Course Curriculum

Comprehensive Course Curriculum

Our industry-aligned curriculum is designed by experts to help you master the most in-demand data science and AI skills and prepare you for a successful career.

Data Foundations & AI-Ready Business Tools

Build a solid base in handling, cleaning, structuring, and visualizing data with the tools used by top AI teams. You’ll learn how to work with spreadsheets, write efficient SQL queries, and design dashboards that communicate insights clearly. This module is designed to make you fluent in the essential tools of a data-driven AI professional.

By the end, you’ll be comfortable working with relational databases, performing transformations, and preparing data pipelines for AI workflows.

What you'll learn:

Data Analysis & Management
Dashboards & Visual Insights
Data Cleaning for AI/ML

Excel + Google Sheets

Master Excel and Google Sheets, including advanced formulas, pivot tables, dashboards, and What-if analysis, while leveraging real-time collaboration features for teams to efficiently analyze and present business data. Read More

Opportunities it opens

SQL + Databases

Gain expertise in SQL and databases by performing CRUD operations, joins, aggregations, views, window functions, indexing, and schema design to query, manage, and optimize relational databases for analytics projects. Read More

Opportunities it opens

Power BI/Tableau

Learn to create KPI dashboards and compelling data stories in Power BI and Tableau, using calculated fields, AI-driven visual insights, and interactive reports to communicate business performance effectively. Read More

Opportunities it opens

Databases & Wrangling

Develop skills in handling missing values, reshaping datasets, scaling features, and converting data types to prepare clean datasets ready for machine learning and AI model training. Read More

Opportunities it opens

Python for Data & AI Development

Learn the most in-demand programming language for AI and data science: Python. You’ll go from writing simple scripts to building reusable, efficient, and scalable code for real-world AI models.

With libraries like NumPy and Pandas, you’ll manipulate complex datasets and begin integrating early-stage AI libraries like TensorFlow or PyTorch.

You’ll also gain hands-on experience with GitHub, enabling you to work like a professional in collaborative, version-controlled environments.

What you'll learn:

Python Programming Fundamentals
Data Manipulation with NumPy & Pandas
Introduction to AI Libraries

Python Basics

Master Python fundamentals including variables, loops, functions, data structures, exception handling, and object-oriented programming concepts to build a solid foundation for data analysis and AI projects. Read More

Opportunities it opens

NumPy & Pandas

Work extensively with NumPy and Pandas for vectorized operations, DataFrame manipulation, filtering, merging, and reshaping datasets to transform raw data into structured formats suitable for analysis and modeling. Read More

Opportunities it opens

Data Visualization

Create visualizations using Matplotlib and Seaborn, including heatmaps, pairplots, and boxplots, to communicate insights effectively and produce clear, impactful graphical representations of complex datasets. Read More

Opportunities it opens

Visualization Librarie

Understand Git and version control workflows, including repositories, branching, and pull requests, to collaborate efficiently on projects and maintain organized, reproducible code for professional data science workflows. Read More

Opportunities it opens

AI Libraries Intro

Get introduced to AI libraries such as TensorFlow and PyTorch, including setting up environments and running basic machine learning models to gain hands-on exposure to modern AI tools. Read More

Opportunities it opens

Statistics, Probability & GenAI-Ready EDA

Understand the math that powers AI. This module teaches you to draw conclusions from data, identify hidden trends, and apply statistical techniques to validate assumptions.

You’ll work with Python libraries for statistical modeling and leverage AI tools like ChatGPT for guided Exploratory Data Analysis (EDA).

The goal is to help you build statistical intuition and fluency so you can prepare high-quality features for both ML and GenAI models.

