  • Activation function
  • Network Topology
  • Classification Hyperplanes
  • Best fit “boundary”
  • Gradient Descent
  • Stochastic Gradient Descent Intro
  • Back Propogation
  • Intoduction to concepts of CNN

Description: Text mining or Text data mining is one of the wide spectrum of tools for analyzing unstructured data. As a part of this course, learn about Text analytics, the various text mining techniques, its application, text mining algorithms and sentiment analysis.

Topics

  • Sources of data
  • Bag of words
  • Pre-processing, corpus Document-Term Matrix (DTM) and TDM
  • Word Clouds
  • Corpus level word clouds
    • Sentiment Analysis
    • Positive Word clouds
    • Negative word clouds
    • Unigram, Bigram, Trigram
  • Vector space Modelling
  • Word embedding
  • Document Similarity using Cosine similarity

Description: Learn how to extract data from Social Media, download user reviews from E-commerce and Travel websites. Generate various visualizations using the downloaded data.     

Topics

  • Extract Tweets from Twitter
  • Extract user reviews of the products from Amazon, Snapdeal and TripAdvisor

Description: Learn how to perform text analytics using Python and work with various libraries that aid in data extraction, text mining, sentiment analysis and  

Topics

  • Install Libraries from Shell
  • Extraction and text analytics in Python

Description: Natural language processing applications are in great demand now and various natural language processing projects are being taken up. As part of this tutorial, learn about Natural language and ‘Natural language understanding’.

Topics

  • Sentiment Extraction
  • Lexicons and Emotion Mining

Description: Under the Naive Bayes classifier tutorial, learn how the classification modeling is done using Bayesian classification, understand the same using Naive Bayes example. Learn about Naive Bayes through the example of text mining.

Topics

  • Probability – Recap    
  • Bayes Rule
  • Naive Bayes Classifier
  • Text Classification using Naive Bayes

Description: Forecasting or Time Series Analysis is an important component in analytics. Here, get to know the various forecasting methods, forecasting techniques and business forecasting techniques. Get introduced to the time series components and the various time series analysis using time series examples.

Topics

  • Introduction to time series data
  • Steps of forecasting
  • Components of time series data
  • Scatter plot and Time Plot
  • Lag Plot
  • ACF - Auto-Correlation Function / Correlogram
  • Visualization principles
  • Naive forecast methods
  • Errors in forecast and its metrics
  • Model Based approaches
    • Linear Model
    • Exponential Model
    • Quadratic Model
    • Additive Seasonality
    • Multiplicative Seasonality
  • Model-Based approaches
  • AR (Auto-Regressive) model for errors
  • Random walk
  • ARMA (Auto-Regressive Moving Average), Order p and q
  • ARIMA (Auto-Regressive Integrated Moving Average), Order p, d and q
  • Data-driven approach to forecasting
  • Smoothing techniques
    • Moving Average
    • Simple Exponential Smoothing
    • Holts / Double Exponential Smoothing
    • Winters / HoltWinters
  • De-seasoning and de-trending
  • Forecasting using Python and R
  • Concept with a business case
  • End to End project Description with deployment using R and Python

Assignments/Projects/Placement Support

  • Basic Statistics
    • Data types Identification and probability
    • Expected values, Measures of central tendencies
    • Skewness and Kurtosis & Boxplot
    • Practice Mean, Median, Varience, Standard Deviation and Graphical representations in R
    • Creating Python Objects
    • Practice Mean, Median, Varience, Standard Deviation and Graphical representations in Python
    • Confidence intervals and distributions
  • Hypothesis Testing
    • Buyer ratio
    • Customer Order Form
    • Cutlets
    • Pantaloons
    • Lab TAT
  • Linear regression
    • Prediction of weight based on Calories consumed
    • Delivery Time period Vs Sorting time
    • Employee Churn rate Vs Salary
    • Salary Prediction
  • R shiny and Flask
    • Practice R shiny and Python Flask for Linear Regression assignments
  • Multiple Linear Regression
    • 50 startups case study
    • Computer data Case study
    • Toyota Corolla
  • Logistic Regression
    • Term deposit case study
    • Elections results Case study
  • Multinomial Regression
    • Student Program Case study
  • Hierarchical Clustering
    • Crime data
    • Eastwest Airlines
  • K means Clustering
    • Insurance policy
    • Crime data
  • PCA
    • Dimension Reduction for Wine data
  • Network Analytics
    • Node Properties practice in R
  • Association Rules
    • Association Rules for Book store
    • Association Rules for Mobile store
    • Association Rules for Retail Transactions
  • Recommendation Engine
    • Recommend Jokes for subscribers
  • Text mining, Web Extraction
    • Extraction of tweets from twitter
    • Reviews from ecommerce websites
  • Text mining
    • Sentiment Analysis on extracted data
  • NLP
    • Emotion mining by extracting a speech or novel from web
  • Naive Bayes
    • Spam and Ham classifications
  • KNN Classifier
    • Types of Glass
    • Classification of Animals
  • Decision Tree and Random Forest
    • Fraud Check
    • Sales prediction of an Organization
  • XGB and GLM
    • Social Networks Ads
  • Lasso and Ridge Regression
    • Practice Lasso and Ridge with multiple Linear Assignments
  • ANN
    • Forest Fires case study
  • SVM
    • Classification of Alphabets
  • Survival analysis
    • Prediction of Patient survival probability
  • Forecasting model based
    • Airlines Forecasting
    • Forecasting of sales for a soft drinks case study
  • Forecasting
    • Forecasting of Bike shares
    • Forecasting of Solar power consumption
  • Industry : Aviation

