D24
Data & AIIntermediate10 weeks
Data Science
Master statistics, Python, and predictive modeling to turn raw data into actionable business intelligence.
What you'll learn
Statistics and probability for data-driven decisions
Predictive modeling with regression and classification
Feature engineering and model evaluation
A portfolio project solving a real business problem
Course details
Duration
10 weeks
Best for
Learners with basic Python who want to move into data science roles
On completion
Verified certificate & placement assistance
Course Curriculum
A module-by-module breakdown of what you'll cover in Data Science.
01
Statistics for Data Science
Weeks 1–3- Probability and distributions
- Hypothesis testing
- Statistical inference for decision making
02
Python for Data Science
Weeks 4–5- Pandas and NumPy for data wrangling
- Data visualization with Matplotlib and Seaborn
- Working with Jupyter notebooks
03
Predictive Modeling
Weeks 6–8- Regression and classification models
- Model evaluation and validation
- Feature engineering techniques
04
Real-World Projects
Weeks 9–10- End-to-end data science pipeline
- Business intelligence case study
- Final portfolio project and review
Tools & Technologies
Hands-on practice with the same tools used by working professionals.
Python
PdPandas
sklScikit-learn
JnJupyter Notebook
SQL
Hands-on Projects
This is project-based learning — you'll build real, portfolio-ready work as you go, not just watch lectures.
Predicting customer lifetime value
House price prediction model
End-to-end data science case study
Every course includes a guided internship on top of your project work — so you graduate with real experience, not just a certificate.
Communication & Soft Skills
Built into every course, alongside the technical curriculum.
Communication Skills
Practice explaining technical work clearly to teammates, managers, and clients.
Teamwork & Collaboration
Work in small project teams using real workflows like stand-ups and code reviews.
Problem Solving
Build the habit of breaking down ambiguous problems into clear, solvable steps.
Time Management
Learn to plan, prioritize, and deliver project work against realistic deadlines.
Presentation Skills
Present your project work and results with confidence, as you would to a client.
Resume & Profile Building
Build a strong resume, LinkedIn profile, and project portfolio that recruiters notice.
Interview Preparation
Mock interviews and portfolio reviews to get you ready for real job applications.
Student Reviews
“The pacing was perfect for someone transitioning from software into data science. Loved the case studies.”
Aravind Kumar
Software Engineer
“Finally a course that connects the statistics to actual business decisions, not just theory.”
Divya Menon
MBA Student
Enroll in Data Science
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