PGP in Business Management & AI

HEPro PGP in Business Management and AI

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INR 50,000 Only for PGP in Business and Management and AI

Program Overview

This 15-month program is structured in four distinct phases totaling 600+ hours of learning:

Foundation Phase (Months 1-3): Business fundamentals, Python programming, and market analysis
Core Management Phase (Months 4-8): Financial, operations, marketing, HR, and strategic management with industry internship
AI Integration Phase (Months 9-12): Machine learning, data analytics, generative AI, and cloud certifications
Capstone Phase (Months 13-15): Industry-integrated capstone project with real business partnership and executive showcase

15-Month Business Management & AI Program Timeline

The program has been designed with 40 critical skills to develop, with 17 of them identified as essential for immediate industry readiness. By program completion, participants will achieve advanced proficiency in 25 skills, intermediate mastery in 13 skills, and expert-level capability in 2 specialized areas (financial modeling and data visualization).

Program Overview & Architecture

A comprehensive, project and assignment-based online program structure that uniquely integrates business management fundamentals with cutting-edge AI and machine learning capabilities, fully aligned with industry needs and delivered through real-world projects.

Key Differentiator

1. 100% Project-Based Learning

Each month features a major capstone project directly tied to business problems:

Month 4: Financial Analysis & Business Valuation Model
Month 6: Full Digital Marketing Campaign Execution
Month 9: ML Model Development for Predictive Analytics
Month 11: Custom Generative AI Application
Months 13-15: Real company capstone with executive partnership

2. Industry Integration at Every Stage

Real Datasets: Access to anonymized data from 50+ partner companies across BFSI, Tech, Retail, and Healthcare
8-Week Virtual Internship: Starting Month 8, working on actual business problems with dedicated mentor
Industry Mentorship: 15 senior professionals providing guidance across all phases
Executive Advisory Board: For capstone projects, providing strategic oversight and industry validation

3. Skill Development Progression

The program strategically introduces skills in sequence:

Months 1-3: Foundation (1 skill by Month 1 → 15 skills by Month 3)
Months 4-8: Core Business (15 skills → 23 skills added)
Months 9-12: AI/ML (23 skills → 39 skills, including 3 certifications)
Months 13-15: Integration (40 skills, portfolio completion, job readiness)

Phase 1: Foundation (Months 1-3)

Phase 1: Foundation (Months 1–3) focuses on building core business and AI readiness skills.

In Month 1, learners work on Business & AI fundamentals, completing a Business Bootcamp and analysing three AI case studies using tools like Python, ChatGPT, and Jupyter.

In Month 2, the emphasis shifts to Python and data structures, where participants undertake a “Programming for Analytics” project with five coding challenges, working hands-on with Python, Pandas, NumPy, and GitHub.

In Month 3, learners move into business planning, developing a full Business Model Design with market analysis and competitive positioning, supported by tools such as Tableau, Excel, Miro, and Figma.

Phase 2: Core Management (Months 4-8)

Phase 2 (Months 4–8) deepens core business management skills through monthly, industry-linked projects.

In Month 4, the focus is on Financial Management, where learners build financial models, perform DCF valuation, and create budget forecasts using real financial data provided by a fintech partner.

In Month 5, the Operations module centers on supply chain optimization and process improvement, driven by actual retailer supply chain datasets for realistic analysis.

Month 6 is dedicated to Marketing, with a major project on digital campaign design, analytics dashboards, and A/B testing executed in collaboration with a marketing agency.

Month 7 moves into HR Management, where participants design talent strategies, refine recruitment processes, and build HR analytics dashboards under the mentorship of an HR director. Finally, in

Month 8, the focus shifts to Strategic Management, culminating in a 3–5 year business strategy project that directly ties into a real company strategy engagement and marks the formal beginning of the internship experience.

Phase 3: AI Integration (Months 9-12)

Phase 3 (Months 9–12) focuses on deep AI integration with clear certification-oriented pathways.

In Month 9, the focus is Machine Learning, where learners build an end‑to‑end ML model and participate in a Kaggle-style competition, framed as real company ML consulting work to simulate client engagements.

Month 10 emphasizes Data Analytics, with a major project on BI dashboard creation, forecasting, and statistical analysis in collaboration with an analytics partner company.

In Month 11, the focus shifts to Generative AI, where participants develop a custom chatbot or RAG system and implement NLP solutions anchored to a real business use case and a GenAI workshop experience.

Month 12 is dedicated to Cloud Mastery, culminating in cloud deployment of models, setting up an MLOps pipeline, and sitting for a cloud certification exam through structured training paths for AWS, Azure, or GCP.

Phase 4: Capstone (Months 13-15)

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Phase 4 (Months 13–15) is the capstone phase, where learners turn everything they have built into a complete, industry‑ready solution.

In Month 13 (Research & Design), the focus is on scoping and designing the project: learners produce a 20–30 page proposal detailing the business problem, research, and technical architecture, working closely with an executive sponsor and an industry advisory board.

In Month 14 (Implementation), they build the fully developed solution and complete codebase, supported by weekly industry mentoring and real‑time feedback from stakeholders. Finally, in Month 15 (Showcase & Finish), they deliver a polished final presentation and a 50–75 page report during an industry showcase event, creating strong job prospects and networking opportunities with hiring managers and potential partners.

Critical Implementation Features

Tools & Technology Stack:

Programming: Python, SQL, Git/GitHub, JavaScript basics
Data & Analytics: Pandas, NumPy, Tableau, Power BI, Google Analytics, Plotly
Machine Learning: Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM, Hugging Face
Generative AI: OpenAI API, LangChain, Vector databases (Pinecone), Claude, Gemini
Cloud Platforms: AWS (SageMaker, S3, EC2, Lambda), Azure (ML Studio, Synapse), GCP (Vertex AI)
Business Tools: Asana, Monday.com, Miro, Figma, Slack, PowerPoint, Google Workspace

Assessment Method:

Continuous Project-Based Assessment (50%): Real rubrics with 4 proficiency levels (Exemplary, Proficient, Developing, Beginning)
Technical Skill Evaluation (30%): Code quality, algorithm efficiency, model performance
Industry Mentor Feedback (20%): Professional evaluation and career readiness assessment

Each project is evaluated on content knowledge (30%), critical thinking (25%), creativity (20%), collaboration (15%), and communication (10%)

Career Outcomes

Program graduates typically transition to:

Entry-Level (Fresh Graduates):

Business/Data Analyst, Junior ML Engineer

Mid-Level (2-3 years):

Senior Analyst, ML Engineer, Product Manager

Advanced (5+ years):

Manager/Director roles, CTO, Startup Founder

The program is designed for working professionals and entrepreneurs who want to combine deep business acumen with practical AI capabilities, validated through real-world industry partnerships and demonstrated in a comprehensive capstone project that can serve as a career launchpad or startup foundation.