Data Science & AI/ML Course in Champs-Élysées
For Champs-Élysées learners, this course goes from raw data to a deployed model — not just a notebook exercise.
- Live cohorts, not recordings
- Practitioner-taught
- Community & placement
- Lifetime access
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For Champs-Élysées analysts and engineers
Live sessions cover Python, statistics, ML models and MLOps basics, with a capstone project you can show in interviews. This course goes from raw data to a deployed model for Champs-Élysées students — not just a notebook exercise.
Curriculum breakdown
Module 1: Python and statistics foundations
Foundational skills Champs-Élysées students need before touching a real model — Python fluency and core statistics.
Topics covered
- Python for data analysis (pandas, numpy)
- Probability and statistical inference
Module 2: Data wrangling and EDA
Cleaning and exploring real, messy data for Champs-Élysées students — the part of the job that takes most of the actual time.
Module 3: Machine learning models
Building, validating and comparing models on real datasets.
Topics covered
- Classification and regression models
- Model evaluation and validation
Module 4: MLOps and deployment basics
Getting a model from a notebook into something running in production.
How the cohort runs
Format and schedule
Live, capstone-driven cohort
Live sessions for Champs-Élysées students, building toward a capstone project from raw data to a deployed model.
Who this course is for
Analysts moving into data science
Champs-Élysées analysts ready to move from reporting into predictive modelling.
Engineers adding ML to their toolkit
Champs-Élysées engineers who want to add machine learning skills to an existing technical background.
Outcomes and support
Capstone project
A full project from raw data to a deployed model, built by Champs-Élysées students during the cohort.
Tools used
Python, pandas, scikit-learn, SQL, plus an intro to PyTorch
The same tools used in real production data science work.
Note
Basic statistics helps, but the course builds up the concepts Champs-Élysées students need as they go.
Why Champs-Élysées students choose Deepak Suhag
Taught by a practitioner who ships models to production for clients, not just an academic curriculum.
How it works
Simple, transparent process — from first contact to measurable results.
Enrol & Onboard
Instant portal access, cohort Slack invite, and full session calendar on day one.
Live Sessions
Weekly Zoom sessions with real campaign walkthroughs, live dashboard reviews, and Q&A.
Build & Get Feedback
Hands-on assignments on your own campaigns with direct 1:1 feedback from Deepak.
Graduate & Network
Industry certificate, alumni community, job board access, and ongoing placement support.
Tools & platforms
The exact stack I use daily across growth marketing, web development, AI, and automation — no guesswork, no vendor lock-in.
Why work with Deepak
Here's what makes this different from every other option in Champs-Élysées.
Taught by a practitioner
Every module comes from live campaigns with real budgets — not textbook theory or outdated slides.
Live cohorts, not recordings
Ask questions in real time, get live feedback on your campaigns, and learn with a cohort of peers.
Practitioner-led curriculum
Real ad accounts, real case studies, real budgets — everything relevant to where you work, wherever that is.
Career-ready outcomes
Portfolio projects, alumni Slack, and direct referrals to companies actively hiring in your city.
Everything you need to know
Still have a question that isn't answered here? Reach out directly — I respond to every inquiry personally.
Ask a question01Do I need a math background to join from Champs-Élysées?
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 for Champs-Élysées students?
Yes — a full project from raw data to a deployed model.
I started teaching because I was frustrated seeing marketers memorise theory they'd never use. Every lesson I teach comes from a live campaign, a real mistake, or a real win. You'll leave with skills you can use tomorrow morning.