    Predicting the flight delays

    • How to determine which flights would be delayed and by how long?

  • Industry : Manufacturing

    Predict impurity in ore

    • The main goal is to use this data to predict how much impurity is in the ore concentrate As this impurity is measured every hour if we can predict how much silica (impurity) is in the ore concentrate, we can help the engineers giving them early information to take actions

  • Industry : Oil and Gas

    Predicting the oil price

    • Oil production and prices data are for 1932-2014(2014 data are incomplete );gas production and prices are for 1955-2014 export and net export data are for 1986-2013

  • Industry : Automotive

    Electric Motor Temperature

    • Predict the temperature of rotor and stator of E-Motor

  • Industry : Daily Analysis of a product

    "Daily" Twitter Data Analysis for a Product

    • Sentiment Emotion mining of twitter data of new product

  • Industry : E commerce

    Natural Language Processing

    • Top 5 relevant answers to be retrived based on input question
  • Resume Preparation
  • Interview Support

Value added courses

  • Introduction to Big Data
  • Challenges in Big Data and Workarounds
  • Introduction to Hadoop and its Components
  • Hadoop components and Hands-on
  • Understand the MapReduce (Distributed Computation Framework) and its Drawback
  • Introduction to Spark
  • Spark Components
  • Spark MLlib and Hands-on (one ML model in spark)

Introduction to R Programming

  • Introduction to R
  • Data Types in R

How To Install R & R Studio

Data Structures in R

  • Variable in R
  • R-Overview
    • Vector
    • Matrix
    • Array
    • List
    • Data-Frame
  • Operators in R
    • arithmetic
    • Relational
    • Logical
    • Assignment
    • Miscellaneous
  • Conditiional Statement
    • Decision Making
      • IF Statement
      • IF-Else Statement
      • Nested IF-Else Statement
      • Switch Statement
    • Loops
      • While Loop
      • Repeat Loop
      • For Loop
    • Strings
    • Functions
      • User-defined Function
      • Calling a Function
      • Calling a Function without an Argument
      • Calling a Function with an Argument

Programming Statistical

  • Box Plots
  • Bar Charts
  • Histogram
  • Pareto Chart
  • Pie Chart
  • Line Chart
  • Scatterplot

How to Import Dataset in R

  • Read CSV Files
  • Read Excel Files
  • Read SAS Files
  • Read STATA Files
  • Read SPSS Files
  • Read JSON Files
  • Read Text Files

R-Packages

  • DpLyr
  • Hmisc or mise
  • Ggplot2
  • Caret
  • Data Table

How to Integrate R and SQL

How to Get Data From SQL to R

Introduction

  • Python Introduction - Programing Cycle of Python
  • Python IDE and Jupyter notebook

Variables

  • Variables
  • Data type

Code Practice Platform

  • create , insert , update and delete operation , Handling erros

Operators

  • Operator -Arthmatic ,comparison , Assignment ,Logical , Bitwise opeartor
  • Decision making - Loops

Loops

  • While loop, for loop and nested loop
  • Number type conversion - int(), long(). Float ()
  • Mathametical functions , Random function , Trigonometric function

Sting

  • Strings- Escape char, String special Operator , String formatting Operator
  • Build in string methods - center(), count()decode(), encode()

List

  • Python List - Accessing values in list, Delete list elements , Indexing slicing & Matrices
  • Built in Function - cmp(), len(), min(), max(), list comprehension

