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
Compare enrollment modes
| Mode | Price | What you get |
|---|---|---|
| Live Class | ₹4,999 | Instructor-led live cohort with doubt-clearing sessions — includes both the course and internship certificates. |
| Recorded Batch | ₹2,999 | Full recordings of a completed live batch — includes both the course and internship certificates. |
| Internship | ₹2,499 | Work on 4 real projects with our team and earn an internship certificate (course certificate not included). |
| Self-Paced | ₹1,999 | Learn anytime with the full recorded curriculum and community access. |
Choose how you learn
Course Curriculum
A module-by-module breakdown of what you'll cover in Data Science.
01
Getting Started & Environment Setup
Week 1- 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
02
Python, Math & SQL Foundations
Weeks 2–3- Linear algebra, probability, and descriptive statistics basics
- pandas and NumPy for data manipulation
- SQL joins, window functions, and CTEs on real schemas
- API ingestion and file-based data wrangling
03
Statistics & Experimentation
Week 4- Probability distributions and hypothesis testing
- Confidence intervals and A/B test design
- Intro to Bayesian thinking
- Writing up experiment results and recommendations
04
Data Visualization & Storytelling
Weeks 5–6- Charting with matplotlib and seaborn
- Dashboard design in Power BI or Tableau
- Choosing the right visualization for the question
- Structuring a data narrative for stakeholders
05
Machine Learning Fundamentals
Week 7- Regression, classification, trees, and ensembles
- Cross-validation and feature engineering
- Building scikit-learn pipelines
- Comparing baseline vs. tuned model performance
06
Deep Learning & Generative AI
Weeks 8–9- Neural network fundamentals, CNNs, and transformers
- Tokenization, embeddings, and Hugging Face basics
- Prompt engineering and retrieval-augmented generation
- Building LLM-powered tools and lightweight fine-tuning
07
MLOps & Deployment
Week 10- Serving models with FastAPI or Flask
- Containerizing with Docker and basic CI/CD
- Model monitoring and drift detection
- Cloud deployment fundamentals
Tools & Technologies
Hands-on practice with the same tools used by working professionals.
Python
SQL
panpandas & scikit-learn
PyTPyTorch
HugHugging Face
LanLangChain
PowPower BI / Tableau
MLfMLflow
Hands-on Projects
This is project-based learning — you'll build real, portfolio-ready work as you go, not just watch lectures.
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
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.
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
“A highly knowledgeable and passionate trainer with a deep understanding of data analytics, data science, and BI tools. Sessions were well-structured and filled with practical insights.”
Prethivirajan S
Data Scientist Intern
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. It still starts with a "Getting Started & Environment Setup" module covering tool installation, but assumes some prior familiarity with the subject.
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?
Live Class (₹4,999) — Instructor-led live cohort with doubt-clearing sessions — includes both the course and internship certificates. Recorded Batch (₹2,999) — Full recordings of a completed live batch — includes both the course and internship certificates. Internship (₹2,499) — Work on 4 real projects with our team and earn an internship certificate (course certificate not included). Self-Paced (₹1,999) — Learn anytime with the full recorded curriculum and community access.
Is there an internship option for Data Science?
Yes — enrolling in the Internship track (or the Live/Recorded tracks, which include it) means working through 4 real projects with our team and earning a separate Internship Certificate, in addition to the course completion certificate for Live/Recorded enrollments.
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 all 4 internship projects are approved. Live and Recorded enrollments earn both. Every certificate is publicly verifiable on our website and can be added directly to your LinkedIn profile.
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.
