DS
Deepak Suhag
📊Data Science & AIML

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.

10 weeks📶 Beginner → Intermediate4.8 ★💰 ₹15,999
Free Consultation

Get the Data Science & AIML Syllabus

Free curriculum PDF + early-bird seat. I'll reply within 24 hours.

D
A
R
M

50+ founders consulted last month

👤
✉️
📱
💰
📅
🔒 No spam ever⚡ 24h response🤝 NDA on request

Most data science courses stop at the notebook. This one goes further — training, evaluating and deploying models that plug into real decisions.

What you'll learn

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
Curriculum

Week-by-week breakdown

A clear, transparent syllabus — no surprises.

01🐍

Python & statistics foundations

The core toolkit: pandas, numpy, and the statistics that underpin ML.

02🔍

Data wrangling & EDA

Cleaning, exploring and understanding real, messy datasets.

03🤖

Machine learning models

Classical ML and an introduction to deep learning.

04🚀

MLOps & deployment basics

Versioning, APIs and monitoring for models in production.

05🏆

Capstone project

An end-to-end project you can show in interviews.

FAQ

Common questions

Can't find what you're looking for? Ask directly →

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.

📊 Data Science & AIML

Ready to enrol?

Get the full syllabus and hold your seat at the early-bird price. No spam, no pressure.

Ask Deepak's AIHow can I help scale your growth?