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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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
Quick answer
Data Science & AI/ML is a 10-week course teaching Python, statistics, ML models and deployment. It's for analysts, engineers and career switchers who want a job-ready portfolio, not just notebook skills, and it ends with a deployed, end-to-end capstone project.
How this compares to self-taught Kaggle/online courses
- Kaggle and free MOOCs mostly stop at model accuracy in a notebook; this course covers deploying, versioning and monitoring a model in production
- You get structured statistics and Python foundations built up week by week, instead of piecing together scattered tutorials
- Executive dashboard design is taught explicitly, so your models connect to business decisions, not just leaderboard scores
- The capstone is an end-to-end deployed project, not a Kaggle notebook that never leaves your laptop
What you'll walk away with
- Working fluency in Python, pandas, numpy and the statistics underlying ML
- Predictive models (churn, LTV, demand forecasting) validated against real metrics
- A deployed ML pipeline with training, versioning and monitoring, not just a notebook
- Executive-ready dashboards built in Looker or Metabase
- A complete capstone project spanning raw data to a deployed model
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.
05How much does this course cost?
₹15,999 for the full 10-week Beginner → Intermediate cohort, priced against the months it typically takes to piece together the same skills from scattered free resources.
06Is this worth it compared to learning from Kaggle and free courses?
Kaggle and free MOOCs mostly stop at notebook accuracy; this course adds the deployment, versioning and monitoring skills that make you a working data scientist rather than a leaderboard hobbyist.
07Do I need a math or coding background to start?
No — it's a Beginner → Intermediate course, so Python and statistics are built up from the ground as you go; basic computer literacy is enough to start.
08Is there a certificate?
Yes, along with a portfolio-ready capstone project — the deployed model and case study matter more to employers than the certificate itself.
09What if I fall behind over the 10 weeks?
The course is modular with recorded sessions, so you can revisit any block (Python, EDA, ML, MLOps) before moving to the capstone, which has its own buffer time.
10What job outcomes can I expect?
Graduates typically move from analyst roles into data scientist or ML engineer roles, using the capstone project as the centerpiece of their portfolio in interviews.
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