What you'll learn:

Descriptive & Inferential Statistics
Probability & Hypothesis Testing
Exploratory Data Analysis

Descriptive Statistics

Apply descriptive statistics to summarize data using central tendency, variance, skewness, and kurtosis, developing the ability to interpret data distributions and generate actionable insights for business and research purposes. Read More

Opportunities it opens

Probability Theory

Learn probability theory, including conditional probability, Bayes’ theorem, and distributions such as normal, binomial, and Poisson, to build a strong foundation for modeling uncertainty and performing simulations in real-world analytics. Read More

Opportunities it opens

Hypothesis Testin

Perform hypothesis testing using Z-tests, t-tests, chi-square tests, ANOVA, and p-values, to validate assumptions and make evidence-based, statistically sound business decisions. Read More

Opportunities it opens

Exploratory Data Analysis

Conduct exploratory data analysis (EDA), including outlier detection, correlation analysis, missing value imputation, and feature engineering to uncover patterns and prepare datasets for predictive modeling. Read More

Opportunities it opens

AI-Assisted EDA

Leverage AI-assisted EDA tools, including ChatGPT, to automate data exploration, accelerating analysis and generating actionable insights quickly for enhanced decision-making. Read More

Opportunities it opens

Machine Learning & Model Building

Dive deep into machine learning with hands-on implementation of core algorithms for classification, regression, and clustering. You’ll not only learn the “how” but also the “why” behind popular techniques—understanding when to apply each and how to optimize their performance.

This module covers model tuning, evaluation, and ensemble learning with tools like Scikit-learn, XGBoost, and SHAP for model interpretability.

It prepares you to build robust, explainable ML systems ready for real-world deployment.

What you'll learn:

Supervised & Unsupervised Learning
Model Evaluation & Tuning
Ensemble Methods & Interpretability

Supervised Learning

Learn supervised learning techniques including linear regression, logistic regression, decision trees, and support vector machines to build predictive models that inform data-driven business strategies. Read More

Opportunities it opens

Unsupervised Learning

Explore unsupervised learning techniques such as K-means clustering, hierarchical clustering, and PCA dimensionality reduction to identify hidden structures in data and optimize analytical models. Read More

Opportunities it opens

Model Evaluation

Evaluate models using accuracy, precision, recall, F1 score, ROC curves, and confusion matrices to rigorously assess model performance and select the best predictive solutions. Read More

Opportunities it opens

Model Tuning

Optimize model performance using cross-validation, grid search, and hyperparameter tuning, to improve predictive accuracy and ensure machine learning models generalize effectively to new data. Read More

Opportunities it opens

Ensemble Techniques

Apply ensemble methods such as Random Forests, AdaBoost, and XGBoost, and interpret models using SHAP and LIME to build robust, high-performing, and explainable machine learning solutions. Read More

Opportunities it opens

Advanced AI, Deep Learning & Generative AI

You’ll build neural networks using TensorFlow/Keras, experiment with computer vision models, and work on real-world NLP tasks using BERT and GPT.

You’ll also explore Generative AI and prompt engineering with tools like OpenAI API and HuggingFace. This module makes you confident in applying AI to real industry challenges—from building smart assistants to automating content and insights.

What you'll learn:

Neural Networks & CNNs
NLP & Transformers
Generative AI Applications

Neural Networks & CNNs

Learn the fundamentals of neural networks and CNNs, including perceptrons, activation functions, backpropagation, and convolutional neural networks for image classification tasks in deep learning applications. Read More

Opportunities it opens

Natural Language Processing

Explore natural language processing (NLP) concepts including tokenization, stemming, TF-IDF, Word2Vec, and sentiment analysis to extract meaningful insights from text data for AI solutions. Read More

Opportunities it opens

Transformers & Time Series

Get introduced to transformers, large language models (LLMs) like BERT and GPT, and prompt engineering techniques to understand state-of-the-art AI approaches for text and sequence modeling. Read More

Opportunities it opens

Time Series Forecasting

Learn time series forecasting using ARIMA, exponential smoothing, and Facebook Prophet, to predict trends and business metrics accurately for planning and strategic decision-making. Read More

Opportunities it opens

Generative AI & AI Applications

Explore generative AI applications such as chatbots, text summarization, recommendation systems, and OpenAI API integration to build AI-driven solutions for real-world problems. Read More

Opportunities it opens

MLOps, AI Deployment & Career Readiness

Learn how to turn your AI models into real products by deploying them using modern tools like Flask, Streamlit, and AWS.

You’ll build complete end-to-end ML pipelines, explore automation using MLflow, and follow best practices for versioning and monitoring models.

