PG Data Science With AI & ML Job Gurantee Program
Master Data Science with AI & ML from industry experts — gain in-demand skills and real-world experience. Get a guaranteed job placement or your money back.
Program Duration
7 Months
Learning Format
Online Live
Placement Assistance
100% Guaranteed
Trusted By Leading Companies
No Content
Join the Most In-Demand PG Data Science with AI & ML Job Guarantee Program
Master the real-world application of Data Science, Artificial Intelligence, and Machine Learning to build smart, scalable solutions that drive real business impact. This 100% Job Guarantee program is crafted for both fresh graduates and working professionals looking to break into high-growth tech careers.
Get hands-on training with AI tools, ML algorithms, Python, SQL, and deep learning frameworks, while working on real industry projects. Whether you aim to become a Data Scientist, AI Engineer, or ML Specialist, this program equips you with the skills, mentorship, and placement support to achieve your career goals—risk-free.
Industry-Relevant Curriculum
Comprehensive curriculum covering Python, Machine Learning, Deep Learning, NLP, and Big Data technologies.
Job Guarantee
Get placed in a data science role within 6 months of graduation or receive a full refund of your program fee.
Live Online Classes
Learn from top industry experts through interactive live sessions with real-time doubt resolution.
1:1 Mentorship
Personalized guidance from industry mentors to help you navigate your learning journey effectively.
Career Support
Resume building, interview preparation, and exclusive access to job opportunities with top companies.
Capstone Projects
Work on real-world projects to build a strong portfolio that showcases your data science expertise.
Comprehensive Course Curriculum
Our industry-aligned curriculum is designed by experts to help you master the most in-demand data
science, AI & ML skills and prepare you for a successful career.
Foundations of Data & Python
Master Python, the go-to language for data science. Learn how to write efficient code, automate tasks, and perform basic analysis with built-in libraries. You’ll also develop confidence in handling structured and unstructured datasets.
Gain hands-on experience using tools like Pandas and NumPy to clean, organize, and explore data. This module builds the essential groundwork for all upcoming analytics and ML work.
What you'll learn:
- Python programming fundamentals
- Descriptive and inferential statistics
- Data manipulation with NumPy and Pandas
Course Content
Python Programming Fundamentals
Learn syntax, variables, operators, and how to write modular, readable code.
Data Structures, Functions, and Loop
Work with lists, dictionaries, loops, and functions to solve real-world problems.
Exploratory Data Analysis (EDA)
Analyze datasets to discover trends and patterns using visual and statistical methods.
Git & GitHub Version Control
Collaborate effectively using Git basics and push projects to GitHub repositories.
Statistics & SQL for Analytics
Statistical thinking is the backbone of data science. In this module, you’ll learn to summarize, interpret, and draw conclusions from data. You’ll explore descriptive statistics, probability, and hypothesis testing.
At the same time, you’ll learn SQL — the language of databases — and use it to extract insights from large datasets in business scenarios.
What you'll learn:
- Python programming fundamentals
- Descriptive and inferential statistics
- Data manipulation with NumPy and Pandas
Course Content
Descriptive & Inferential Statistics
Understand mean, variance, confidence intervals, and distributions.
Probability & Hypothesis Testing
Test assumptions with z-tests, t-tests, and understand statistical significance.
SQL Joins & Aggregates
Query relational databases using filters, joins, grouping, and subqueries.
Business Case Analytics
Apply SQL to answer business questions and generate insights from raw data.
Machine Learning
Learn the core concepts and algorithms that power recommendation engines, fraud detection, and intelligent decision systems. This module introduces both supervised and unsupervised learning.
You’ll build machine learning models, evaluate performance, and apply them to structured data for predictive analysis.
What you'll learn:
- Python programming fundamentals
- Descriptive and inferential statistics
- Data manipulation with NumPy and Pandas
Course Content
Supervised & Unsupervised Learning
Train models like linear regression, decision trees, and clustering methods.
Classification & Regression
Predict categories and continuous values using real-world datasets.
Model Tuning & Evaluation
Use accuracy, F1-score, precision-recall to assess model quality.
ML Project Execution
Design end-to-end machine learning pipelines and deploy solutions in Jupyter.
Deep Learning & AI
Step into the world of artificial intelligence through hands-on experience with neural networks. Learn how to process visual and textual data and build intelligent systems.
This module includes both theoretical
understanding and practical use of popular deep learning frameworks.
What you'll learn:
- Python programming fundamentals
- Descriptive and inferential statistics
- Data manipulation with NumPy and Pandas
Course Content
Neural Networks with TensorFlow/Keras
Build feedforward networks for classification tasks.
CNNs for Image Recognition
Analyze images using convolution layers and activation functions.
RNNs & LSTMs for Time-Series
Handle sequential data like text or stock prices using deep learning.
NLP for Text Analysis
Build models that classify sentiment, extract keywords, or summarize content.
BI Tools & Big Data
Learn how to visualize insights, communicate results, and work with high-volume datasets. This module connects your data skills with business decision-making.
Understand how dashboards drive strategic decisions and explore the basics of big data architecture.
What you'll learn:
- Python programming fundamentals
- Descriptive and inferential statistics
- Data manipulation with NumPy and Pandas
Course Content
Power BI & Tableau
Create interactive dashboards and data stories for business stakeholders.
Data Aggregation & KPIs
Calculate metrics and present insights using drill-downs and filters.
Big Data Concepts & Spark
Understand distributed computing, Hadoop basics, and introduction to PySpark.
Real-World Case Studies
Analyze messy, real-life datasets from e-commerce and banking sectors.
Capstone Project & Career Readiness
Apply everything you’ve learned to build a full-scale AI/ML project. Choose your domain, gather data, model outcomes, and present insights just like in the real world.
