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Data Science Course in Vadodara

5 (591 Ratings)

This Data Science course in Vadodara by CCE IIT Madras and Intellipaat helps you master Data Scientist skills like Machine Learning, Tableau, , Statistics, etc. Learn from IIT Madras faculty and industry experts and get certified by CCE IIT Madras.

Ranked #1 Data Science Program by India TV

Key Highlights

50+ Live sessions across seven months
218 Hrs Self-paced Videos
200 Hrs Project & Exercises
Learn from IIT Madras Faculty & Industry Practitioners
1:1 with Industry Mentors
3 Guaranteed Interviews by Intellipaat
24*7 Support
No Cost EMI Option

Data Science Course Overview

What will our experts teach you in this Data Science training?

In this course, our experts will teach you the following concepts:

  • Deploying Machine Learning models on Clouds ( MLOps).
  • Data analysis, project life cycle, and Data Science in the real world.
  • Python with Data Science.
  • Data Science projects, analytics, and recommender systems.
  • Natural Language Processing and its applications.
  • Git, Storytelling.
  • Techniques of evaluation, experimentation, and project deployment.
  • Data visualization with Tableau.
  • Machine Learning algorithms.
  • Microsoft Excel for data analysis and data transformation.
  • Data Science at scale with PySpark, AI with TensorFlow.
  • Analysis segmentation using clustering and the technique of prediction.

Our best Data Science training is helpful for the following professionals and aspirants:

  • Big Data professionals
  • Business Analysts
  • Business Intelligence professionals
  • UG and PG graduates who wish to pursue a career in this domain

There are no eligibility criteria needed to enroll in this Data Science training.

  • The average salary of a Data Scientist in Vadodara is ₹300,000 per annum – PayScale
  • The openings for professionals with Data Science skills will reach nearly 700,000 by 2022 – Forbes
  • There are just over 100 job listings for Data Science professionals in Gujarat – LinkedIn

Here are the roles and responsibilities of Data Scientists, Data Analysts, and Business Analysts in their respective teams:

  • Data Scientists perform statistical analysis and make decisions based on the given data.
  • The role of Data Analysts in a firm is to analyze the business needs and perform the entire life cycle of data analysis. 
  • Business Analysts come up with detailed business analysis, outlining the business problems and their solutions.
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We are happy to help you 24/7

With data collection, ’the sooner, the better’ is the best answer - The CEO of Yahoo
Everything is going to be connected with data and mediated by software - The CEO of Microsoft
The world is now awash in data, and we can see consumers more cleanly - The Co-founder of PayPal

Career Transition

55% Average Salary Hike

$1,20,000 The Highest Salary

12000+ Career Transitions

500+ Hiring Partners

Career Transition Handbook

Meet Your Mentors

Who can apply for the Data Scientist course?

  • Information Architects and Statisticians
  • Developers looking to master Machine Learning and Predictive Analytics
  • Big Data, Business Analysis, Business Intelligence, and Software Engineering Professionals
  • Aspirants who are looking to work as Machine Learning Experts, Data Scientists, etc.
Who can aaply

What roles does a Data Scientist play?

Data Scientist

Design and implement scalable codes alongside effectively developing high-quality applications.

Analytics and Insights Analyst

Develop solutions for fixing quality issues in the data upon investigating the reported errors in the data.

AI & ML Engineer

Use Lambda functions and API Gateway to integrate Machine Learning models to web apps and deploy models in SageMaker.

Data Engineer & Data Analyst

Perform data cleansing, data transformation, analyze the outcomes and present the insights in reports and dashboards.

Junior Data Scientist

Analyze the operating behavior using advanced statistical techniques and tools. Also, create algorithms with prescriptive and descriptive methods.

Applied Scientist

Derive intelligence for the business products through designing and developing Machine Learning models.

