Skill Up Today With Knowledge and Build a Brighter Tomorrow for Yourself - Flat 20% Off Course Fee Enroll Now!

Data Science training in chennai-Edubrights

16845 Ratings

Data Science Course in Chennai

Launch your career in the fast-growing field of data science with Edubrights' Data Science Course in Chennai. Designed for students, freshers, job seekers, and working professionals, this training program helps you build practical skills in data analysis, machine learning, statistical modeling, and predictive analytics through hands-on learning and real-world projects.

Learn how organizations use data science to solve complex business problems, uncover valuable insights, automate decision-making, and drive innovation. Through practical case studies, industry-relevant assignments, and project-based training, you will gain the confidence to work with real-world datasets and data-driven solutions.

Key Highlights:

✅ Real-Time Data Science Projects & Industry Case Studies

✅ Hands-On Training in Machine Learning & Predictive Analytics

✅ Data Visualization, Statistical Analysis & Data Modeling Techniques

✅ Practical Exposure to Business Problem Solving Using Data

✅ Industry-Aligned Curriculum Designed by Experienced Trainers

✅ Certification Guidance & Career Development Support

✅ Resume Building, Mock Interviews & Placement Assistance

✅ Flexible Online, Classroom & Weekend Training Options

Start your data science journey with Edubrights and gain the practical skills, project experience, and industry knowledge needed to become a job-ready Data Science professional.

Call Course Advisor

₹19800

₹25000

Data Science training in chennai-Edubrights thumbnail

Enroll Now View Syllabus

Placement Assistance

100%

Case Studies and Projects

Hours of Training

20+

Mock Interview Preparation

100%

Expert Support

Yes

Support & Access

Lifetime

Certification

Yes

Language

All

Course Objectives

What exactly Data Science training cover?

Data Science training covers data analysis, statistical modeling, and machine learning using tools like Python, R, and SQL. It teaches how to collect, clean, visualize, and interpret large datasets to solve real-world problems. The training often includes hands-on projects and model deployment for practical experience.

Best Data Science Course in Chennai — EduBrights

Data Science is one of the highest-paying and fastest-growing careers in India. At EduBrights, our Data Science course in Chennai covers Python, statistics, machine learning, deep learning, and real-world projects. Our industry-certified trainers have 10+ years of experience and our students get placed in top MNCs like TCS, Infosys, Wipro, and HCL.

Why Learn Data Science at EduBrights?

Industry-certified trainers with 10+ years of real-world experience in Data Science Flexible batch timings — weekday, weekend, morning, and evening batches Hands-on training with live projects, case studies, and industry datasets Small batch sizes (max 15 students) for personalized attention 100% placement assistance with resume building and mock interviews Globally recognized certification upon course completion Course curriculum updated quarterly to match latest Data Science industry demands Lifetime access to study materials, recorded sessions, and LMS portal

Deploy MLOps and Model Automation:

Package, containerize, and host production-grade models using Docker and cloud-native endpoints

Popular Techniques Covered in This Course

Mathematics & Statistics

Programming

Data Handling & Analysis

Machine Learning

Data Visualization

Advanced Predictive Modeling

Deep Neural Network Engineering

Statistical Inference & Experimentation

MLOps & Pipeline Orchestration

Feature Engineering & Transformation

Get Hands-on Knowledge about Real-Time Projects

Project 1

1.COVID-19 Data Dashboard

Goal: Show the number of COVID-19 cases, recoveries, and fatalities for various areas over time.

Project 2

2. Movie Recommendation System

Goal: Create a basic system that makes movie recommendations to people based on their prior tastes or ratings.

Project 3

3. Titanic Survival Prediction

Goal: Use characteristics like age, sex, and class to forecast survival odds by analyzing passenger data.

Project 4

4: Enterprise Customer Sentiment & Semantic Search Engine

Create an interactive Natural Language Processing pipeline. You will ingest multi-source customer feedback, extract underlying semantic themes via Hugging Face transformers, and structure the data within a local vector database for intelligent querying.

