Post Graduate Diploma/Executive Diploma in AI and Data in Pharmaceutical Technology (PGDAIDPT/EDAIDPT)

The pharmaceutical industry is undergoing a profound digital transformation, driven by advances in Artificial Intelligence (AI), Machine Learning (ML), Big Data Analytics, and digital technologies. As drug development becomes increasingly complex and data-intensive, the ability to collect, analyze, interpret, and utilize vast amounts of data has become essential for innovation, efficiency, and regulatory compliance.

AI and data-driven technologies are revolutionizing every stage of the pharmaceutical product lifecycle—from drug discovery and formulation development to manufacturing, quality control, supply chain management, pharmacovigilance, and regulatory affairs. These technologies enable faster decision-making, predictive analytics, process optimization, enhanced product quality, and reduced time-to-market, ultimately improving patient outcomes and healthcare delivery.

The growing adoption of digital health, real-world evidence, electronic health records, wearable devices, and automated manufacturing systems has created an unprecedented demand for professionals who can effectively integrate pharmaceutical science with data analytics and AI tools. Regulatory agencies worldwide are also increasingly recognizing the role of AI and advanced analytics in ensuring product quality, patient safety, and operational excellence.

This course, is designed to provide participants with a comprehensive understanding of the principles, applications, opportunities, and challenges of AI and data science within the pharmaceutical sector. It equips learners with the knowledge and skills required to leverage modern digital technologies for research, development, manufacturing, quality assurance, regulatory compliance, and healthcare innovation.

By bridging the gap between pharmaceutical sciences and emerging digital technologies, this course prepares professionals to thrive in the future of Pharma 4.0 and contribute to a smarter, more efficient, and patient-centric pharmaceutical industry.

Need and Importance of the Course

• Understand the role of AI and data analytics in modern pharmaceutical operations.
• Explore AI applications in drug discovery, formulation development, and clinical research.
• Learn how data-driven approaches improve manufacturing efficiency and product quality.
• Gain insights into AI-assisted regulatory submissions, pharmacovigilance, and compliance.
• Develop awareness of ethical, governance, and regulatory considerations associated with AI.
• Build competencies aligned with Industry 4.0 and Pharma 4.0 transformation initiatives.
• Enhance career opportunities in emerging areas of pharmaceutical technology and digital healthcare.
• Support evidence-based decision-making through effective data management and analytics.

The course empowers pharmaceutical professionals to harness the power of data and artificial intelligence, transforming information into actionable insights that drive innovation, quality, compliance, and patient-centered healthcare solutions. 

Programme Structure

International Affiliation with

Module 1: Introduction to AI/ML, Digital Transformation and automation in Pharma

• Evolution from traditional pharma to Pharma 4.0
• What is AI, Machine Learning, Deep Learning, and Generative AI?
• Why AI matters in pharmaceutical development
• Global trends and adoption in life sciences
• Limitations and challenges of AI 

Module 2: Fundamentals of Data Science:  Data Types, Data Collection & Cleaning

• Types of data: Structured vs Unstructured
• Sources: Databases, APIs, Web scraping, Surveys
• Data cleaning techniques (handling missing data, duplicates, outliers)
• Tools: Python (pandas), Excel, OpenRefine

Module 3. Statistics for business applications

• Descriptive statistics (mean, median, mode, variance, etc.)
• Inferential statistics (sampling, confidence intervals, hypothesis testing)
• Correlation vs Causation
• Probability basics
• Distributions (normal, binomial)  

 Module 4. Data Analysis with Excel / Google Sheets

• Functions (VLOOKUP, IF, COUNTIF, etc.)
• Pivot Tables
• Charts and Dashboards
• Conditional formatting  

Module 5. Python for Data Analytics

• Python basics (variables, loops, functions, data structures)
• Libraries:
• pandas for data manipulation
• numpy for numerical operations
• matplotlib & seaborn for visualization
• Exploratory Data Analysis (EDA) in Python  

