Course Objectives
1. 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.
2. 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.
3. 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
4. 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 expertise from experienced Java professionals with practical project exposure across diverse industries. Learn Java through real-world development practices, enterprise applications, backend systems, and industry-focused use cases.
Backgrounds at the Top
Learn from trainers with professional experience across leading organizations and technology environments, including exposure to projects associated with companies such as HCL, TCS, Accenture, and Cognizant. Their industry experience brings practical development knowledge into every Java training session.
Clear & Effective Teaching
Complex Java programming concepts, object-oriented programming, frameworks, APIs, databases, and application development are explained through simple examples and real-world scenarios. The training focuses on helping learners understand both the concepts and how they are applied in professional projects.
Hands-On Learning Focus
Students gain extensive hands-on practice through coding exercises, case studies, mini-projects, and real-time application development scenarios. This practical approach helps learners strengthen their programming skills, problem-solving abilities, and confidence in building Java applications.
Up-to-Date Knowledge
Trainers continuously update the Java course curriculum with the latest tools, development techniques, frameworks, libraries, and industry best practices. Learners gain relevant skills aligned with modern Java development and current enterprise application requirements.
Earn a certification upon course completion
Our certification highlights your ability to analyze complex datasets, build predictive models, and deliver actionable insights. It prepares you for diverse roles in data-driven industries.
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.
Get Training Quote for Free
Testimonials
Hear What Our Students Say
"Transform your life through Education, hear it from our Alumni"
6 LPA
Fathima
Student
Software Engineer
"Transform your life through Education, hear it from our Alumni"
8 LPA
Rahul
Student
Data Scientist
"Transform your life through Education, hear it from our Alumni"
8 LPA
Siddharth
NIELSON IQ
Data Analyst
