Design Intelligent Systems using LLMs, RAG, and Agentic AI

You will learn the most in-demand languages and tools in this AI and Data Science Program, including:
● Python
● Google Colab
● VS Code
● OpenAI GPT-5
● Codex
● LangChain
● LangGraph
● Claude
Development environments, Generative AI frameworks for building autonomous agents, and evaluation techniques such as PCA and t-SNE are all part of the curriculum. Access to OpenAI API keys and Codex is provided by Great Learning for hands-on lab work.

The curriculum, expertly crafted by MIT faculty, is designed to equip learners with industry-relevant tools and techniques, enabling them to apply these skills in AI, Data Science, Machine Learning, and Generative AI to real-world problems. Here's what makes it unique:
● Crafted by MIT faculty for academic depth and industry relevance.
● Covers complete AI and Data Science concepts from foundational techniques to advanced Machine Learning models, deep learning, NLP, computer vision, and recommendation systems.
● Focus on Generative AI and Responsible AI to ensure you're equipped for the next wave of innovation.
● Provides hands-on experience through 4 industry-relevant projects and 10+ real-world case studies.
● Built for working professionals with a flexible format, recorded lectures, and live weekend mentorship.

The 16-week curriculum covers:
● Week 0 (Pre-Work): Data Science and AI foundations, introduction to Python and programming for AI.
● Week 1: AI landscape, key terminologies, evolution from classical ML to Generative AI and autonomous agents.
● Week 2: Large language models (LLMs), foundation models, prompt engineering techniques, and business applications.
● Week 3: AI-assisted Python coding, debugging AI-generated code, prompt design for problem-solving.
● Week 4: AI-assisted exploratory analysis, clustering (K-Means, K-Medoids, Gaussian Mixture Models), dimensionality reduction (PCA, t-SNE).
● Week 5: Project 1.
● Week 6: Predictive modeling with regression, supervised learning, model validation.
● Week 7: Building decision systems with AI, classification with decision trees, Random Forest, GenAI-based text classification.
● Week 8: AI-powered recommendation systems — rank-based, content-based, and collaborative filtering; building personalized engines with Generative AI.
● Week 9: Project 2.
● Week 10: Learning break.
● Week 11: Building context-aware AI workflows, transformer architecture, Retrieval-Augmented Generation (RAG), embeddings, and evaluating RAG pipelines.
● Week 12: Prompt optimization and evaluation, hallucination detection, LLM-as-a-Judge methodology.
● Week 13: Project 3.
● Week 14: Designing and building agentic AI workflows, reinforcement learning fundamentals, autonomous agent architecture.
● Week 15: Orchestrating multi-agent systems, dynamic work routing, adaptive RAG in multi-agent workflows.
● Week 16: Project 4.
Self-paced modules available throughout the program include Claude-based AI workflows, deep learning and neural networks, computer vision methods, ethical and responsible AI, and temporal data exploration.

Yes. Agentic AI is one of the three headline modules listed on the program brochure. Week 1 introduces the full AI landscape, including agentic systems. Week 14 covers designing and building agentic AI workflows, including reinforcement learning fundamentals, agent architecture (memory, planning, tool use), and building single-agent systems for business problems. Week 15 covers orchestrating multi-agent systems, including dynamic work routing, adaptive RAG, error handling, and performance evaluation. A self-paced module on Claude-based AI workflows extends this content further.

Prompt engineering is taught across multiple points in the program rather than as a single isolated topic. Week 2 covers prompt engineering techniques for accuracy and efficiency in the context of LLMs. Week 3 covers effective prompt design for Python problem-solving using AI coding assistants. Week 12 focuses on prompt optimization for model reliability, hallucination detection through consistency checks, and the LLM-as-a-Judge evaluation methodology. The self-paced Claude-based AI workflows module covers model selection and prompt engineering using Claude Chat.

The program includes four hands-on projects and 10+ real-world case studies across various industries
Sample case studies span supply chain (disruption response assistant using LLMs and AI agents), healthcare (clinical trial protocol feasibility review using advanced prompt engineering), retail (AI-assisted data cleaning using Python, customer segmentation, and recommendation systems), finance (loan application risk triage and banking customer service copilot using RAG and evaluation metrics), HR (employee HR policy assistant using RAG), and tech (autonomous SaaS support triage agent and competitive market intelligence platform using multi-agent systems). Sample projects include a retail customer segmentation model, an OTT content recommendation engine, a RAG-based legal contract review assistant, and a multi-agent financial research assistant.

