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Data Science and Artificial Intelligence

Unlock the power of tomorrow’s technology through this all-in-one learning experience that blends core Data Science fundamentals with cutting-edge Generative AI expertise. This program offers hands-on capstone projects, interactive masterclasses, live learning sessions, and insights from seasoned professionals in the field.

Gain practical experience through our data science course, covering Python programming, Machine Learning, Deep Learning, Natural Language Processing (NLP), MLOps, RNNs, GANs, Attention mechanisms, Transformers, BERT, and Business Intelligence tools.

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Certification: Data Science & Artificial Intelligence

The Most In-Demand and Highly Paid Skills

Learning Paths

Certification (4 Months) | Diploma (6 Months)

10000+ Learners

Trained Students Across The Country

360° Career Support

Resume Building, Interview Prep, and Access to Partner Companies

Course Key Highlights

Immersive Classroom & Online Blended Training
Hands-On Learning Guided by Data Science & AI Experts
Applied Case Studies from Industry Domains
AI-Enhanced Modules Featuring Generative AI Applications
Dedicated Career Portal for Data Science & AI Roles
Recognized Certification in Data Science & AI
Project-Based Learning with Real-World Datasets
1:1 Career Mentorship with Data & AI Professionals
Interactive Learning with Practical Coding Exercises
360° Career Support Including Resume & Portfolio Building
Live Doubt-Clearing and Concept Reinforcement Sessions
Network of 50+ Hiring Corporate Partners
Flexible Learning with Immersive + Online Modules
Capstone Projects in Machine Learning, Deep Learning & AI
Personalized Job Interview Preparation (1:1)
Hands-On Experience with 10+ Data Science & AI Tools
Direct Access to Recruitment Channels of Top MNCs

Globally Accredited Data Science Course in India

Delve into the entire Data Science lifecycle, encompassing Data Collection, Extraction, Cleansing, Exploration, Transformation, and beyond. Explore a vast array of skills and tools, from Statistical Analysis to Text Mining, Regression Modeling to Deep Learning, all meticulously covered in our data science course curriculum in India. At Institute of Analytics, we go beyond mere data science training – we offer a pathway to success. Join us in India, and embark on a journey to become a sought-after Data Science professional in India.

Why Should You Choose Data Science Course?

Embark on your journey towards a rewarding career in Data Science , recognized as one of India‘s premier data science training institutes. With a proven track record of shaping the careers of numerous Data Science professionals, both locally and internationally, we stand as a beacon of excellence in the field. Benefit from our expert trainers, each possessing over 15 years of professional experience in the industry. Our dual data science certification in Data Science & Data Analytics, widely regarded as the best in the industry, is tailored to equip you with the skills and knowledge needed to excel in the competitive landscape of Data Science in India. We offer a blended learning model that combines data science classroom sessions, instructor-led online sessions, and e-learning modules. Our dedicated data science placement cell and extensive network of 350+ corporate partners ensure that you receive ample opportunities for interviews and data science placement assistance. Whether you’re a seasoned professional looking to upskill or a fresh graduate aspiring to kickstart your data science career in India, our comprehensive Data Science course in India is designed to meet your needs and exceed your expectations. Join us today and take the first step towards unlocking your full potential in the dynamic field of Data Science in India.

20+ Programming Tools, Libraries & Technologies Covered

Keras
Scikit-Learn
Seaborn
TensorFlow
NLP

10+ Generative AI Tools, Libraries & Technologies Covered

Gamma
Gemini
Gradio
HF
OpenAI

Course Overview

This comprehensive course provides a deep dive into the foundational and advanced concepts of Data Science and Artificial Intelligence. You'll learn to analyze data, build predictive models, develop machine learning solutions, and understand the ethical implications of AI, preparing you for a data-driven career.
  • Defining Data Science: Interdisciplinary Nature
  • Data Science Lifecycle: CRISP-DM Methodology
  • Key Roles in Data Science (Analyst, Engineer, Scientist)
  • Defining AI, Machine Learning, and Deep Learning
  • Historical Milestones and Current Trends in AI
  • Applications of AI in Various Industries
  • Linear Algebra (Vectors, Matrices)
  • Calculus (Derivatives, Gradients)
  • Probability Theory (Distributions, Bayes' Theorem)
  • Descriptive and Inferential Statistics
  • Relational Databases (SQL)
  • NoSQL Databases
  • APIs and Web Scraping
  • Data Formats (CSV, JSON, XML, Parquet)
  • Handling Missing Values
  • Outlier Detection and Treatment
  • Data Transformation (Scaling, Normalization)
  • Feature Engineering
  • Descriptive Statistics with Pandas
  • Data Visualization with Matplotlib and Seaborn
  • Correlation Analysis
  • Regression (Linear, Polynomial, Ridge, Lasso)
  • Classification (Logistic Regression, Decision Trees, SVM, K-NN)
  • Ensemble Methods (Random Forest, Gradient Boosting)
  • Model Evaluation (Metrics, Cross-Validation)
  • Clustering (K-Means, Hierarchical, DBSCAN)
  • Dimensionality Reduction (PCA, t-SNE)
  • Association Rule Mining
  • Bias-Variance Tradeoff
  • Grid Search, Random Search
  • Feature Importance and Interpretability
  • Perceptrons and Multilayer Perceptrons (MLPs)
  • Activation Functions and Backpropagation
  • Optimizers (SGD, Adam, RMSprop)
  • Convolutional Layers, Pooling Layers
  • Image Classification and Object Detection
  • Transfer Learning and Pre-trained Models
  • Sequence Data and Time Series Analysis
  • LSTMs and GRUs
  • Introduction to Transformer Architecture
  • Natural Language Processing (NLP) Applications
  • TensorFlow and Keras
  • PyTorch
  • Text Preprocessing and Embeddings
  • Sentiment Analysis and Text Classification
  • Machine Translation and Chatbots
  • Image Segmentation
  • Facial Recognition
  • Generative Models for Images (GANs, VAEs, Diffusion)
  • Version Control for Data and Models
  • Model Deployment and Monitoring
  • Pipelines and Orchestration (e.g., Kubeflow, Airflow basics)
  • Bias, Fairness, and Transparency in AI Models
  • Privacy and Data Security in AI Systems
  • Accountability and Governance of AI
  • Importance of Model Interpretability
  • Techniques for Explaining Predictions (LIME, SHAP)
  • Reinforcement Learning
  • Quantum Machine Learning
  • AI for Scientific Discovery

Enrollment Process

Step 1
Application Submission

Prospective students complete and submit an online application form, providing essential profile details for enrolling in the Course / Training.

Step 2
Application Review and Discovery

After a thorough review of applications by the academics team, qualified candidates receive a call from our experienced counselor who guides them with the details of the Course / Training

Step 3
Enrollment Offer

Successful candidates receive an offer of admission, and upon acceptance, proceed to complete the enrollment process. This includes submitting necessary documents, paying fees, and attending an orientation session to kickstart their educational journey