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New Cohort · June 2025 · 180+ Hours

Master
Data Science
using Python

From raw data to production ML models — learn pandas, NumPy, scikit-learn, deep learning, and real-world analytics. Build a portfolio that gets you hired.

Start Learning Explore Curriculum
18K+
Students
180h
Content
4.9 ★
Rating
96%
Job Rate
model_accuracy.ipynb ● RUNNING
Model Performance Comparison (%)
XGBoost
96.2
RandomF.
91.4
LightGBM
94.1
LogReg
82.7
SVM
87.3
0.961
F1-Score
0.943
Precision
0.978
AUC-ROC
train_model.py
import pandas as pd
from sklearn.ensemble import XGBClassifier
from sklearn.model_selection import train_test_split
 
df = pd.read_csv('dataset.csv')
X, y = df.drop('target', axis=1), df['target']
X_train, X_test, y_train, y_test = train_test_split(
  X, y, test_size=0.2, random_state=42)
 
model = XGBClassifier(n_estimators=200)
model.fit(X_train, y_train)
# accuracy: 96.2% ✓
Libraries & Tools You'll Master
🐍 Python 3.12 🐼 Pandas 🔢 NumPy 📊 Matplotlib 🎨 Seaborn 🤖 Scikit-learn ⚡ XGBoost 🔥 PyTorch 🧠 TensorFlow 📓 Jupyter 🚀 FastAPI ☁️ AWS SageMaker
What You'll Get

Built to take you from
curious to employed

Hands-on projects, real datasets, and mentorship — everything a modern data scientist needs in one program.

Python for Data Science
Master Python from scratch with a data-first curriculum — lists, dicts, comprehensions, OOP, and file I/O all tied to real analysis tasks.
Core Track
Data Wrangling & EDA
Tame messy real-world data with pandas and NumPy. Master groupby, merges, pivots, and exploratory analysis with Seaborn and Matplotlib.
Analysis
Machine Learning A–Z
Linear/logistic regression, decision trees, ensembles, SVMs, and clustering — with scikit-learn, XGBoost, and LightGBM on 20+ datasets.
ML Track
Deep Learning & Neural Nets
Build CNNs, RNNs, Transformers, and fine-tune pre-trained models with PyTorch and TensorFlow. Train on image, text, and tabular data.
DL Track
Business Intelligence & Viz
Tell stories with data using Plotly, Dash, and Power BI integrations. Build interactive dashboards and present insights to non-technical stakeholders.
Analytics
MLOps & Model Deployment
Package models with FastAPI, containerize with Docker, and deploy to AWS SageMaker. Learn MLflow, DVC, and production monitoring pipelines.
Deployment
Program Structure

7 Modules.
One Complete Data Scientist.

01
Python Fundamentals
Data types, control flow, functions, OOP, file handling, and the data science toolchain setup.
02
Data Analysis with Pandas & NumPy
DataFrames, indexing, aggregations, merges, time-series, and advanced EDA workflows.
03
Data Visualization
Static and interactive charts with Matplotlib, Seaborn, Plotly, and storytelling dashboards.
04
Machine Learning with Scikit-learn
Supervised & unsupervised models, feature engineering, cross-validation, and hyperparameter tuning.
05
Deep Learning & NLP
Neural architectures, CNNs, RNNs, BERT fine-tuning, and sentiment/classification projects.
06
SQL & Big Data Pipelines
PostgreSQL, PySpark, Apache Kafka, and building end-to-end data engineering pipelines.
07
MLOps & Capstone Project
Deploy a full ML system to production. Includes portfolio review, mock interviews, and job placement support.
Got Questions?

Frequently Asked
Questions

No prior coding experience required! Module 1 starts from absolute Python basics. If you can use Excel and are comfortable with numbers, you're ready. Many of our top graduates came from non-technical backgrounds like finance, healthcare, and marketing.

The program is structured for 7 months at 15–18 hours/week, but it's fully self-paced. Sprint through it in 4 months or take it slow over a year — you have lifetime access to all content and future updates.

You'll complete 30+ guided projects and 5 major capstone-style projects including a stock price predictor, customer churn model, NLP sentiment analyzer, image classifier, and a full MLOps pipeline deployed on AWS. All projects use real-world datasets from Kaggle, UCI, and industry partners.

Yes. Our verified digital certificate is recognized by 600+ hiring partners across India, the US, and the EU. We also provide LinkedIn-ready credentials and help you showcase your GitHub portfolio. Many students report interview callbacks within 2 weeks of completing the course.

Every student gets weekly live doubt sessions, access to a private community of 18,000+ learners, bi-weekly 1-on-1 code reviews, and a dedicated mentor for the capstone project. Average response time in our community is under 2 hours.

We offer a 7-day free trial with full access to Module 1 (no credit card required). After enrolling, you're covered by our 30-day money-back guarantee — no questions asked, full refund, no hoops to jump through.

Yes! All video lessons, reading materials, and quizzes are fully mobile-responsive. Our iOS and Android app also lets you download lessons for offline viewing — great for commutes. Coding exercises are desktop/tablet recommended for the best experience.

Absolutely. Our Team & Enterprise plan supports 5–500 learners with custom learning paths, admin analytics dashboards, team invoicing, and dedicated onboarding support. Reach out at enterprise@datasci.dev for a custom quote.
Limited Early-Bird Seats

Your career in data science
starts today.

Join 18,000+ students who transformed their careers. Early-bird pricing ends when the cohort fills.

Enroll Now — Save 42% 30-day money-back · Free 7-day trial
₹17,999 ₹30,999
One-time · Lifetime access · All future updates
180+ Hours 30+ Projects Certificate Community