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Skills Covered

Python

Data Science

Data Analysis

AI

GIT

MLOps

Data Wrangling

SQL

Story Telling

Machine Learning

Prediction algorithms

NLP

PySpark

Model

Data visualization

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Tools Covered

tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop
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Course Fees

Online Classroom Preferred

  • Live Classes from IIT Madras Faculty
  • Certification from CCE, IIT Madras
  • Job Assistance ( Mock Interviews, Resume Preparation)
  • Guaranteed 3 Interviews by Intellipaat
  • Dedicated Learning Manager
01 Oct

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

08 Oct

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

15 Oct

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

22 Oct

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

$1,492 10% OFF Expires in

Corporate Training

  • Customized Learning
  • Enterprise grade learning management system (LMS)
  • 24x7 Support
  • Enterprise grade reporting

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Data Science Course Curriculum

Live Course Self Paced

Module 1 – Preparatory Session - Linux and Python

Preview

Python 

  • Introduction to Python and IDEs – The basics of the python programming language, how you can use various IDEs for python development like Jupyter, Pycharm, etc. 
  • Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling ,etc.
  • Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
  • Hands-on Sessions And Assignments for Practice – The culmination of all the above concepts with real-world problem statements for better understanding. 

Linux

  • Introduction to Linux  – Establishing the fundamental knowledge of how linux works and how you can begin with Linux OS. 
  • Linux Basics – File Handling, data extraction, etc.
  • Hands-on Sessions And Assignments for Practice – Strategically curated problem statements for you to start with Linux. 

Module 2 – Data Analysis With MS-Excel

Preview

Excel Fundamentals 

  • Reading the Data, Referencing in formulas , Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering 
  • Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security 
  • VBA Macros, Ranges and Worksheet in VBA 
  • IF conditions, loops, Debugging, etc.

Excel For Data Analytics 

  • Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.  

Data Visualization with Excel

  • Charts, Pie charts, Scatter and bubble charts
  • Bar charts, Column charts, Line charts, Maps
  • Multiples: A set of charts with the same axes, Matrices, Cards, Tiles

Excel Power Tools 

  • Power Pivot, Power Query and Power View

Classification Problems using Excel

  • Binary Classification Problems, Confusion Matrix, AUC and ROC curve 
  • Multiple Classification Problems  

Information Measure in Excel

  • Probability, Entropy, Dependence 
  • Mutual Information 

Regression Problems Using Excel

  • Standardization, Normalization, Probability Distributions 
  • Inferential Statistics, Hypothesis Testing, ANOVA, Covariance, Correlation
  • Linear Regression, Logistic Regression, Error in regression, Information Gain using Regression

SQL Basics – 

  • Fundamentals of Structured Query Language
  • SQL Tables, Joins, Variables 

Advanced SQL –  

  • SQL Functions, Subqueries, Rules, Views
  • Nested Queries, string functions, pattern matching
  • Mathematical functions, Date-time functions, etc. 

Deep Dive into User Defined Functions

  • Types of UDFs, Inline table value, multi-statement table. 
  • Stored procedures, rank function, triggers, etc. 

SQL Optimization and Performance

  • Record grouping, searching, sorting, etc. 
  • Clustered indexes, common table expressions.

Version Control 

  • What is version control, types, SVN.

GIT 

  • Git Lifecycle, Common Git commands, Working with branches in Git
  • Github collaboration (pull request), Github Authentication (ssh and Http)
  • Merging branches, Resolving merge conflicts, Git workflow

Descriptive Statistics – 

  • Measure of central tendency, measure of spread, five points summary, etc. 

Probability 

  • Probability Distributions, bayes theorem, central limit theorem. 

Inferential Statistics –  

  • Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

Extract Transform Load

  • Web Scraping, Interacting with APIs 

Data Handling with NumPy

  • NumPy Arrays, CRUD Operations,etc. 
  • Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices. 

Data Manipulation Using Pandas

  • Loading the data, dataframes, series, CRUD operations, splitting the data, etc. 

Data Preprocessing

  • Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc. 
  • Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross validation techniques, train-test split, etc. 

Data Visualization

  • Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc, with Python matplotlib.
  • Regression plots, categorical plots, area plots, etc, with Python seaborn

Introduction to Machine learning 

  • Supervised, Unsupervised learning.
  • Introduction to scikit-learn, Keras, etc. 

Regression 

  • Introduction classification problems, Identification of a regression problem, dependent and independent variables. 
  • How to train the model in a regression problem. 
  • How to evaluate the model for a regression problem. 
  • How to optimize the efficiency of the regression model. 