You’ll also receive personalized support to polish your profile and prepare for interviews with hiring managers.
What you'll learn:
- Python programming fundamentals
- Descriptive and inferential statistics
- Data manipulation with NumPy and Pandas
Course Content
Capstone AI/ML Project
Solve an industry-relevant problem end-to-end and showcase it on GitHub.
Resume & LinkedIn Optimization
Learn how to position yourself for data science roles effectively.
Mock Interviews & Case Rounds
Practice behavioral and technical questions with expert mentors.
Final Demo Day
Present your project to mentors and potential recruiters in a live session.
🚀 Your Data Science Journey
Start Here
Orientation & Assessment
Foundations
Python, SQL, Excel, Tableau
ML Bootcamp
Modeling & Visualization
Assessment
Track Selection
Choose Track
AI/DL or Analytics & BI
Model Deployment
Flask, Streamlit, AWS
Capstone
Project & Hackathon
Career Prep
Resume, LinkedIn, Referrals
Final Outcome
Job in Data Science
🚀 Your Data Science Journey
🏁 Start Here
Orientation & Skill Assessment
🧑💻 Programming & Data Foundations
Python, Excel, SQL, Tableau & Power BI
📊 ML & Data Viz Bootcamp
Model building with Pandas, Seaborn, Scikit-learn
🧭 Pre-Placement Assessment
Identify your ideal AI specialization track
🎯 Choose Your Track
Track 1: AI & Deep Learning
Track 2: Data Analytics & BI
🚀 AI Model Deployment
Flask, Streamlit, AWS, MLflow & monitoring
🛠 Capstone Project & Hackathon
Final project & sprint presentation
💼 Career Launch
Resume, LinkedIn, mock interviews, referrals
🏆 Final Outcome
Land a job in Data Science
👩🏫 Meet Your Data Science Mentors
Where Can a PG in Data Science with AI & ML Take You?
Data Scientist
Use AI and ML to analyze complex datasets and create intelligent solutions.
ML Engineer
Build, deploy, and monitor machine learning pipelines in production environments.
AI Engineer
Develop smart systems using neural networks, LLMs, and natural language processing.
Data Analyst
Draw business insights using visual dashboards and statistical data analysis.
Data Engineer
Design robust data pipelines and manage cloud data architecture.
BI Developer
Create interactive reports and dashboards to drive business decisions.
Big Data Specialist
Handle massive datasets using Hadoop, Spark, and distributed computing.
AI Researcher
Work on advanced AI models and contribute to innovative research in deep learning.
Product Data Analyst
Use data insights to shape product strategy and improve user experiences.
Master 25+ Tools & Technologies in AI, ML & Data Science
Work Hands-On With the Most Powerful Tools in Data Science
Python
NumPy
Pandas
Jupyter
TensorFlow
Scikit-Learn
SQL
Matplotlib
Seaborn
Tableau
R
Linux
Apply Skills in Real-World Industry Projects
Customer Segmentation for Personalized Banking
Use K-means clustering to segment customers based on demographics and transaction history. Present insights via dashboards for targeted campaign planning.
Tools: SQL, Python (Pandas, Scikit-learn), Tableau Skills: Clustering, EDA, Dashboarding Sector: BFSIDemand Forecasting for Online Retail
Analyze historical sales data and build time series models to predict future demand and improve stock planning and logistics.
Tools: Python (Pandas, Prophet), Excel Skills: Time Series Analysis, Forecasting, Data Cleaning Sector: Retail / E-commerceChurn Prediction for Telecom
Develop a classification model to predict which customers are likely to leave the telecom service and implement retention strategies.
Tools: Python (Scikit-learn, XGBoost), Power BI Skills: Classification, Imbalanced Data Handling, Model Evaluation Sector: TelecomEmployee Attrition Analysis in HR
Identify key factors influencing employee exits using logistic regression and decision trees to help HR reduce attrition.
Tools: Python (Logistic Regression, DecisionTree), Excel Skills: Feature Engineering, Regression, Analytics Reporting Sector: HR AnalyticsReal-Time Credit Card Fraud Detection
Use anomaly detection and classification models to identify fraudulent transactions in real time and minimize financial risk.
Tools: Python (Scikit-learn, PyCaret), Streamlit Skills: Anomaly Detection, Classification, AUC Optimization Sector: FinTechMovie Recommendation System
Build a content-based and collaborative filtering model to recommend movies to users based on their preferences and history.
Tools: Python (Surprise, Pandas), Streamlit Skills: Recommendation Systems, Matrix Factorization, Collaborative Filtering Sector: Media & EntertainmentCustomer Sentiment Analysis from Reviews
Extract insights from product reviews using NLP techniques to classify sentiments and guide product and marketing decisions.
Tools: Python (NLTK, TextBlob), Power BI Skills: Text Preprocessing, Sentiment Analysis, Data Visualization Sector: E-commerceImage Classification for Product Tagging
Develop a convolutional neural network to automatically classify product images into categories for inventory tagging.
Tools: Python (TensorFlow, Keras, OpenCV) Skills: Computer Vision, CNN, Image Augmentation Sector: Retail / AI AutomationOur Hiring Partners
Career Services to Get You Hired
Dedicated career support to transform your skills into a successful data science career.
🎓 Earn Your Industry-Endorsed PG Certification
Hear from Our Alumni
Real journeys of transformation through our Data Science with AI & ML Program.
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FAQs: PG Data Science with AI & ML Job Guarantee Program
Q. Who is this program for?
Anyone with a graduation degree and interest in data science, machine learning, or AI can apply. No prior experience required.
Q. What is the duration of the program?
Q. Do I need AI/ML background?
Q. What tools & frameworks will I learn?
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