Integrated MS+PGP Program in Data Science & AI

Elevate your career with dual, industry-recognized certifications in Artificial Intelligence

  • Earn dual certifications within 12 months instead of standard 24 months 
  • Fast track your learning through RPL (Recognition of Prior Learning  
  • Dual Certification Phase 1: Earn PGP in Generative AI and ML  in 6 months
  • Dual Certification Phase 2: Accelerated MS in Data Science from Birchwood University 
  • Learn through a perfect blend of live online and asynchronous learning modules
  • Work on real-world AI projects and build a strong industry-ready portfolio
  • Get both programs at a special combo price with our Learn More, Pay Less initiative
2097 Learners 4.9 102 Ratings

Earn two career-defining certifications within 12 months through our RPL enabled learning pathway. Begin with a 6-month PGP in Generative AI and ML, then progress to an accelerated MS in Data Science from Birchwood University. Learn through live and self-paced modules, work on real projects, and join us now to get a special combo price.

Program Highlights

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Gain advanced expertise in Generative AI, Machine Learning, and Data Science to tackle real-world business challenges.

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Build a strong professional portfolio through capstone projects and applied AI solutions.

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Open doors to AI, ML, and Data Science roles across industries and geographies, backed by dual university credentials.

Learning Track

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Market Opportunities

$279 B

MARKET SIZE
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Global artificial intelligence market projected to grow from about US$279 billion in 2024 to roughly US$3.5 trillion by 2033

- Grand View Research

34%

Job Growth
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Data science jobs in the U.S. are expected to grow 34% by 2034, outpacing the other job roles

- Bureau of Labor Statistics

$181 K

Average Salary
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Average base salary for AI Engineers in the US is around US$181,000 per year in 2025

- builtin.com

PHASE 1 PROGRAM STRUCTURE

Post Graduate Program in Gen AI and ML

  • Module 1: Python Programming Fundamentals
  • Module 2: Data Structures, Control Flow, and File Operations
  • Module 3: Functions, Object-Oriented Programming, and Exception Handling
  • Module 4: Data Analysis and Visualization
  • End Course Test

  • Module 1 : Machine Learning - Data Pre-processing and Feature Engineering
  • Module 2 : Supervised Learning - Regression
  • Module 3 : Supervised Learning - Classification
  • Module 4 : Unsupervised Learning
  • Module 5 : Clustering Algorithms and Dimensionality Reduction
  • Module 6 : Ensemble Learning
  • Module 7 : Time Series Analysis and Reinforcement Learning (Self-Paced)
  • Module 8 : Recommender Systems (Self-Paced)
  • End Course Test

  • Module 1 : Neural Networks and Deep Learning Foundation
  • Module 2 : Tuning and Optimizing Deep Neural Networks
  • Module 3 : Convolutional Neural Networks - I
  • Module 4 : Convolutional Neural Networks - II
  • Module 5 : Recurrent Neural Networks
  • Module 6 : Long Short Term Memory (LSTM) Networks
  • End Course Test

  • Module 1 : Introduction to NLP
  • Module 2 : Text Processing and Feature Engineering
  • Module 3 : Named Entity Recognition (NER) & Parsing
  • Module 4 : Tokenization and Text Encoding
  • Module 5 : Sentiment Analysis Essentials
  • Module 6 : Advanced Sentiment Analysis
  • Module 7 : Neural Language Models
  • Module 8 : Machine Translation
  • Module 9 : Speech and Multimodal NLP
  • Module 10 : Building Chatbots
  • End Course Unit

  • Module 1 : Introduction to Generative AI
  • Module 2 : Autoencoders and GANs
  • Module 3 : Transformers and Attention Mechanism
  • Module 4 : Small Language Models
  • Module 5 : Prompt Engineering Essentials
  • Module 6 : Advanced Prompting Strategies
  • End Course Test

  • Module 1 : Introduction to Large Language Models
  • Module 2 : Open Source LLMs and Model Variants
  • Module 3 : Vector Databases
  • Module 4 : Retrieval Augmented Generation (RAG) Techniques
  • Module 5 : LLM Frameworks and Development
  • Module 6 : LLM Application Development
  • Module 7 : Fine-Tuning and Model Adaptation
  • Module 8 : Generative AI Application Deployment
  • Module 9 : Cloud-based Generative AI (Self-Paced)

  • Module 1 : Agentic AI Essentials
  • Module 2 : Agentic AI: Architectures and Design Patterns
  • Module 3 : Working with LangChain and LCEL
  • Module 4 : Building AI Agents with LangGraph
  • Module 5 : Implementing Agentic RAG
  • Module 6 : Developing AI Agents with Phidata
  • Module 7 : Multi Agent Systems with LangGraph and CrewAI
  • Module 8 : Advanced Agent Development with Autogen
  • Module 9 : AI Agent Observability and AgentOPs (Self-Paced)
  • Module 10 : Building AI Agents with No/Low- Code Tools (Self-Paced)

  • Module 1 : AI Pair Programming for Developers
  • Module 2 : AI-Driven Development and Testing
  • Module 3 : Mastering Amazon Q for Developers
  • Module 4 : Generative AI for Developers with Vertex AI

  • Module 1 : Fundamentals of MLOps and LLMOps
  • Module 2 : Infrastructure, Tooling, and Open Source LLMOps
  • Module 3 : Data Management, Model Training, and Best Practices
  • Module 4 : Continuous Integration, Deployment, and Scaling LLMs
  • Module 5 : Monitoring, Governance, and Ethical AI
  • Module 6 : LLM in Production

  • Project

PHASE 2 PROGRAM STRUCTURE

MS in Data Science by Birchwood

Gain insight into the Python Programming language with this introductory course. An essential programming language for data analysis, Python, Programming is a fundamental key to becoming a successful Data Science professional. In this course, you will learn how to write python code, learn about Python's data structures, and create your functions. After the completion of this course, you can represent yourself as an ideal candidate for python Developer.