This module also includes extensive career support—resume building, portfolio reviews, mock interviews, and real-world GenAI project presentation.

By the end, you’ll be job-ready with a deployable AI portfolio and full placement assistance.

What you'll learn:

Model Deployment & Hosting
Career Prep & Mock Interviews
Capstone Project

Model Deployment

Deploy models using Flask APIs, Streamlit dashboards, and cloud hosting on AWS or Heroku, making AI solutions production-ready, accessible, and user-friendly for end-users and stakeholders. Read More

Opportunities it opens

MLOps & Pipelines

Understand MLOps pipelines, including automation, model versioning, monitoring, and MLflow integration, to ensure scalable, maintainable, and reproducible machine learning projects in professional settings. Read More

Opportunities it opens

Resume, LinkedIn & Mock Interviews

Optimize your resume, LinkedIn profile, and interview preparation for data and AI roles, building a professional portfolio and practicing mock interviews to successfully secure analytics and AI career opportunities. Read More

Opportunities it opens

Capstone Project

Work on a capstone project solving a real-world AI problem, including end-to-end data processing, modeling, and presentation to demonstrate practical skills and deliver actionable business insights with AI solutions. Read More

Opportunities it opens

Data Science with AI Program

What roles can you target after completing the PG Data Science with AI Program?

AI-Powered Data Scientist

Design advanced AI models and turn complex data into actionable insights.

AI Product Analyst

Drive AI product decisions by analyzing user behavior and performance metrics.

Machine Learning Specialist

Develop and deploy cutting-edge ML solutions for real-world problems.

Applied AI Engineer

Implement and optimize AI-driven systems in production environments.

AI Solutions Architect

Architect end-to-end AI platforms and align solutions to business goals.

Data Strategy Consultant

Advise companies on leveraging AI for data-driven decision-making.

AI Business Analyst

Bridge business needs with AI-powered analytics and tools.

AI Research Associate

Support cutting-edge AI research and experiment with novel models.

Proven 85% Placement Success Across Batches

International Placements

Learners placed in top global companies across UAE, UK, Canada, and more.

10+ Batches 500+ Learners

Transformed 5,000+ Careers

Empowering professionals and freshers to level up through skill-based learning.

110+ Batches 5K+ Learners

100+ Hiring Partners

Trusted by industry-leading recruiters and top-tier startups.

110+ Batches 5K+ Learners

Avg. Salary Hike of 70%

Learners have reported significant hikes post program completion.

1000+ Placements

Expert Faculties

Learn from experienced mentors with proven industry expertise

Ranjitha

Expertise:

Data Science & AI Trainer

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Postgraduate in computer science Certified AI & Machine Learning Professional Specialized training in Python, Machine Learning, Deep Learning and Natural Language Processing Proficient in MLPPS, Model Deployment, and Cloud Integration (AWS)

Jeevitha D S

Expertise:

Power BI, Tableau, SQL, Excel

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Jeevitha is a detail-oriented Principal Instructor – Data Science & AI with 5 years of experience delivering hands-on training in Machine Learning, Deep Learning, MLOps, and Generative AI.

Ravi Singh

Expertise:

Deep Learning & Computer Vision

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Arjun is a researcher turned instructor who specializes in computer vision projects such as image recognition and object detection. His sessions are hands-on, blending theory with projects like self-driving car simulations.

Simran Kaur

Expertise:

Business Analytics & Decision Sciences

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Simran has worked with Fortune 500 companies to solve strategic problems using analytics. She teaches statistical modeling, hypothesis testing, and decision-making frameworks with real case studies.

Dr. Vivek Iyer

Expertise:

Natural Language Processing (NLP)

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With a strong research background, Dr. Vivek has published multiple papers in NLP. He mentors learners on text mining, sentiment analysis, and building conversational AI systems.

Meera Joshi

Expertise:

Data Strategy & Analytics Consulting

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Meera brings 12+ years of corporate consulting experience, teaching how to align analytics with business goals. She emphasizes data storytelling and translating insights into executive-level decisions.

Karan Malhotra

Expertise:

Artificial Intelligence & MLOps

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Karan is an AI solutions architect, training students in production-level ML systems. He focuses on deploying, monitoring, and scaling ML models, bridging the gap between ML and DevOps.