Classification 

  • Introduction to classification problems, Identification of a classification problem, dependent and independent variables. 
  • How to train the model in a classification problem. 
  • How to evaluate the model for a classification problem. 
  • How to optimize the efficiency of the classification model. 

Clustering 

  • Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables. 
  • How to train the model in a clustering problem. 
  • How to evaluate the model for a clustering problem. 
  • How to optimize the efficiency of the clustering model.

Supervised Learning 

  • Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc. 
  • Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc. 
  • Decision Tree – Creating decision tree models on classification problems in a tree like format with optimal solutions.   
  • Random Forest – Creating random forest models for classification problems in a supervised learning approach. 
  • Support Vector Machine – SVM or support vector machines for regression and classification problems. 
  • Gradient Descent – Gradient descent algorithm that is an iterative optimization approach to finding local minimum and maximum of a given function. 
  • K-Nearest Neighbors – A simple algorithm that can be used for classification problems. 
  • Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting. 

Unsupervised Learning 

  • K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach. 
  • Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation. 
  • Linear Discriminant Analysis –  LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data. 
  • Principal Component Analysis – PCA follows the same approach in handling the multidimensional data.

Performance Metrics

  • Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc. 
  • Confusion matrix – To evaluate the true positive/negative, false positive/negative outcomes in the model. 
  • r2, adjusted r2, mean squared error, etc.

Artificial Intelligence Basics 

  • Introduction to keras API and tensorflow

Neural Networks

  • Neural networks
  • Multi-layered Neural Networks
  • Artificial Neural Networks 

Deep Learning 

  • Deep neural networks
  • Convolutional Neural Networks 
  • Recurrent Neural Networks
  • GPU in deep learning
  • Autoencoders, restricted boltzmann machine 

Text Mining, Cleaning, and Pre-processing

  • Various Tokenizers, Tokenization, Frequency Distribution, Stemming, POS Tagging, Lemmatization, Bigrams, Trigrams & Ngrams, Lemmatization, Entity Recognition.

Text classification, NLTK, sentiment analysis, etc  

  • Overview of Machine Learning, Words, Term Frequency, Countvectorizer, Inverse Document Frequency, Text conversion, Confusion Matrix, Naive Bayes Classifier.

Sentence Structure, Sequence Tagging, Sequence Tasks, and Language Modeling

  • Language Modeling, Sequence Tagging, Sequence Tasks, Predicting Sequence of Tags, Syntax Trees, Context-Free Grammars, Chunking, Automatic Paraphrasing of Texts, Chinking.

AI Chatbots and Recommendations Engine 

  • Using the NLP concepts, build a recommendation engine and an AI chatbot assistant using AI. 

RBM and DBNs & Variational AutoEncoder

  • Introduction rbm and autoencoders
  • Deploying rbm for deep neural networks, using rbm for collaborative filtering
  • Autoencoders features and applications of autoencoders.

Object Detection using Convolutional Neural Net

  • Constructing a convolutional neural network using TensorFlow
  • Convolutional, dense, and pooling layers of CNNs
  • Filtering images based on user queries

Generating images with Neural Style and Working with Deep Generative Models

  • Automated conversation bots leveraging
  • Generative model, and the sequence to sequence model (lstm).

Distributed & Parallel Computing for Deep Learning Models

  • Parallel Training, Distributed vs Parallel Computing
  • Distributed computing in Tensorflow, Introduction to tf.distribute
  • Distributed training across multiple CPUs, Distributed Training
  • Distributed training across multiple GPUs, Federated Learning
  • Parallel computing in Tensorflow

Reinforcement Learning

  • Mapping the human mind with deep neural networks (dnns)
  • Several building blocks of artificial neural networks (anns)
  • The architecture of dnn and its building blocks
  • Reinforcement learning in dnn concepts, various parameters, layers, and optimization algorithms in dnn, and activation functions.