Great Learning is the education delivery partner for the AI and Data Science Program. The curriculum is developed and taught by MIT IDSS faculty; Great Learning manages program delivery, learner support, and mentorship logistics.
Specifically, Great Learning provides access to experienced industry mentors, the Program Manager assigned to each learner, live mentored learning sessions, career support, including CV and LinkedIn profile reviews, and access to OpenAI API keys and Codex for hands-on lab work. The application review panel is also operated by Great Learning.

The AI and Data Science: Leveraging Responsible AI, Data and Statistics for Practical Impact is ideal for:
● Career Starters in AI and Data Science
● Early-Career Professionals in Data and Technology
● Tech Innovators and AI Practitioners
● Professionals Building Next-Generation AI Systems
Learners with high school-level mathematics and statistics will be well-prepared for this program. All learners can make the most of it with the structured support provided by Great Learning.

Yes. If you have a high school-level understanding of mathematics and statistics, you will be well-prepared for the AI and Data Science program. You do not need prior coding experience to enroll. We cover Python foundations in the pre-work and introduce AI-assisted Python coding in week three.
The program is designed in a way so that all learners can build skills progressively with the structured support provided by Great Learning. Business managers, product managers, and data analysts without deep engineering backgrounds are among the intended audience.

The AI and Data Science Program is structured for working professionals. Lectures are pre-recorded, so learners can watch them at any time. The weekly commitment of eight to twelve hours is spread across self-paced content, assignments, and live mentorship sessions. A dedicated Program Manager from Great Learning supports learners throughout the 16 weeks and can help with pacing.

No prior coding experience is required to enroll in the AI and Data Science Program. The program builds Python skills progressively, starting with AI-Assisted Python Coding in Week 3, which covers how AI coding assistants generate Python code, debugging, prompt design for problem-solving, and security considerations. Learners with high school-level mathematics and statistics are well-prepared to start.

The registration process for this AI and Data Science program is as follows:
● Step-1: Register by completing the online application form.
● Step-2: A panel from Great Learning will review your application based on your academic performance, work experience, and motivation to assess your fit for the program.
● Step-3: After a final review, you will receive an offer for a seat in the upcoming cohort of the program.

Upon successful completion of AI and Data Science: Leveraging Responsible AI, Data and Statistics for Practical Impact, you will receive a Certificate of Completion from MIT IDSS and also earn 8.0 Continuing Education Units (CEUs), validating your ability to apply AI and Data Science for impact. The certificate is a formal recognition of the expertise developed throughout the program, not a degree or academic credit.

Continuing Education Units (CEUs) are a standardized measure of participation in non-degree professional development programs. One CEU represents ten contact hours of participation in an organized continuing education experience. The AI and Data Science Program awards 8.0 CEUs upon successful completion, recognizing the learner's commitment to structured, applied learning in AI and Data Science.

Each learner is assigned a dedicated Program Manager from Great Learning who serves as the primary point of contact throughout the 16-week program. The Program Manager monitors learner progress, provides timely assistance, and supports learners in staying on track toward their learning objectives. The structured schedule includes a dedicated learning break at Week 10, designed to allow learners to consolidate concepts and complete pending work before advancing.

The total program fee is USD 2500.

No. All required learning materials are provided online through the Learning Management System (LMS). Because these fields continue to evolve, you will also receive a list of recommended books and resources for optional, in-depth exploration.

Applicants can pay the program fee through Bank Transfer and Credit/Debit Cards. They can also pay in easy installments using PayPal credit options and get interest-free payments for up to 6 months (Note that these services are subject to credit approval by PayPal). [For further details, please get in touch with us at ai-ds.mit.idss@mygreatlearning.com.

Please note that submitting the registration fee constitutes enrollment in the program, and the cancellation penalties outlined below will be applied. If you are unable to attend your program, please review our dropout and refund policies below:
● Dropout requests received within 7 days of enrollment and more than 42 days prior to the commencement of the program will incur no fee. Any payment received will be refunded in full.
● Dropout requests received more than 42 days prior to the program but more than 7 days after the acceptance are subject to a cancellation fee of USD 250.
● Dropout requests received 22-41 days prior to the commencement of the program are subject to a cancellation fee equal to 50% of the program fee.
● Any dropout requests received fewer than 22 days prior to the commencement of the program are subject to a cancellation fee equal to 100% of the program fee.
● No refund will be made to those who do not engage in the program or leave before completing a program for which they have registered.

We accept corporate sponsorships and can assist you with the process. [For more information, please write to us at ai-ds.mit.idss@mygreatlearning.com .