Tuples

  • Tuples - Accessing values in Tuples, Delete Tuples elements , Indexing slicing & Matrices
  • Built in tuples functions - cmp(), len ()

Dictionary

  • Dictionary - Accessing values from dictionary, Deleting and updating elements in Dict.
  • Properties of Dist. , Built in Dist functions & Methods, Dict comprehension
  • Date & time -Time Tuple , calendor module and time module

Function

  • Function - Define function , Calling function
  • pass by refernece as value , Function arguments , Anonymous functions , return statements
  • Scope of variables - local & global , Decorators and recursion
  • Map reduce and filter

Modules

  • Import statemnts , Locating modules - current directory , Pythonpath
  • Dir() function , global and location functions and reload () functions , Sys module and subprocess module
  • Packages in Python

Files

  • Files in Python- Reading keyboard input , input function
  • Opening and closing files . Syntax and list of modes
  • Files object attribute- open , close . Reading and writing files , file Position.
  • Renaming and deleting files
  • Pickle and Json

Directories

  • mkdir methid, chdir () method , getcwd method , rm dir

Exception Handling

  • Exception handling - List of exceptions - Try and exception
  • Try- finally clause and user defined exceptions

OOP

  • OOP concepts , class , objects , Inheritance
  • Overriding methods like _init_, Overloading operators , Data hiding

Regular Expressions

  • match function , search function , matching vs searching
  • Regular exp modifiers and patterns

SQLite and My SQL

  • Data base connectivity
  • Methods- MySQL , oracle , how to install MYSQL , DB connection
  • create , insert , update and delete operation , Handling erros

Framework

  • Introduction to Django framwork , overview , environment
  • Apps life cycle , creating views
  • Application, Rest API
  • Introduction to What is DataBase
  • Difference between SQL and NOSQL DB
  • How to Install MYSQL and Workbench
  • Connecting to DB
  • Creating to DB
  • What are the Languages inside SQL How to Create Tables inside DB and Inserting the Records
  • Select statement and using Queries for seeing your data
  • Joining 2 tables
  • Where clause usage
  • Indexes and views
  • Different operations in SQL
  • How to Connect to your applications from MYSQL includes R and Python

What is Data Visualization?

  • Why Visualization came into Picture?
  • Importance of Visualizing Data
  • Poor Visualizations Vs. Perfect Visualizations
  • Principles of Visualizations
  • Tufte’s Graphical Integrity Rule
  • Tufte’s Principles for Analytical Design
  • Visual Rhetoric
  • Goal of Data Visualization
 

Tableau – Data Visualization Tool

  • Introduction to Tableau
  • What is Tableau? Different Products and their functioning
  • Architecture Of Tableau
  • Pivot Tables
  • Split Tables
  • Hiding
  • Rename and Aliases
  • Data Interpretation
 

Tableau User Interface

  • Understanding about Data Types and Visual Cues
 

Basic Chart types

  • Text Tables, Highlight Tables, Heat Map
  • Pie Chart, Tree Chart
  • Bar Charts, Circle Charts
 

Intermediate Chart

  • Time Series Charts
  • Time Series Hands-On
  • Dual Lines
  • Dual Combination
 

Advanced Charts

  • Bullet Chart
  • Scatter Plot
  • Introduction to Correlation Analysis
  • Introduction to Regression Analysis
  • Trendlines
  • Histograms
  • Bin Sizes in Tableau
  • Box Plot
  • Pareto Chart
  • Donut Chart, Word Cloud
  • Forecasting ( Predictive Analysis)
 

Maps in Tableau

  • Types of Maps in Tableau
  • Polygon Maps
  • Connecting with WMS Server
  • Custom Geo coding
  • Data Layers
  • Radial & Lasso Selection
 

Adding Background Image

  • How to get Background Image and highlight the data on it
  • Creating Data Extracts
  • Filters and their working at different levels 
  • Usage of Filters on at Extract and Data Source level
  • Worksheet level filters
  • Context, Dimension Measures Filter
 

Data Connectivity in-depth understanding

  • Joins
  • Unions
  • Data Blending
  • Cross Database Joins
  • Sets
  • Groups
  • Parameters
 

Creating Calculated Fields

  • Logical Functions
  • Case-If Function
  • ZN Function
  • Else-If Function
  • Ad-Hoc Calculations
  • Quick Table Calculations
  • Level of Detail (LoD)
  • Fixed LoD
  • Include LoD
  • Exclude LoD
 

Responsive Tool Tips

  • Dashboards
  • Actions at Sheet level and Dashboard level
  • Story
 

Connecting Tableau with Tableau Server

  • Publishing our Workbooks in Tableau Server
  • Publishing dataset on to Tableau Server
  • Setting Permissions on Tableau Server
 

