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Data Science Course in Kottayam

5 (591 Ratings)

This Data Science Course in Kottayam is in collaboration with CCE IIT Madras. The course will help you acquire proficiency in deep learning using TensorFlow, GIT, AI, and other advanced topics through real-time projects. You will learn from IIT Madras faculty and industry experts and receive your certification from CCE IIT Madras on the successful completion of the course.

Ranked #1 Data Science Program by India TV

Data Scientist Course 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

Why should you take up this Data Science Course in Kottayam?

  • LinkedIn has over 3,000 data science job opportunities in India
  • According to Glassdoor, the average income of data scientists in India is about ₹976,000 p.a.
  • The number of jobs for data science professionals in the US will increase by 2.7 million per year—IBM
  • The global big data market will reach US$122 billion in revenue in six years—Frost & Sullivan

Today, almost all industry verticals, regardless of their customer orientation, are actively hiring data science professionals, which makes it truly worthwhile to get certified in the domain.

Intellipaat, as a premier online learning platform, offers the best Data Science Course in Kottayam to help you master concepts such as:

  • Techniques of evaluation, experimentation, and project deployment
  • Analysis segmentation using clustering and technique of prediction
  • Data visualization with Tableau
  • Natural language processing and its applications
  • Python with data science
  • Git and storytelling
  • Data analysis, project life cycle, and data science in the real world
  • ML algorithms
  • Microsoft Excel for data analysis and data transformation
  • Data science at scale with PySpark and AI with TensorFlow
  • Deploying machine learning models on clouds (MLOps)
  • Data science projects, analytics, and recommender systems

Intellipaat’s Data Science Course is designed for:

  • Big Data Experts
  • Big Data Statisticians
  • ML Professionals
  • BI Professionals and Analysts
  • Information Architects
  • Predictive Analytics Experts
  • Freshers or professionals looking for a data science career

There are no prerequisites for taking up this Data Science Course in Kottayam. However, an interest in mathematics will help you.

According to PayScale, the average salary of a data scientist in Kottayam is ₹450,000 p.a.

On average, a data science professional earns almost 20 percent more than the national average. One of the top five skills with the most pay in the domain include:

  • Statistical Analysis—₹1,100,000 p.a.
  • Machine Learning—₹1,050,000 p.a.
  • R—₹985,000 p.a.
  • Data Analysis—₹983,000 p.a.
  • Python—₹980,000 p.a.

Some of the top employers hiring data science professionals are Mu Sigma, IBM, Accenture, Amazon, EY, Flipkart, Robert Bosch, etc.

The top companies that hire data science professionals are:

  • Fidelity Investments
  • Accenture
  • Aon
  • Oath
  • MSD
  • Intel
  • Amazon
  • Google

In this Data Science Course in Kottayam, you will work on real-world industry-based projects, which will help you gain hands-on experience in the field and prepare you for challenging roles.

Industry Project Name Objective
BFSI Fraud detection in banking system Detecting fraudulent activities and taking remedial actions
Entertainment Movie recommendation engine Building a movie recommendation engine that is based on the users’ interests
E-commerce Making sense of customer buying patterns Deploying target selling
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Talk To Us

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

Contact Us

Data Scientist Training 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

We are happy to help you 24/7

Data Science Projects

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.

Advanced Data Science Certification

CCE IIT Madras Digital Skills Academy has initiated various programs in partnership with NASSCOM. The courses offered by them are taught by leading academics and aim to upskill millions of students and professionals in trending technologies through a blend of theoretical and hands-on knowledge.

On the successful completion of this course, you will:

  • Receive an Advanced Certification in Data Science and AI from CCE IIT Madras Center for Continuing Education (CCE)

Key Achievements of CCE, IIT Madras:

  • Ranked 1 by NIRF for the last three years
  • Ranked 54 in Asia by the QS World University Rankings in 2022

Data Scientist Course Reviews

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Data Science Course FAQs

Why should I opt for a Data Science Course in Kottayam from Intellipaat?

Intellipaat offers an online Data Science Course in Kottayam exclusively for professionals who want to expand their knowledge base and start a career in this field. There are several reasons why you should consider Intellipaat for your learning:

  • Personal mentor to track your progress
  • Immersive online instructor-led sessions conducted by SMEs
  • Extensive LMS, allowing you to view recorded sessions within three hours
  • Real-time exercises, assignments, and projects
  • 24/7 learning support
  • Large community of like-minded learners
  • Industry-recognized Intellipaat certifications
  • Personalized job support

Intellipaat has been serving data science enthusiasts from every corner of the city. You can be living in any locality in Kottayam, be it Adukkam, Alanad, Erumely, Adivaram, Karipadom, Kunnam, or Veroor,you can have 24/7 access to our Data Science Course while sitting at your home or office.

Here is the list of all areas where we provide our Data Science Course in Kottayam:

Area Pincode
Adivaram 686582
Adukkam 686580
Akkarapadom 686143
Alanad 686651
Alapra 686101
Amalagiri 686036
Amanakara 686576
Amara 686546
Amayannur 686025
Ambika Market 686144
Ampara Nirappel 686578
Anakkal 686508
Anikad 686503
Anikad East 686503
Anikad West 686503
Anjoottimangalam 686579
Anthinad 686651
Areeparampu 686501
Arpookara East 686008
Arumanur 686568
Arunapuram 686574
Arunoottimangalam 686604
Aruvikuzhy 686503
Aruvithura 686122
Athirampuzha 686562
Ayamkudy 686613
Ayarkunnam 686564
Aymanam 686015
Ayyarkulangara 686142
Bharananganam 686578
Brahmamangalam 686614
Chakkampuzha 686574
Chakompathal 686517
Chamakala 686603
Champakara 686540
Changanacherry 686101
Changanacherry College 686101
Changanacherry Industrialnagar 686106
Changanacherry Market 686101
Channanikadu 686533
Cheepunkal 686563
Cheeranchira 686106
Chelacompu 686540
Chemmalamattom 686508
Chemmanathukara 686606
Chempilavu 686584
Chempu 686615
Chenappady 686520
Chengalam 686585
Chengalam South 686022
Chennad 686581
Chennakunnu 686506
Cherpunkal 686584
Cheruvally 686543
Chettuthode 686508
Chingavanam 686531
Chirakadavu 686520
Chirakadavu Centre 686519
Chirakadavu East 686520
Chittadi 686512
Cholathadom 686582
Choondacherry 686579
Chovvur 686586
Devagiri 686555
Devalokam 686038
Edakadathy 686510
Edakkunnam 686512
Edamaruku 686652
Edanad 686574
Edappady 686578
Edavattom 686605

Intellipaat provides top courses in data analytics, artificial intelligence, machine learning, R, Python, Python for data science, business analytics, etc., to help you grow in your data science career.

To learn data science for free, you can go through the blogs and YouTube videos published by Intellipaat. Read the top-recommended blogs on data science interview questions, tutorial, and everything else about data science.

This online Data Science Course is curated by top data scientists from India and the United States. The SMEs have designed the program in such a manner that even if you are from a nontechnical background and have almost zero knowledge of this domain, you can still learn all the concepts easily. We also provide practical experience through real-time projects that allow even freshers to easily grasp the concepts.

Intellipaat does not directly forward resumes to any company or recruiter. However, we do have a placement team that will conduct a number of mock interviews and assist you in updating your resume to prepare you for job interviews. The team, thus, will help you land a lucrative job in the data science domain.

In the career mentoring session at Intellipaat, our data science experts offer solutions to all your queries on career opportunities and possible growth in the domain.

If you wish to enroll in our Data Science Course in Kottayam, then you need to first make a choice between online instructor-led and self-paced training. Once you do that, you can make the payment using any major credit card, debit card, or EMI option.

If you wish to enroll in our Data Science Course in Kottayam, then you need to first make a choice between online instructor-led and self-paced training. Once you do that, you can make the payment using any major credit card, debit card, or EMI option.

Intellipaat selects SMEs from top MNCs, who have at least 8 to 12 years of experience in the domain, as instructors. The instructors are selected after they successfully go through our rigorous selection process.

Intellipaat’s teaching assistants are SMEs whose main aim is to make you a certified professional in the respective domain. The trainers conduct interactive video lectures to teach the latest technologies and enrich your experience with various industry-based projects. The teaching assistance provided by Intellipaat is only available during regular hours.

This online Data Science Course comprises all the topics that are required and significant to learn so that you can master this technology. The course comprises both basic and advanced concepts involved in this technology so that you can learn them and master the skills to pursue a career in this domain. Moreover, the trainers of this course are experts in the domain who spend time and effort to teach you all the concepts in detail.

The supply of data science professionals in India is far less than the demand; in recent times, the demand for data science professionals has increased by nearly 400 percent. Kottayam has a high demand for this role, but only accredited learners can secure a lucrative job.

Since it involves various aspects of advanced technologies, such as machine learning, deep learning, and artificial intelligence, data science is comparatively difficult to learn. However, this online Data Science Course is taught by IIT Madras faculty and industry experts who have a lot of experience in the field. They make all concepts easy to understand as they explain each concept in detail with the help of several real-life examples.

Intellipaat provides various group offers and discounts for its online courses as per the size and type of group. If you wish to avail the discount, you need to get in touch with our course advisors who will explain all the details to you.

Intellipaat is one of the most affordable e-learning providers today. This online Data Science course in Kottayam 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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