Data Science & AI/ML Course — from raw data to production models.
Python, statistics, ML models and MLOps basics — a practitioner's path into data science.
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50+ founders consulted last month
Most data science courses stop at the notebook. This one goes further — training, evaluating and deploying models that plug into real decisions.
Course outcomes
Every module ships with a working tool or template you keep using after class.
Build predictive models end to end
Churn, LTV and demand forecasting models validated against real metrics.
Ship an ML pipeline to production
Training, versioning and deployment — not just a Jupyter notebook.
Design executive dashboards
Looker/Metabase dashboards leadership will actually open.
Portfolio-ready capstone project
A complete case study from data to deployed model.
From notebook to production
You'll learn the statistics and modelling fundamentals, but also the deployment and monitoring skills that separate a working data scientist from a Kaggle hobbyist.
Who this is for
- Analysts moving into data science
- Engineers adding ML to their skill set
- Career switchers building a portfolio
Week-by-week breakdown
A clear, transparent syllabus — no surprises.
Python & statistics foundations
The core toolkit: pandas, numpy, and the statistics that underpin ML.
Data wrangling & EDA
Cleaning, exploring and understanding real, messy datasets.
Machine learning models
Classical ML and an introduction to deep learning.
MLOps & deployment basics
Versioning, APIs and monitoring for models in production.
Capstone project
An end-to-end project you can show in interviews.
01Do I need a math background?
Basic statistics helps, but the course builds up the concepts you need as you go.
02What tools does the course use?
Python, pandas, scikit-learn, and SQL, plus an intro to PyTorch.
03Is this different from the Data Analytics course?
Yes — this covers predictive modelling and ML; Data Analytics focuses on SQL, dashboards and reporting.
04Is there a capstone project?
Yes — a full project from raw data to a deployed model.
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