Connecting Tableau with R

  • What is R?
  • How to integrate Tableau with R?
  • Tableau Prep

Introduction to Neural Network & Deep Learning

Topics

  • Introduction
  • Deep Learning Importance [Strength & Limitation]
  • SP | MLP
  • Neural Network Overview
  • Neural Network Representation
  • Activation Function
  • Loss Function
  • Importance of Non-linear Activation Function
  • Gradient Descent for Neural Network

Parameter & Hyper parameter

Topics

  • Train, Test & Validation Set
  • Vanishing & Exploding Gradient
  • Dropout
  • Regularization
  • Optimization algorithm
  • Learning Rate
  • Tuning
  • Softmax

CNN

Topics

  • CNN
  • Deep Convolution Model
  • Detection Algorithm
  • Face Recognition

RNN

Topics

  • RNN
  • LSTM
  • Bi Directional LSTM

Introduction to ChatGPT and AI

  • What is ChatGPT?
  • The history of ChatGPT
  • Applications of ChatGPT
  • ChatGPT vs other chatbot platforms
  • Industries using ChatGPT
  • The benefits and limitations of ChatGPT
  • Future developments in ChatGPT technology
  • Ethical considerations related to ChatGPT and AI

Types of AI and Chatgpt architecture

  • Narrow AI
  • Strong AI
  • Superintelligence
  • Chatgpt architecture

ChatGPT Functionalities and Applications

  • How does ChatGPT work?
  • ChatGPT Functionalities
  • Drafting emails and professional communication
  • Automating content creation
  • Resume and Cover letter creation
  • Research and information gathering
  • Brainstorming ideas and creative problem solving
  • Best Practices for Using ChatGPT

ChatGPT Prompt Engineering

  • What is Prompt Engineering?
  • Types of Prompts
  • Crafting Effective Prompts
  • Using ChatGPT to generate prompt

 

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Global Presence

ExcelR is a training and consulting firm with its global headquarters in Houston, Texas, USA. Alongside to catering to the tailored needs of students, professionals, corporates and educational institutions across multiple locations, ExcelR opened its offices in multiple strategic locations such as Australia, Malaysia for the ASEAN market, Canada, UK, Romania taking into account the Eastern Europe and South Africa. In addition to these offices, ExcelR believes in building and nurturing future entrepreneurs through its Franchise verticals and hence has awarded in excess of 30 franchises across the globe. This ensures that our quality education and related services reach out to all corners of the world. Furthermore, this resonates with our global strategy of catering to the needs of bridging the gap between the industry and academia globally.

ExcelR's Global Presence

 

Data Science Course Training locations in Delhi : Asaf Ali Road [110006] , Bhikaji Cama Place [110029] , Chandni Chowk Old Delhi [110006] ,Chattarpur [110074 ], Chirag Enclave [110048] , Civil Lines [110054] , Connaught Place [110001] , Defence Colony [110024] , Diplomatic Enclave [110021] , Dwarka [110075] , East of Kailash [110065] , Friends Colony [110025] , Golf Links [110003] , Greater Kailash [110048] , Green Park [110016] , Karol Bagh [110005] , Lajpat Nagar [110024], Mayur Vihar [110091] , Mehrauli Gurgaon Road [110037] , Naraina [110028] , Narela [110040] , Nehru Place [110019] , Nizammuddin [110013] , Paharganj [110055] , Panchshil Enclave [110017] , Paschim Vihar [110063] , Patel Nagar [110008] , Pushpanjali Farms [110037] , Qutabgarh Village [110039] , Rajendra Place [110008] , Rajokri [110038] , Saket [110017] , Samalkha [110037] , Shahdara [110032] , Shalimar Bagh [110088] , Shiv Murti [110037] , Sukhdev Vihar [110025] , Sundar Nagar [110013],Vasant Kunj [110070] , Vasant Vihar [110057] , Janakapuri [110058] , Noida [201301] , Ajmeri Gate [110002] , Aram Bagh [110055] , Sidipura [110005] , Azadpur [110052] , Karol Bagh 110005, Old rajinder nagar 110060, Rajouri garden 110027, Vishwa vidyalaya 110009, Kamla nagar 110007, Kalu sarai 110016, Nehru park 190001, (N.S.P) Netaji Subhash Place 110034, J N U 110067,Connaught Place 110001, Saket 110017, Pitampura 110034, East kidwai nagar 110023 , Shakur pur [110034].

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