To become a Data Scientist, you need to have a blend of technical expertise, analytical thinking, and real-world problem-solving skills. If you have a strong academic background, that will help you learn the concepts related to the field.
Here’s how you can start:
● Build a strong foundation in mathematics, statistics, and programming.
● Gain hands-on experience with tools used in the industry.
● Develop applied knowledge through projects that simulate real-world data challenges.
● Strengthen your understanding of Generative AI, AI, and Machine Learning techniques.

Yes, the future of Data Science and AI is highly promising. As organizations become more AI and data-driven, the demand for professionals who can turn raw data into strategic insights using data analytics in Artificial Intelligence is growing. Here’s why:
● Growing demand in Business: Companies are leveraging AI and Data Science to reduce costs, improve marketing effectiveness, launch better products, and tap into new markets.
● Data-driven strategy making: Gartner has forecasted that many corporate strategies will highlight data and analytics as essential business competencies.
● Expanding applications: From healthcare and finance to retail and tech, Data Science is shaping decision-making and driving innovation across industries.
● Career longevity: With data at the core of digital transformation, professionals with expertise in AI, ML, and Data Science are well-positioned for long-term career growth.
As industries increasingly rely on data to drive innovation and growth, the need for skilled Data Science professionals will only continue to rise.
Gaining expertise in Artificial Intelligence, Machine Learning, and Data Science is a smart investment for you to future-proof your career.

Data Science is a field that uses statistical and analytical methods to extract meaningful insights from data, insights that inform decisions, uncover patterns, and drive measurable business outcomes. Machine Learning is a core set of techniques within Data Science that enables computers to learn from data and improve their performance without being explicitly programmed for every task.
In practice, Data Science defines the problem and prepares the data; Machine Learning provides the methods to build predictive models from it. Together, they form the technical foundation for most modern AI applications i.e. from recommendation engines and fraud detection to autonomous agents and large language models.

Organizations across industries increasingly rely on advanced Data Science and Machine Learning to drive strategic decision-making and improve business outcomes. Here’s how Data Science and Machine Learning create impact.
Inform stronger business strategies: Leading companies use data-driven insights and Machine Learning models to design effective business plans, optimize operations, and accelerate growth.
Deliver solutions that meet customer needs: Organizations with clear data strategies can anticipate market trends, innovate faster, and build products that offer greater value to end users.
Reduce operational costs: For small and medium-sized enterprises, AI, Data Science, and Machine Learning enable more efficient processes and cost-effective solutions, helping them stay competitive despite limited resources.
This combination of strategic insight, customer-centric innovation, and operational efficiency is why Data Science and Machine Learning have become essential capabilities for modern businesses.

According to BuiltIn.com, the average salary for an AI professional in the United States is $184,757. As per Indeed.com, For a Data Scientist, it is $130,388, and for a Machine Learning Specialist, it is $189,067.

Data science is a universal requirement in the modern economy, with specific roles including:
● Finance: Loan application risk triage and customer segmentation for retail banks.
● Healthcare: Predicting hospital length of stay (LOS) and clinical trial protocol feasibility reviews.
● Retail & E-commerce: Developing recommendation engines and identifying quick-commerce order volume drivers.
● Tech: Building autonomous SaaS support triage agents and multi-agent systems.

Yes. There is a rising demand for Product and Business Managers who can evaluate AI solutions, understand Large Language Models (LLMs), and oversee decision systems. These roles focus on the strategic judgment and human intuition required to implement AI successfully, rather than just writing code.

To stay competitive, professionals should be proficient in:
● Development Environments: VS Code and Google Colab.
● Generative AI Frameworks: LangChain and LangGraph for building autonomous agents.
● Visualization & Analysis: Techniques such as PCA and t-SNE for making sense of unstructured data.

Agentic AI refers to AI systems that do not just respond to a single prompt. They plan a sequence of steps, use external tools, execute actions, adapt based on results, and complete tasks autonomously. The distinction from conventional AI models is that agentic systems operate over multiple steps with memory, planning, and tool use built in. The AI and Data Science Program devotes two full weeks (Weeks 14 and 15) to designing agentic AI workflows and orchestrating multi-agent systems, reflecting the practical reality that most enterprise AI deployments in 2026 involve some form of autonomous or semi-autonomous agent pipeline.

Responsible AI refers to the practice of building AI systems that are fair, transparent, and accountable, systems whose decisions can be explained, audited, and corrected. The AI and Data Science Program includes a dedicated self-paced module on Ethical and Responsible AI, covering the AI lifecycle, bias, causality, privacy, and interdependency in AI systems.