Python & foundations
- Python for data
- NumPy & Pandas
- Exploratory analysis
- Visualization
IT · Beginner to advanced
Data Science
Python, statistics, ML pipelines, and storytelling for data — offline track includes full job assistance.
OnlineOfflineJob assist · offline
About this program
Build end-to-end data science skills: explore messy datasets, engineer features, train models, and present insights stakeholders can act on. Offline cohorts unlock placement support, mock interviews, and portfolio reviews.
Who this is for
Learners targeting Data Analyst or Junior Data Scientist paths who want structured projects, mentor feedback, and offline placement coaching.
Target roles
- Data Analyst
- Junior Data Scientist
- ML Ops associate
Curriculum outline
Statistics & ML
- Inference
- Regression & classification
- Model evaluation
- Feature engineering
Projects & career
- Capstone project
- Portfolio packaging
- Interview drills
What you'll walk away with
- Ship end-to-end analysis notebooks and dashboards
- Apply statistical reasoning to real business problems
- Deploy ML models with sound validation practices
- Present findings that drive hiring-ready case studies
Job assistance (offline)
Offline cohorts in this program include career roadmap, resume & portfolio reviews, mock interviews, and partner hiring activity. Online learners still graduate with projects and mentor feedback — talk to admissions about mixed paths.
How placement worksFAQ
Data Science