Deploying Deep Learning Models and Beyond

  • Understanding model Persistence, Saving and Serializing Models in Keras, Restoring and loading saved models
  • Introduction to Tensorflow Serving, Tensorflow Serving Rest, Deploying deep learning models with Docker & Kubernetes, Tensorflow Serving Docker, Tensorflow Deployment Flask.
  • Deploying deep learning models in Serverless Environments
  • Deploying Model to Sage Maker
  • Explain Tensorflow Lite Train and deploy a CNN model with TensorFlow

Introduction to MLOps 

  • MLOps lifecycle
  • MLOps pipeline 
  • MLOps Components, Processes, etc

Deploying Machine Learning Models 

  • Introduction to Azure Machine Learning 
  • Deploying Machine Learning Models using Azure

Power BI Basics

  • Introduction to PowerBI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , Data Extraction with Power BI. 
  • SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
  • Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI.

DAX 

  • Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

Data Visualization with Analytics  

  • Slicers, filters, Drill Down Reports
  • Power BI Query, Q & A and Data Insights
  • Power BI Settings, Administration and Direct Connectivity 
  • Embedded Power BI API and Power BI Mobile 
  • Power BI Advance and Power BI Premium

The Data Science capstone project focuses on establishing a strong hold of analyzing a problem and coming up with solutions based on insights from the data analysis perspective. The capstone project will help you master the following verticals: 

  • Extracting, loading and transforming data into usable format to gather insights. 
  • Data manipulation and handling to pre-process the data.
  • Feature engineering and scaling the data for various problem statements. 
  • Model selection and model building on various classification, regression problems using supervised/unsupervised machine learning algorithms.
  • Assessment and monitoring of the model created using the machine learning models.

Introduction to Big Data And Spark

  • Apache spark framework, RDDs, Stopgaps in existing computing methodologies

RDDs 

  • RDD persistence, caching, General operations: Transformation, Actions, and Functions.
  • Concept of Key-Value pair in RDDs, Other pair, two pair RDDs
  • RDD Lineage, RDD Persistence, WordCount Program Using RDD Concepts
  • RDD Partitioning & How it Helps Achieve Parallelization

Advanced Concepts & Spark-Hive

  • Passing Functions to Spark, Spark SQL Architecture, SQLContext in Spark SQL
  • User-Defined Functions, Data Frames, Interoperating with RDDs
  • Loading Data through Different Sources, Performance Tuning
  • Spark-Hive Integration
  • Recommendation Engine – The case study will guide you through various processes and techniques in machine learning to build a recommendation engine that can be used for movie recommendations, restaurant recommendations, book recommendations, etc.
  • Rating Predictions – This text classification and sentiment analysis case study will guide you towards working with text data and building efficient machine learning models that can predict ratings, sentiments, etc.
  • Census – Using predictive modeling techniques on the census data, you will be able to create actionable insights for a given population and create machine learning models that will predict or classify various features like total population, user income, etc.
  • Housing – This real estate case study will guide you towards real world problems, where a culmination of multiple features will guide you towards creating a predictive model to predict housing prices.
  • Object Detection – A much more advanced yet simple case study that will guide you towards making a machine learning model that can detect objects in real time.
  • Stock Market Analysis – Using historical stock market data, you will learn about how feature engineering and feature selection can provide you some really helpful and actionable insights for specific stocks.
  • Banking Problem – A classification problem that predicts consumer behavior based on various features using machine learning models.
  • AI Chatbot – Using the NLTK python library, you will be able to apply machine learning algorithms and create an AI chatbot.
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Free Career Counselling

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Data Science Projects Covered

Peer Learning

Via Intellipaat PeerChat, you can interact with your peers across all classes and batches and even our alumni. Collaborate on projects, share job referrals & interview experiences, compete with the best, make new friends — the possibilities are endless and our community has something for everyone!

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Career Services

Career Services
Resume

Career Oriented Sessions

Throughout the course

Over 20+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These will be guided sessions and that will help you stay on track with your up-skilling objective.

Resume

Resume & LinkedIn Profile Building

After 70% of course completion

Get assistance in creating a world-class resume & LinkedIn Profile from our career services team and learn how to grab the attention of the hiring manager at the profile shortlisting stage

Resume

Mock Interview Preparation

After 80% of the course completion.

Students will go through several mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement.

Resume

1 on 1 Career Mentoring Sessions

After 90% of the course completion

Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learners’ educational background, experience, and future career aspirations.

Resume

Assured Interviews

After 80% of the course completion

Assured Interviews upon submission of projects and assignments. Get interviewed by our 500+ hiring partners.