Module 6. SQL for Data Analytics

• SELECT, WHERE, GROUP BY, JOIN, ORDER BY
• Aggregations
• Subqueries & Common Table Expressions (CTEs)
• Hands-on with real-world datasets (using SQLite, MySQL, or PostgreSQL)  

Module 7. Data Visualization

• Principles of effective data visualization
• Tools:
• Excel
• Python (Matplotlib, Seaborn)
• Power BI / Tableau / Google Data Studio
• Dashboards and storytelling with data

Module 8. Introduction to Big Data

• What is Big Data?
• Hadoop & Spark overview
• Hive and SQL on Hadoop
• Real-world big data use case 

Module 9. Predictive Analytics, AI-ML for different sources of data

• Introduction to AI-ML
• Regression, Classification
• Model evaluation metrics
• Tools: Scikit-learn
• Generative AI and Agentic AI 

Module 10: AI in Drug Discovery and Pre-clinical Development

• Target identification
• Virtual screening and HTS
• Molecular Docking
• Molecular dynamics simulation
• Drug repurposing
• Pharmacophore modelling
• Prodrug design
• AI in Precision Medicine
• AI in Biologics and Advanced Therapeutics- Vaccine, Biologics, Biosimilar (Antibody, mRNA and peptide)
• Predictive Modeling for Drug-Target Interactions
• Deep Learning and Machine learning Algorithms
• De Novo Drug design using generative algorithms
• Predictive toxicology and Pharmacology: ADME prediction 

Module 11: AI in Formulation Development and Optimization (QbD and DoE)

• QbD and Digital Formulation Development
• Integration of AI with DoE
• AI in Pre-formulation Studies
• AI-Assisted Formulation Optimization
• AI-Enhanced DoE Approaches
• Predictive Modeling of Critical Quality Attributes
• Design Space Establishment
• AI in Stability Studies
• Identification of influential variables
• Reduction of experimental runs
• Sequential optimization strategies 

Module 12: AI in Manufacturing and Quality

• Pharma 4.0 concepts
• Predictive analytics
• Process analytical technology (PAT)
• Real-time release testing
• Anomaly detection
• Continuous manufacturing and digital twins 

Module 13: AI in Regulatory Affairs and Compliance

• AI in dossier preparation
• Regulatory intelligence
• Labeling automation
• Submission management
• Health authority expectations
• Documentation requirements
• Inspection readiness 

Module 14: AI in IPR Administration

• Automated patent classification
• Prior-art recommendation
• Patent translation
• Examination support
• Digital patent management systems 

Module 15: Data Integrity and Governance

• ALCOA+ principles
• GxP data requirements
• Data quality management
• Master data governance
• Metadata management
• Audit trails
• Data privacy considerations 

Module 16: Validation of AI Systems

• Computerized System Validation (CSV)
• Computer Software Assurance (CSA)
• GAMP 5 principles
• Risk-based approaches
• Validation lifecycle
• Model verification and monitoring
• Change management 

Module 17: Ethics, Governance, and Emerging Regulations in Pharma Industry: USFDA, EMA, TGA, and Medsafe

• Explainable AI
• Algorithmic bias
• Transparency and accountability
• Ethical use of patient data
• International guidance trends
• Future regulatory frameworks

Module 18: Industry Based Case Studies

Module 19: Capstone Project

Eligibility

Graduates in any discipline are eligible for our Post Graduate Diploma, Executive Diploma and Professional Certifications Programmes. 10+2 pass-outs are eligible for our Under Graduate Diploma and Diploma holders of two to three years course duration are also eligible for the PG Diploma.

Programme Duration

The minimum duration to complete the PG diploma programme is 12 months and maximum is 24 months. The minimum duration to complete the executive diploma programme is 6 months and maximum is 12 months.

Programme Mode

Registrations are currently open for regular and Part-time (Online Live Classes) both modes.

Programme Deliverables

A comprehensive study material for all the modules in hard copies ensuring the needs of the audience. The accompanying training material is appropriately aligned with the current Industry’s expectations.