Project 5

5: Production-Grade Generative AI Knowledge Base (Capstone)

Architect an end-to-end Retrieval-Augmented Generation ($RAG$) application. You will ingest unformatted corporate handbooks, process them via text-chunking pipelines into a vector store, link them to an open foundation model, and deploy the entire application using Docker containers

Key Features

Edubrights offers Data Science Training in virtual mode with expert trainers. Here are the key features,

40 Hours Course Duration

100% Job Oriented Training

Industry Expert Faculties

Free Demo Class Available

Completed 500+ Batches

Certification Guidance

Curriculum

Basics of Data Science

What is data science and how it works
Data science vs machine learning vs data analytics
Real-world applications across industries
The data science project lifecycle — end to end

Mathematics for Data Science

Vectors, matrices, and linear algebra fundamentals
Matrix operations and transformations
Descriptive statistics — mean, median, mode, and variance
Probability distributions — Normal, Binomial, and Poisson
Bayes' theorem and conditional probability
Hypothesis testing and p-value analysis
Gradients, derivatives, and gradient descent optimisation

Programming for Data Science Python (Primary Language)

Data types, loops, functions, and OOP concepts
Libraries: NumPy, Pandas, Matplotlib, and Seaborn

R (Optional)

Core syntax and statistical modelling basics
Data visualisation using ggplot2

Data Wrangling & Manipulation

Reading and writing data — CSV, JSON, and Excel
Data cleaning, wrangling, and quality checks
Handling missing values and outliers
Merging, reshaping, and combining datasets
Working with date and time data

Data Visualisation & EDA

Bar, line, histogram, scatter, and box plots
Heatmaps and correlation matrices
Interactive visualisations using Plotly, Dash, Tableau, and Power BI
Univariate, bivariate, and multivariate analysis
Correlation, covariance, and summary statistics
Feature understanding and selection

** SQL & Databases**

Relational databases and schema design
SQL queries — SELECT, JOIN, GROUP BY, and subqueries
NoSQL fundamentals using MongoDB

Machine Learning — Supervised & Unsupervised Supervised Learning

Linear and logistic regression
Decision trees and random forests
Support vector machines (SVM)
k-Nearest Neighbors (k-NN) Unsupervised Learning
Clustering — K-Means and hierarchical
Dimensionality reduction — PCA and t-SNE

Model Evaluation & Improvement

Train/test split and cross-validation
Accuracy, precision, recall, F1-score, and confusion matrix
ROC-AUC curve analysis
Hyperparameter tuning — Grid Search and Random Search

Deep Learning & NLP Neural Networks

Perceptrons and feedforward networks
Activation functions — ReLU, Sigmoid, and Tanh
Backpropagation and optimisers — SGD and Adam
Introduction to CNNs and RNNs
Frameworks: TensorFlow, Keras, and PyTorch Natural Language Processing
Bag of Words and TF-IDF
Word embeddings — Word2Vec and GloVe
Sentiment analysis and topic modelling

Big Data, Time Series & Capstone Project Time Series

Stationarity, trends, and seasonality
Autocorrelation and decomposition
ARIMA and SARIMA models
Forecasting techniques

Big Data & Cloud

Hadoop and Apache Spark fundamentals
Cloud platforms — AWS (S3, EC2), GCP (BigQuery), and Azure
Overview of distributed computing

Receive Training From Our Skilled and Effective Trainers

Experience in the Industry Gain knowledge from experts with practical Data Science project experience in a variety of sectors.

Backgrounds at the Top Prominent corporations such as hcl, tcs , accenture and cognizant employ trainers.

Clear & Effective Teaching Excellent communication and real-world examples simplify complex subjects.

Hands-On Learning Focus Students can apply their abilities in real-world situations with the use of case studies and real-time projects.

Up-to-Date Knowledge Trainers stay current with the latest tools, techniques and best practices.