Resume

Exclusive access to Intellipaat Job portal

After 80% of the course completion

Exclusive access to our dedicated job portal and apply for jobs. More than 400 hiring partners’ including top start-ups and product companies hiring our learners. Mentored support on job search and relevant jobs for your career growth.

Data Science Certification

What should I do to unlock Intellipaat’s certificate?

You can unlock Intellipaat’s certificate by following these three simple steps:

  1. Complete the Data Science course in Vadodara successfully
  2. Work on the industry-based projects included in the course
  3. Pass the certification exam conducted by Intellipaat

The Data Science certification you receive from Intellipaat is valid for your entire lifetime, and it is recognized by most of the leading organizations across the world.

When you complete the Data Science course in Vadodara and clear the Data Science certification exam conducted by Intellipaat, you will receive Intellipaat’s Data Science certificate on the Learning Management System (LMS). You can download the certificate or share it through email or LinkedIn.

Yes., This Data Science online certification issued by Intellipaat & CCE IIT Madras is well-recognized in the industry.

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Data Science Training FAQs

Can Intellipaat provide the Data Science course near me in Vadodara?

For Intellipaat’s courses, geographical boundaries do not apply. It does not matter in which locality of Vadodara you are in, be it Alkapuri, Sama Savl, Vasna Bhayali, Karelibagh, Fatehgunj, Sayajiganj, Manjalpur, Maneja, Akota, Atladara, or anywhere. You can access our online Data Science courses in Vadodara sitting at your home or office.

Intellipaat is the leading provider of Data Science courses in Vadodara. The courses like Artificial Intelligence, Machine Learning, R Certification, Data Science with Python, Python, Business Analytics Course and others help you to become job-ready by focussing on practical implementations on real-time live projects.

To learn Data Science for free, you need to take a look at the blogs and videos published by Intellipaat. Read the top blogs on its Interview Questions and Answers, Tutorial and everything to know about Data Scientist.

Yes. At Intellipaat, we offer a Data Science Master’s training course designed by professionals from top organizations around the globe. They will teach you and help you gain proficiency in all the basic and advanced level modules in this domain, including Big Data Analytics with Spark, Python, SQL, Deep Learning methods, R statistical computation, real-time analytics, parsing machine-generated data, etc. Moreover, you will have exclusive access to the IBM Watson Cloud Lab for Chatbots. The training involves 10 courses, 53 real-world industry projects, and 1 CAPSTONE project that will give you hands-on experience.

The courses mentioned below will be covered in this training:

Online instructor-led courses:

Course 1: Data Science with R

Course 2: Python for Data Science

Course 3: Machine Learning

Course 4: Artificial Intelligence and Deep Learning with TensorFlow

Course 5: Big Data Hadoop & Spark

Course 6: Tableau Desktop 10

Course 7: Data Science with SAS

Self-paced courses:

Course 8: Advanced Excel

Course 9: MongoDB

Course 10: MS SQL

Intellipaat is one of the most affordable e-learning providers today. This online instructor-led Data Science course in Vadodara costs ₹85,044.

No Cost EMI options are available. For more a detailed information, contact our team @ IN: +91-7022374614

At Intellipaat, you can enroll in either the instructor-led online training or self-paced training. Apart from this, Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience, and they have been actively working as consultants in the same domain, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.

Intellipaat is offering 24/7 query resolution, and you can raise a ticket with the dedicated support team at any time. You can avail of email support for all your queries. If your query does not get resolved through email, we can also arrange one-on-one sessions with our support team. However, 1:1 session support is provided for a period of 6 months from the start date of your course.

Intellipaat is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.

You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.

Intellipaat actively provides placement assistance to all learners who have successfully completed the training. For this, we are exclusively tied-up with over 80 top MNCs from around the world. This way, you can be placed in outstanding organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation as well.

You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.

Once you complete Intellipaat’s training program, working on real-world projects, quizzes, and assignments and scoring at least 60 percent marks in the qualifying exam, you will be awarded Intellipaat’s course completion certificate. This certificate is very well recognized in Intellipaat-affiliated organizations, including over 80 top MNCs from around the world and some of the Fortune 500companies.

Apparently, no. Our job assistance program is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.

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