• Assignments for all the programme modules for continuous evaluation and guidance.
• Interactive or online live sessions on all key areas of the programme giving all flexibility to the participants.
• Part-time (Online Live Classes) for all the modules will be conducted on the weekends. Moreover, a doubt clearing session will also be scheduled before the examination.
• All the efforts are made by IGMPI faculty members to make the entire programme modules easily understandable.
• Assessment and evaluation for all the programme modules in order to enhance the levels of competencies and skills of the participants leading towards the objective of application in the job.
• At the end of each programme modules, the trainers shall obtain feedback from the participants using specially designed questionnaires.
• All learning and training delivery initiatives shall be conducted in English.

Examination & Certification

All the participants are expected to appear for an online exam and are also obliged to submit assignments after each module. After successful completion, the participants will be awarded Post Graduate Diploma/Executive Diploma in AI and Data in Pharmaceutical Technology by IGMPI. For all the above-mentioned modules, Part-time (Online Live Classes) or face-to-face classes (Regular mode), elaborate programme material, self-assessment assignments would be provided by the Institute. Details get updated on the webpage as well.

Discipline in Classes and Examination

Every student is required to observe a disciplined behaviour during her/his classes, assessments & examinations and to follow instructions from the Professors. Any act of indiscipline may result into discredit & it will be mentioned in her/his academic report.

Placement Assistance & Corporate Relations

The Institute has partnered with many organizations for providing with placement assistance to its participants. Besides, it has a robust placement cell comprised of senior level Human Resources professionals and Talent Acquisition experts which maintains close links with business and industry. This cell is continuously engaged in promoting the employability of our participants and encouraging the concerned Human Resources department and Hiring Managers to recruit/hire our participants for their vacant positions. The efforts of our placement cell also include helping with professional resume writing & interview skills.

In recent months the Institute has witnessed more and more participation from professionals working with global pharmaceutical, IT and Healthcare like Abbott Laboratories, Infosys Ltd., Dell Technologies, Accenture, Mobikwik, Novo Nordisk, Merck Group, Optum, American Express, Allegis Group, IBM, Cardinal Health, Wipro Limited, Tata Consultancy Services, Aster DM Healthcare, Merck Group, etc. The IGMPI’s Corporate Resource Division actively recommends our students and training participants for various job requirements and specialized roles to Human Resource, Talent Acquisition as well as the heads of various departments in Pharmaceutical, Healthcare industries on regular basis.

Future career prospects

The rapid adoption of Artificial Intelligence (AI), Machine Learning (ML), Big Data Analytics, and digital technologies across the pharmaceutical and healthcare sectors has created significant demand for professionals with expertise in both pharmaceutical sciences and data-driven technologies. Competitive Intelligence and Market Intelligenceare important applications of AI and data analytics in the pharmaceutical industry. AI enables the collection, analysis, and interpretation of large volumes of scientific, clinical, regulatory, patent, and market data to generate actionable insights. These capabilities help organizations monitor competitor activities, track drug development pipelines, forecast market trends, identify unmet medical needs, assess commercial opportunities, and support strategic decision-making. By leveraging AI-driven intelligence, pharmaceutical companies can enhance innovation, optimize portfolio management, and maintain a competitive advantage in a rapidly evolving healthcare landscape. Graduates of this course can pursue diverse career opportunities in drug discovery, clinical research, pharmacovigilance, regulatory affairs, quality assurance, manufacturing, medical devices, digital health, and healthcare analytics. As organizations embrace Pharma 4.0, AI-powered automation, predictive analytics, and real-world evidence generation, professionals skilled in leveraging data for decision-making and innovation will play a critical role in improving efficiency, compliance, product quality, and patient outcomes. This interdisciplinary skill set not only enhances employability across pharmaceutical companies, biotechnology firms, CROs, CDMOs, healthcare organizations, and regulatory agencies but also prepares individuals for future leadership roles in digital transformation, AI strategy, and innovation management within the life sciences industry.

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Last date for submitting completed Application Form: 30^th Jun 2026.

For further enquiries, call or write to us on:
18001031071 (Toll Free -9:00 am to 5:30 pm IST-except on Central Government holidays)/ info@igmpi.ac.in