Data Science
Data Science is a 10-week intermediate course covering statistics, Python, predictive modeling, and deployment for learners who already know basic Python. It teaches regression, classification, feature engineering, and model evaluation using pandas, scikit-learn, and PyTorch, ending in a portfolio project that solves a real business problem end-to-end.
Master statistics, Python, and predictive modeling to turn raw data into actionable business intelligence.
What you'll learn
Course details
Duration
10 weeks
Best for
Learners with basic Python who want to move into data science roles
Enrollment isn't open for this course yet — check back soon or contact us.
Course Curriculum
A module-by-module breakdown of what you'll cover in Data Science.
01Getting Started & Environment Setup
4 topicsWeeks 1–2
- Installing Python and Jupyter Notebook
- Installing PyTorch and verifying GPU/CPU detection
- Creating free Hugging Face and MLflow accounts
- Verifying SQL access to a practice database
02Basic
3 topicsWeek 3
- Linear algebra, probability, and descriptive statistics basics
- pandas and NumPy for data manipulation
- SQL joins, window functions, and CTEs on real schemas
03Intermediate
3 topicsWeeks 4–5
- Probability distributions, hypothesis testing, and A/B test design
- Charting with matplotlib and seaborn
- Dashboard design and structuring a data narrative for stakeholders
04Advanced
3 topicsWeeks 6–7
- Regression, classification, trees, and ensembles
- Cross-validation, feature engineering, and scikit-learn pipelines
- Comparing baseline vs. tuned model performance
05Pro
4 topicsWeek 8
- Neural networks, transformers, and retrieval-augmented generation
- Serving models with FastAPI, Docker, and basic CI/CD
- Model monitoring, drift detection, and cloud deployment
- Major Project: learner-support prediction study
06AI Pro
4 topicsWeeks 9–10
- Using Claude, ChatGPT, and Perplexity for analysis and modeling code
- Prompting for correct, assumption-aware modeling approaches
- Verifying AI-generated analysis for leakage and correct metrics
- Project: AI-Assisted Data Science Workflow
Tools & Technologies
Hands-on practice with the same tools used by working professionals.
Real-Time, Industry-Based Projects
This is project-based learning modeled on real industry scenarios — you'll build portfolio-ready work as you go, not just watch lectures.
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.
Practical AI Skills
Hands-on training in using AI tools well — prompting, everyday workflows, and where AI actually helps versus where it doesn't.
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.
Career & Placement Support
100% of students enrolled in Data Science get full access to placement support — real tools and guidance, not just a promise. This is support and access, not a guaranteed job outcome.
Resume Builder
A resume auto-built from your real completed projects and certificates, ready to export.
Shareable Portfolio
A public portfolio page showcasing your approved projects, with a link you can share with employers.
Interview Preparation
Guidance on presenting your projects and experience clearly in real interviews.
Peer & Mentor Community
Ongoing access to our learner community for questions, feedback, and support after you finish.
What Your Certificates Look Like
Real, verifiable certificates with your name, Data Science, and a QR code anyone can scan to confirm they're genuine — one for completing the course, and a separate one if you take the internship track. Earning a certificate is free. To view, download and share it, there's a one-time ₹100 release fee per certificate — the same for free-course, paid-course and internship certificates.
Course Completion Certificate
Data24ZoneCertificate of Completion
Illustrative recipient
Your Name Here
Example certificate for completion of
Data Science
- Certificate No.
- D24-SAMPLE
- Mode
- Self-Paced
- Duration
- 10 weeks
- Example issue date
- On completion
Sample only — not a real, issued certificate.
Verification is available only on issued certificates.
Internship Completion Certificate
Data24ZoneCertificate of Internship
Illustrative recipient
Your Name Here
Example certificate for completion of
Data Science
- Certificate No.
- D24-SAMPLE
- Mode
- Internship
- Duration
- 10 weeks
- Example issue date
- On completion
Sample only — not a real, issued certificate.
Verification is available only on issued certificates.
Student Reviews
“I had the privilege of learning data science, data analytics, and data engineering from Prasanth. He breaks down complex topics like decision trees and neural networks into clear, digestible steps.”
Nisha Durairaj
Voice and Accent Trainer
Frequently Asked Questions
What will I learn in the Data Science course?
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.
What tools and technologies does the Data Science course cover?
This course covers: Python, SQL, pandas & scikit-learn, PyTorch, Hugging Face, LangChain, Power BI / Tableau, MLflow.
Is the Data Science course beginner-friendly?
This is a intermediate-level course, so it assumes some prior familiarity with the subject. It starts with a "Getting Started & Environment Setup" module covering tool installation before the main curriculum.
How long does the Data Science course take to complete?
The course is designed to be completed in approximately 10 weeks, depending on your pace.
What enrollment options are available for Data Science?
Project Training Track (₹1,499) — One defined learning project and 2 review sessions; no employment or stipend included. Self-Paced (₹1,999) — Learn anytime with the full recorded curriculum and community access.
Is there an internship option for Data Science?
Yes — the Internship track means working through real projects with our team and earning a separate Internship Certificate.
Will I get a certificate after completing Data Science?
Yes. Self-paced enrollment earns a course completion certificate once your final project is reviewed and approved. Internship enrollment earns an internship certificate once your internship projects are approved. Every certificate is publicly verifiable on our website and can be added directly to your LinkedIn profile. Earning it is free; releasing it to view, download and share is a one-time ₹100 fee per certificate.
What kind of projects will I build in Data Science?
You'll work on real projects such as: Customer churn analysis with a baseline-to-tuned classification model and a precision/recall trade-off writeup; Image classification web app built by training or fine-tuning a CNN and wrapping it in a small Flask app; RAG document Q&A assistant that answers questions over a custom document set with citations; Time-series demand forecasting comparing a classical statistical model against a gradient-boosted baseline; Deployed model with monitoring, containerized and shipped with basic drift detection.
Is placement support included with Data Science?
Yes — every enrolled student gets full access to our placement support: resume building, a shareable project portfolio, mock interview preparation, and guidance applying what you've built to real job applications. This is support and access, not a guaranteed job outcome — how quickly it leads to an offer depends on the market and your own effort.