Rahul Mehta

Expertise:

Data Visualization & BI Analytics

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Rahul is a certified BI professional with 10 years in analytics consulting. He helps learners convert raw data into actionable dashboards and KPIs, making him a go-to mentor for visualization enthusiasts.

Real-World Industry Projects

Apply Skills in Real-World Industry Projects

Intelligent Loan Default Prediction

Use K-means clustering to segment customers based on demographics and transaction history. Present insights via dashboards for targeted campaign planning.

Tools Used: Python (Pandas, Scikit-learn, XGBoost), SHAP, Streamlit
Skills: Classification, Imbalanced Data Handling, Explainable AI
Sector: BFSI Sector

AI Chatbot for Insurance Claim Processing

Develop a conversational AI bot using a transformer-based model (BERT) to automate claim inquiry handling in the insurance domain. Train it on historical claim conversations and FAQs.

Tools Used: Python, HuggingFace Transformers, BERT, Flask, Streamlit
Skills: NLP, Transformers, Chatbot Development
Sector: Insurance / Fintech Sector

Smart Retail Inventory Forecasting

Use AI to predict product-level demand using sales history, holidays, and promotions. Deploy time series forecasting models like Prophet and LSTM to guide automated reordering decisions.

Tools Used: Python (Prophet, LSTM), Pandas, AWS S3
Skills: Time Series Forecasting, Deep Learning, Data Engineering
Sector: Retail / E-commerce Sector

Personalized Learning Path Recommendation Engine

Create an AI-based recommender system for an EdTech platform that suggests learning modules based on user behavior, interests, and performance.

Tools Used: Python (Scikit-learn, Surprise), Streamlit, SQ
Skills: Recommendation Systems, Collaborative Filtering, User Behavior Modeling
Sector: EdTech Sector

AI-Driven Medical Diagnosis Assistant

Develop a CNN model to classify X-ray images for disease detection (e.g., pneumonia). Combine with patient metadata to assist doctors in early diagnosis.

Tools Used: Python (TensorFlow/Keras), OpenCV, Matplotlib
Skills: Computer Vision, CNNs, Image Classification
Sector: Healthcare Sector

Real-Time Fraud Detection System

Build a real-time fraud detection pipeline using anomaly detection techniques on transaction data. Integrate streaming input and model monitoring using MLOps tools.

Tools Used: Python (Isolation Forest, Kafka), MLflow, AWS Lambda
Skills: Anomaly Detection, Streaming ML, MLOps
Sector: Fintech / Banking Sector

AI-Based Resume Screening System

Build a natural language processing model to automatically parse and rank resumes based on job descriptions. Use embeddings and similarity scoring to shortlist candidates.

Tools Used: Python (Spacy, BERT), Streamlit, Elasticsearch
Skills: NLP, Semantic Search, Document Classification
Sector: HR Tech / Recruitment Sector

Climate Impact Forecasting Using AI

Develop a predictive model to forecast long-term CO₂ emissions and temperature anomalies using global climate datasets. Leverage deep learning models to identify patterns and simulate future scenarios.

Tools Used: Python (TensorFlow, Pandas), Prophet, Matplotlib
Skills: Time Series Forecasting, Deep Learning, Data Visualization
Sector: Environment / Research Sector

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PG in Data Science with Al Program

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Join the Most In-Demand PG Data Science with AI Program

Industry-Relevant Curriculum

Placement Support

Live Online Classes

1:1 Mentorship

Career Support

Capstone Projects

Comprehensive Course Curriculum

Data Foundations & AI-Ready Business Tools

What you'll learn:

Excel + Google Sheets

SQL + Databases

Power BI/Tableau

Databases & Wrangling

Python for Data & AI Development

What you'll learn:

Python Basics

NumPy & Pandas

Data Visualization

Visualization Librarie

AI Libraries Intro

Statistics, Probability & GenAI-Ready EDA

What you'll learn:

Descriptive Statistics

Probability Theory

Hypothesis Testin

Exploratory Data Analysis

AI-Assisted EDA

Machine Learning & Model Building

What you'll learn: