Courses

Back

Corporate Training Hire From Us Explore Courses
Intellipaat collaboration image

Data Science Course in Thrissur

92,113 Ratings

This Data Science course in Thrissur will help you in mastering skills such as GIT, AI, Deep Learning using TensorFlow, and others through the use of real-world projects. With rapid advancements in technology, there has been great demand for data science to provide insights from vast data. Get certified by IITM Pravartak after learning from industry experts.

Ranked #1 Data Science Course by India TV

Intellipaat courses

Watch

Course Preview

Data Scientist Course Key Highlights

50+ Live sessions across 7 months
218 Hrs Self-paced Videos
200 Hrs Project & Exercises
Learn from IIT Madras Faculty and Industry Practitioners
1:1 with Industry Mentors and 24*7 Support
Resume Preparation and LinkedIn Profile Review
Campus Immersion at IIT Madras
No-cost EMI Option
Reviews Image 1429
Reviews Image 3332
Reviews Image 24068

Data Science Course in Thrissur Overview

Why should you take up the Data Scientist course in Thrissur?

  • As per Glassdoor, the average income of Data Scientists in India is about ₹976k per annum
  • LinkedIn has over 3,000 Data Science job opportunities in India
  • Data Scientist is the best job of the 21st century – Harvard Business Review
  • The global Big Data market to reach US$122 billion in revenue in 6 years – Frost & Sullivan
  • The number of jobs for all the US data professionals will increase to 2.7 million per year – IBM

Today, almost all industry verticals, regardless of their customer orientation, are actively hiring Data Scientists, which makes it truly worthwhile to get certified.

Intellipaat is a premier online coaching institute that offers the best Data Science courses in Thrissur, which help you master concepts such as

  1. Data analysis, project life cycle, and Data Science in the real world
  2. Machine Learning algorithms
  3. Techniques of evaluation, experimentation, and project deployment
  4. Analysis segmentation using clustering and the technique of prediction
  5. Python with Data Science
  6. Git, Storytelling
  7. Data Science at scale with PySpark, AI with TensorFlow
  8. Deploying Machine Learning Models on Clouds ( MLOps)
  9. Data visualization with Tableau
  10. Natural Language Processing and its Applications
  11. Microsoft Excel for data analysis and data transformation
  12. Data Science projects, analytics, and recommender Systems
Job Role Average Salary (INR)
Data Analyst ₹10.0 Lakhs
Data Scientist ₹12.6 Lakhs
Business Analyst ₹9.5 Lakhs
Machine Learning Engineer ₹13.5 Lakhs
Data Engineer ₹12.0 Lakhs
Research Scientist ₹14.5 Lakhs

Top companies that hire Data Scientists are:

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

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

*Past record is no guarantee of future job prospects

Meet the Data Science 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.
  • Anyone who wants to learn machine learning, artificial intelligence, data visualization, data analytics, data structures, and algorithms (DSA).
Who can apply

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 into web apps and deploy models in SageMaker.

Data Engineer & Data Analyst

Perform data cleansing, and 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 business products through designing and developing machine learning models.

View More

Skills Covered

Python

Data Science

Data Analysis

AI

GIT

MLOps

Data Wrangling

SQL

Story Telling

Machine Learning

Prediction algorithms

NLP

PySpark

Model

Data visualization

View More

Tools Covered

pyspark python jupyter Scipy numpy pandas matplotlib tensorflow SQL tableau excel git SparkSQL
View More

Data Science Course in Thrissur Fees

Online Classroom Preferred

  • Live Classes from IIT Madras Faculty & Industry Experts
  • Certification from IITM Pravartak
  • Career Services( Mock Interviews, Resume Preparation)
  • Placement Assistance upon clearing PRT
  • Dedicated Learning Manager
02 Mar

SAT - SUN

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

09 Mar

SAT - SUN

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

$1,229 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 Industry Expert Academic Faculty

Module 1 – Preparatory Session - Linux and Python

Preview

Python 

  • Introduction to Python and IDEs– The basics of the Python programming language, and 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 OOP 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
Download Brochure

Module 2 – Data Wrangling with SQL

Preview

SQL Basics – 

  • Fundamentals of Structured Query Language
  • SQL Tables, Joins, and 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, SQL ROLLUP, etc.

SQL Optimization and Performance

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

Hands-on exercise: 

Writing comparison data between the past year and the present year with respect to top products, ignoring the redundant/junk data, identifying the meaningful data,  and identifying the demand in the future (using complex subqueries, functions, and pattern matching concepts).

Download Brochure

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, and Matrices

Data Manipulation Using Pandas

  • Loading the data, data frames, 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
Download Brochure

Descriptive Statistics – 

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

Probability 

  • Probability Distributions, Bayes’ theorem, and central limit theorem

Inferential Statistics –  

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

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, and 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
Download Brochure

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

Artificial Intelligence Basics 

  • Introduction to keras API and TensorFlow

Neural Networks

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

Deep Learning

  • Introduction to Deep Learning (by Academic Faculty)
  • Deep neural networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • GPU in deep learning
  • Autoencoders, restricted Boltzmann machine
Download Brochure

The Data Science capstone project focuses on establishing a strong grasp 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, and regression problems using supervised/unsupervised machine learning algorithms
  • Assessment and monitoring of the model created using the machine learning models
Download Brochure
  • 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 toward 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 with 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.
Download Brochure

Electives:

Power BI Basics

  • Introduction to Power BI, 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

Hands-on Exercise:

Creating a dashboard to depict actionable insights in sales data

Download Brochure

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
Download Brochure
  • Job Search Strategy
  • Resume Building
  • LinkedIn Profile Creation
  • Interview Preparation Sessions by Industry Experts
  • Mock Interviews
  • Placement opportunities with 400+ hiring partners upon clearing the Placement Readiness Test
Download Brochure

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 Tables, Dashboards, Data And File Security
  • VBA Macros, Ranges and Worksheets 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, and Maps
  • Multiples: A set of charts with the same axes, Matrices, Cards, and 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, and Dependence 
  • Mutual Information 

Regression Problems Using Excel

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

Hands-on Exercise:

Classification problem using Excel on sales data and statistical tests on various samples from the population

Download Brochure
View More

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!

class-notifications
Hackathons
career-services
major-announcements
collaborative-learning

Career Services

Career Services

Career Oriented Sessions

Throughout the course

Over 10+ 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 that will help you stay on track with your up upskilling.

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

Mock Interview Preparation

After 80% of the course completion.

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

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 learner’s educational background, past experience, and future career aspirations.

Placement Assistance

Upon movement to the Placement Pool

Placement opportunities are provided once the learner is moved to the placement pool upon clearing Placement Readiness Test (PRT)

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

certificateimage Click to Zoom

About IITM Pravartak:

IITM Pravartak, a Technology Innovation Hub of IIT Madras is funded by Department of Science and Technology, GOI under its National Mission on Interdisciplinary Cyber-Physical Systems (NM-ICPS), focuses on application-oriented research and innovation in the areas SNACS.

BharOS, India’s first mobile operating system was developed by an IITM Pravartak incubated company.

Key Achievements of IIT Madras:

  • Ranked No.: 1 in India in both ‘Overall’ and ‘Engineering’ Categories in NIRF 2022 for the last 4th consecutive year.
  • IIT Madras has been identified as an ‘Institution of Eminence’ by the Government of India.
  • Ranked No. 4 Indian Institute in QS World University Ranking and Ranked #250 in the International QS World Rankings  2023.

Data Scientist Course in Thrissur Reviews

( 92,113 )

Our Alumni Works At

Master Client Desktop

Hear From Our Hiring Partners

Data Science Training in Thrissur FAQs

How do I enroll in the Data Scientist course?

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

Intellipaat offers exclusive Data Science courses in Thrissur for professionals who want to expand their knowledge base and start a career in this field. There are many reasons for choosing Intellipaat:

  • A personal mentor to track your progress
  • Immersive online instructor-led sessions conducted by SMEs
  • Extensive LMS, allowing you to view recorded sessions within 3 hours
  • Real-time exercises, assignments, and projects
  • 24/7 learning support
  • A large community of like-minded learners
  • Industry-recognized Intellipaat badge
  • Personalized job support

Intellipaat is the leading course provider in Thrissur. Our e-learning institute provides top courses in Data Analyst Course, Artificial Intelligence, Machine Learning, R programming language, Python, Python for Data Science, Business Analytics, and others to help you gain full control of your Data Science career path.

To learn Data Science for free, you need to look at the blogs and YouTube videos published by Intellipaat. Read the top-recommended blogs on their Interview Questions, Tutorial, and everything about Data Science.

This online training is curated by top Data Scientists from India and the United States. These SMEs have designed the course in such a manner that even if you are from a non-technical background and have almost zero knowledge of this domain, you can still learn and adapt all concepts easily. Also, we provide practical experience through real-time projects that allow even freshers to easily grasp the concepts.

In the career mentoring session at Intellipaat, our Data Science experts offer solutions to all your queries that are based on career opportunities and the growth available in this domain.

Learners are required to submit the mandatory assignments, project work and quizzes for receiving the Intellipaat verified certificates.

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.

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 provides placement assistance to all learners who have successfully completed the training and moved to the placement pool after clearing the PRT( Placement Readiness Test) More than 500+ top MNC’s and startups hire Intellipaat learners. Our Alumni works with Google, Microsoft, Amazon, Sony, Ericsson, TCS, Mu Sigma, etc.

Apparently, no. Our job assistance 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.

View More