// 16-week cohort program
Python
Mastery
From absolute beginner to production-ready practitioner
Data Science
Machine Learning
Scientific Computing
Web APIs
160
learning blocks
21+
major libraries
16
weeks
6
cohorts/year
// what you'll learn
5-part curriculum
PART I — WK 1–4
Foundations
Python core → arrays → DataFrames → viz
NumPy
Pandas
Matplotlib
Seaborn
PART II — WK 5–8
Data & Web
Pipelines, APIs, scraping, ML
requests
Flask
FastAPI
sklearn
PART III — WK 9–12
Science, NLP & Vision
Stats, transformers, computer vision
SciPy
spaCy
Transformers
OpenCV
PART IV — WK 13–14
Engineering & Automation
Databases, ETL, testing, scripting
SQLAlchemy
Dask
pytest
Selenium
PART V — WK 15–16
Capstone: Design → Deploy → Present
5 tracks — Data Analysis / ML API / NLP / Computer Vision / Data Engineering
Track A: EDA report
Track B: REST API model
Track C: NLP web app
Track D: Vision classifier
Track E: ETL pipeline
// learning outcomes
What you'll be able to build
Data pipelines
95%
ML models
88%
REST APIs
85%
NLP apps
80%
Computer vision
75%
📈
Analyze any dataset
NumPy, Pandas, SciPy, and statsmodels for end-to-end EDA
🧠
Train & ship ML models
scikit-learn pipelines with FastAPI serving layer
💬
Build NLP pipelines
BERT, spaCy, and Hugging Face Transformers
🚀
Automate & test
pytest, Selenium, Dask for production-grade code
// investment
Pricing & tiers
Self-paced
$
497
✓ Early bird $399
✓
All 160 blocks
✓
JupyterLab sandbox
✓
6-month access
✕
Live instruction
✕
Mentoring
Cohort live
$
1,997
✓ Early bird $1,597
✓
2×30min live daily
✓
Weekly 1:1 office hours
✓
Slack community
✓
Auto-graded exercises
✓
Graded certificate
Corporate
from $3,200
/learner
Min 4 learners
✓
Dedicated instructor
✓
Custom projects
✓
Unlimited 1:1 sessions
✓
Custom branding cert
✓
Invoice net 30
20%
Early bird 30+ days out
25%
Student / recent grad
40%
Non-profit / NGO
$100
Per referral credit
Start your Python
journey today
6 cohort starts per year — seats fill fast
Enroll now ↗
View syllabus
Jan 2026 — Open
Mar 2026
May 2026
Jul 2026
Sep 2026
Nov 2026
01 / 05 — intro
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