In this course, students will learn how to manage the Data Effectively using My SQL Work Bench. Students will come to know how to Apply Certain Joins techniques, how to manipulate the data. Will be able comfortably design SQL queries to add data to the database, will be familiar with editing, deleting data from the database, and will be able to describe and develop Relational Algebra and Relational Calculus queries.

Gain insight into the R Programming language with this introductory course. An essential programming language for data analysis, R Programming is a fundamental key to becoming a successful Data Science professional. This course will teach you how to write R code, learn about R's data structures, and create your functions. After the completion of this course, you will be fully able to begin your first data analysis.

This course includes the necessary exploratory techniques for summarizing data. These techniques are typically implemented before formal modeling begins and can help in informing the development of numerous complex statistical models. Exploratory techniques are also essential for eliminating or sharpening potential hypotheses about the world that the data can address. In this course, we will study plotting systems and the basic principles of constructing data graphics. We will also cover some of the standard multivariate statistical techniques used to visualize high-dimensional data.

Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques, including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for your role with advanced Machine Learning knowledge.

Implementing models such as support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering, and more in Flask, sending and receiving the requests from deployed machine learning models, building machine learning model APls, and deploy models into the cloud, design testable, version-controlled, and duplicate production code for model deployment.

The Artificial Intelligence course will expand your technical function and become an expert in Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Al concepts and techniques, including, Deep Learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for your role with advanced Al knowledge. Also, it will help in understanding Convolution Neural Networks Convolution, Pooling and Generative Networks Adversarial Networks, and these skills will enable candidates to seek their career in their desired companies.

Data Visualization using Tableau - Tableau Course will help you master the various aspects of the program and gain skills such as building visualization, organizing data, and designing dashboards. You will also learn concepts of statistics, mapping, and data connection. It is an essential asset to those wishing to succeed in Data Science. After learning the tableau tool, one must be able to display and analyze data. Also, it enables the users to create various reports and presentations about data.
Data Visualization using Power Bi - In this course, you will master the various aspects of the program and gain skills such as building visualization, organizing data, and designing dashboards utilizing data visualization using Power Bi. You will also learn concepts of mapping, and data connection; users will be able to provide clear and actionable insights in less than a minute. It also enables the candidate to import data from multiple sources. And learn how to transform the data. It is an essential asset to those wishing to succeed in Data Science.

The capstone project will allow you to implement the skills you learned throughout this program. Through dedicated mentoring sessions, you'll learn how to solve a real-world, industry-aligned Data Science problem, from data processing and model building to reporting your business results and insights. The project is the final step in the learning path and will enable you to showcase your expertise in Data Science to future employers.

Program Fees

Post Graduate Program in Gen AI and ML
$2,599
MS in Data Science by Birchwood
$11,850
Total Price: $14,449
Total Price: $14,449
*Limited Time Offer

Payment Options

The Indian pricing is inclusive of GST for all learners.
The combo price offer is valid only with full upfront payment for a limited period of time.
Finance (EMI) option is available for learners who prefers flexible payment options.

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Frequently Asked Questions (FAQs)

  • The Integrated MS + PGP in AI combines two prestigious qualifications, the Master of Science in Data Science by Birchwood University and the Post Graduate Programme in Generative AI and ML (PGP in Generative AI & ML) by Edureka.

    This accelerated programme builds a strong foundation in data science while developing advanced, industry-ready expertise in AI and machine learning. The integrated curriculum offers a complete understanding of the data-driven technologies shaping the future of business and innovation.

    A key differentiator of this dual pathway is the Recognition of Prior Learning (RPL) model, which allows learners to complete the entire programme in just 12 months instead of the usual 24, graduating with two recognized credentials.

    By the end of the journey, learners gain hands-on experience across data science, artificial intelligence, and machine learning, empowering them to fast-track their careers and succeed in today’s competitive, technology-led world.

  • Recognition of Prior Learning (RPL) formally acknowledges previously acquired knowledge, skills, and professional experience, regardless of where or how that learning was obtained. This recognition may provide credit, module exemptions, or direct assessment eligibility, eliminating the need to repeat content already mastered.

    In the integrated program, RPL supports a streamlined two-stage learning pathway that combines the PGP in Generative AI and Machine Learning with the Master of Science in Data Science. This structure enables completion of both qualifications within an accelerated 12-month track, instead of the conventional 24 months.

  • Applicants should have a bachelor’s degree in a relevant field (such as Computer Science, IT, Engineering, Mathematics, or Statistics). Prior experience in analytics, data, or programming is beneficial but not mandatory.

  • The program follows a blended learning model, combining live online sessions, self-paced learning, and hands-on industry projects.

  • Learners are not required to complete two separate projects. The Capstone Project completed as part of the PGP program can also be used to earn credentials in the MS program.

  • The PGP and MS programs operate as independent academic tracks. If a learner qualifies for the MS program but does not meet the requirements of the PGP, they will receive only the MS degree. Learners must successfully complete the graded components of each program individually to earn the respective degree or certificate.

  • No. Both programs are designed specifically for working professionals and therefore do not include placement assistance or career support services.

  • No. The program does not include any form of campus immersion.

  • Learners may choose to opt out of the program and request a refund any time before the commencement of the cohort by sending an email. Refunds will be processed after deducting processing charges of USD 150.

    No refund requests will be accepted or processed once the cohort has commenced, and any amount paid will be forfeited.

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