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⬡ PHARMA ANALYTICS CURRICULUM
Master Python, ML & AI
for Pharma Analytics

A structured 8-phase program from Python fundamentals to production AI — focused on HCP segmentation, patient analytics, and advanced pharma data science. Build skills employers in pharma & life sciences demand.

⏱ 120–150 hrs 📚 31 Modules 🧬 Pharma-focused 💊 HCP · Patient · Commercial 🤖 ML + AI
0
Lessons Completed
30
Total Lessons
8
Phases
∞
Pharma Use Cases
Learning Roadmap
PHASE 1 · Weeks 1–3

Python Core

Syntax, data structures, OOP, file I/O — the absolute foundation. Build scripts for pharma data processing.

PHASE 2 · Weeks 4–6

Data Analytics Stack

NumPy, Pandas, Matplotlib, Seaborn, Plotly. Work with Rx data, claims, prescriber data, sales data.

PHASE 3 · Weeks 7–10

Machine Learning Foundations

Statistics, Scikit-learn, regression, classification, model evaluation. Learn with pharma datasets.

PHASE 4 · Weeks 11–14

HCP Analytics

HCP 360 data, segmentation, propensity modeling, next-best-action. Core to pharma commercial analytics.

PHASE 5 · Weeks 15–18

Field Force Analytics

Territory optimization, sales force effectiveness (SFE), call activity analytics, ROI and promotional response modeling.

PHASE 6 · Weeks 19–22

Advanced Segmentation

Patient segmentation, DBSCAN, hierarchical clustering, RFM, NLP on medical notes and EHR data, and multi-dimensional HCP+patient+channel segmentation.

PHASE 7 · Weeks 23–26

Deep Learning & AI

Neural networks in PyTorch, transformers, LLMs, BioBERT for clinical text, AI in drug discovery.

PHASE 8 · Weeks 27–30

MLOps & Capstone

Deploy models, build pipelines, Capstone: end-to-end HCP + patient analytics project.

Lesson