Earn a certification upon course completion

• 🌟 Boost your career growth with credentials that showcase your expertise. • 🌟 Stand out from the competition in a crowded job market. • 🌟 Increase your earning potential with industry-recognized skills. • 🌟 Gain hands-on, practical knowledge applicable to real-world projects. • 🌟 Build employer confidence by demonstrating validated and up-to-date skill sets. • 🌟 Unlock global job opportunities with certifications accepted worldwide. • 🌟 Strengthen your resume and LinkedIn profile with credible achievements. • 🌟 Enhance job security by positioning yourself as a skilled, certified professional. • 🌟Make a name for yourself in a congested job market. • 🌟Acquire practical, hands-on knowledge that may be applied to real-world tasks. • 🌟Gain the trust of employers by showcasing current and validated skill sets. • 🌟Gain access to international employment prospects with globally recognized certificates. • 🌟 Present yourself as a knowledgeable, qualified professional to increase job security. • 🌟Increase your self-assurance to tackle difficult assignments, interviews, and technical conversations.

Course FAQs

1. What is Data Science?

Data Science is a multidisciplinary field that uses statistics, algorithms, and technology to extract insights and knowledge from structured and unstructured data.

2. What are the key skills required for Data Science?

Programming (Python, R)
Statistics & Probability
Machine Learning
Data Visualization
SQL & Databases

3. What’s the difference between Data Science, Data Analytics, and Machine Learning?

Data Science: Broad field covering data analysis, modeling, and deployment
Data Analytics: Focuses on analyzing historical data for insights
Machine Learning: Subfield of AI where machines learn from data without explicit programming

4. Do I need a background in programming or math to start Data Science?

A basic understanding of programming and math (especially linear algebra, calculus, and statistics) is very helpful, but you can start learning progressively.

5. What tools and technologies are commonly used in Data Science?

Programming: Python, R
Libraries: NumPy, Pandas, Scikit-learn, TensorFlow
Tools: Jupyter, SQL, Tableau, Power BI
Cloud: AWS, Azure, Google Cloud

6. What high-growth career designations can I target after graduating?

You can target elite professional tracks including Data Scientist, Machine Learning (ML) Engineer, NLP Research Specialist, MLOps Integration Architect, or Advanced Analytics Consultant.

7. How does Data Science differ from standard Data Analytics?

Data Analytics focuses on looking backward, using SQL and business intelligence software to interpret historical patterns and explain what happened. Data Science looks forward, writing code to build automated machine learning models that predict what will happen next and run actions autonomously.

8. Are laboratory training sessions held in person or online?

We host adaptive, flexible learning schedules. Students can choose to study via immersive hands-on projects inside our physical laboratory facilities in Chennai or connect via interactive live virtual streaming classrooms

9. What type of placement support does your institute provide?

We provide rigorous career guidance, featuring professional portfolio architecture workshops, technical resume polishing, simulated mock engineering whiteboard reviews, and direct applicant routing pipelines to top tech organizations.

10. Why is Python preferred over R for modern Data Science workloads?

While R is exceptional for standalone academic statistical research, Python serves as a general-purpose programming language. Python's production-ready design allows you to integrate your machine learning models directly into web apps, cloud pipelines, and live enterprise products effortlessly.

11. What is the core function of the MLOps discipline?

Machine Learning Operations (MLOps) focuses on bridging the gap between model training and actual product deployment. It unifies software engineering principles with data workflows to ensure models stay monitored, scalable, and easy to update without breaking live systems.

12. What is the difference between a shallow Machine Learning model and Deep Learning?

Shallow Machine Learning models (like linear regressions or decision trees) rely on humans to manually engineer features out of datasets. Deep Learning models use multi-layered artificial neural networks to automatically discover, learn, and extract optimal features from raw data like images, audio, or text

13. Why are Vector Databases essential for Generative AI applications?

Standard SQL databases store structured tables of text or numbers. Vector databases store data as high-dimensional mathematical values (embeddings), allowing modern AI models to search across billions of document components simultaneously based on conceptual meaning rather than exact keyword matches.

14. What type of computing hardware do I need to complete my projects?

Our physical campus in Chennai features dedicated laboratories equipped with high-performance graphic processing units (GPUs) for deep learning operations. If working from home, standard laptop hardware paired with free cloud-accelerated coding environments is completely sufficient to execute your assignments smoothly.