Pharma Data Analytics Course

Lesson 1 • The Pharma Data Landscape

Maps the major data sources across drug discovery, clinical trials, manufacturing, and commercial operations. Establishes the scope of analytics opportunities in the industry.

Lesson 2 • Regulatory and Compliance Context

Introduces data integrity principles, audit trails, and validation requirements governing pharma analytics. Grounds students in compliance constraints before technical work begins.

Lesson 3 • Core Analytics Concepts and Terminology

Defines descriptive, diagnostic, predictive, and prescriptive analytics with pharma examples. Provides shared vocabulary used throughout the course.

Lesson 1 • Data Lifecycle and Retention

Covers data creation, storage, archival, and destruction policies in regulated pharma environments. Ensures students understand lifecycle obligations before building analytics solutions.

Lesson 2 • Data Quality Assessment and Control

Teaches completeness, accuracy, consistency, and timeliness dimensions of data quality. Students apply quality checks to clinical and commercial pharma datasets.

Lesson 3 • Data Standardisation and Interoperability

Introduces clinical and laboratory data standards that enable cross-system data exchange. Prepares students to work with standardised datasets in analytics pipelines.

Lesson 4 • Building a Data Catalog

Guides students through cataloguing datasets with business definitions, lineage, and ownership. A well-maintained catalogue accelerates analytics delivery and audit readiness.

Lesson 1 • Hypothesis Testing and Inference

Teaches significance testing, p-values, confidence intervals, and error types in pharma contexts. Students design and interpret tests for clinical and process data.

Lesson 2 • Sample Size and Power Analysis

Explains how sample size, effect size, and power interact in study and process analytics design. Students calculate sample sizes for clinical and quality analytics scenarios.

Lesson 3 • Correlation and Regression Analysis

Covers linear and logistic regression for identifying relationships in pharma datasets. Students build and validate regression models for real pharma use cases.

Lesson 4 • Descriptive Statistics in Pharma

Covers measures of central tendency, dispersion, and distribution shape applied to pharma data. Builds the quantitative foundation for all subsequent analytical work.

Lesson 1 • Clinical Trial Monitoring Analytics

Introduces risk-based monitoring metrics and site performance analytics for ongoing trials. Students build monitoring dashboards that flag data quality and enrolment risks.

Lesson 2 • Clinical Trial Data Structures

Explains subject-level, visit-level, and event-level data structures used in clinical trials. Understanding these structures is prerequisite to any trial data analysis.

Lesson 3 • Data Cleaning and CDISC Preparation

Covers edit checks, query management, and transformation of raw trial data into submission-ready formats. Students produce clean, standardised datasets from raw clinical data.

Lesson 4 • Reporting and Submission Outputs

Covers tables, listings, and figures required for regulatory submissions and clinical study reports. Students produce compliant outputs from analysis datasets.

Lesson 1 • Communicating RWE Findings

Covers structured reporting of observational study results for regulatory and payer audiences. Students translate complex RWE analyses into clear, decision-ready summaries.

Lesson 2 • Confounding and Bias Control

Identifies major biases in observational research and applies methods to reduce confounding. Students apply propensity scoring and restriction to real-world datasets.

Lesson 3 • Observational Study Design

Covers cohort, case-control, and cross-sectional designs with their strengths and limitations. Students select and justify study designs for post-market safety and effectiveness questions.

Lesson 4 • Pharmacovigilance Signal Detection

Applies disproportionality analysis and sequential methods to spontaneous reporting databases. Students detect and evaluate safety signals from post-market surveillance data.

Lesson 1 • Design of Experiments in Manufacturing

Introduces factorial and response surface designs for optimising pharmaceutical processes. Students plan and analyse experiments to identify critical process parameters.

Lesson 2 • Statistical Process Control

Teaches control chart selection, construction, and interpretation for pharma manufacturing processes. Students distinguish common-cause from special-cause variation in process data.

Lesson 3 • Process Data Collection and Structure

Covers batch records, process historians, and laboratory information management system data. Students identify and extract relevant process variables for quality analytics.

Lesson 4 • Deviation and CAPA Analytics

Applies trend analysis and root cause methods to deviation and corrective action data. Students build analytics workflows that prioritise and track quality events.

Lesson 1 • Commercial Data Sources and Integration

Surveys prescription data, syndicated market data, CRM, and patient support programme data. Students integrate these sources into a unified commercial analytics dataset.

Lesson 2 • Promotional Effectiveness Analytics

Measures the impact of sales force, digital, and multichannel promotional activities on prescribing. Students apply attribution models to optimise promotional mix allocation.

Lesson 3 • Market Access and Pricing Analytics

Analyses formulary positioning, payer mix, and price-volume dynamics for access decisions. Students quantify the revenue impact of access scenarios and contracting strategies.

Lesson 4 • Sales Forecasting and Demand Planning

Covers time-series methods and driver-based models for pharmaceutical sales forecasting. Students build and evaluate forecasts for launch and in-line products.

Lesson 1 • Deploying Analytics Solutions at Scale

Addresses MLOps practices, model monitoring, and change control for production pharma analytics. Students design deployment pipelines that maintain model performance over time.

Lesson 2 • Machine Learning Fundamentals for Pharma

Covers supervised, unsupervised, and reinforcement learning concepts with pharma use cases. Students select appropriate ML paradigms for clinical, manufacturing, and commercial problems.

Lesson 3 • Natural Language Processing for Pharma Text

Applies NLP to adverse event narratives, clinical notes, and scientific literature. Students extract structured insights from unstructured pharma text data.

Lesson 4 • AI Model Validation and Regulatory Alignment

Covers explainability, fairness, and validation requirements for AI models in regulated pharma settings. Students document and validate models to meet regulatory expectations.