Enrolling now · Beginner Data Science

Become a Data Scientist in 16 Weeks

Name: Data Science & AI
Price: 45999 INR
Availability: InStock

Not $2,000. Not 6 months of theory. Not pre-recorded videos. 100% live mentor-led classes. Ship 10+ real ML projects and become the most hireable data scientist in the room.

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Enroll now · $499.00 arrow_forward description See the curriculum

starstarstarstarstar4.9 · 127 reviews play_lesson48 live classes schedule16 Weeks

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10+

projects you ship

schedule16 Weeks

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starRated 5.0 on Google verifiedCertificate on completion all_inclusiveLifetime access forumMentor support deployed_codeBuild-first projects cast_for_educationLive, mentor-led classes

auto_stories What you'll master

12 modules — from foundations to production

A structured, build-first path: master the fundamentals and the tooling, then go deep — finishing with a full-stack capstone you can show anyone.

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Module 01

Python Foundations

The language every data scientist speaks

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Module 02

Statistics & Probability

The mathematical intuition behind every model

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Module 03

Exploratory Data Analysis

Uncover stories hiding in messy data

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Module 04

Supervised Learning — Regression

Predict numbers from data

Supervised Learning — Classification

Predict categories & make decisions

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Module 06

Unsupervised Learning

Find hidden patterns without labels

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Module 07

NLP & Text Analytics

Turn text into structured insights

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Module 08

SQL & Databases for Data Science

Query real databases like a pro

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Module 09

Model Deployment & MLOps

Ship models into production

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Module 10

Deep Learning & Neural Networks

From perceptrons to modern neural nets

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Module 11

Time Series & Forecasting

Predict what happens next

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Module 12Capstone

Capstone Project

Build an end-to-end data science solution

format_list_numbered The full curriculum

Every module, topic by topic

Expand any module to see exactly what you'll cover — 12 modules and 77+ topics, with hands-on builds throughout and a full-stack capstone to finish.

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modules

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77+

topics

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hands-on

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Core Python

chevron_right Variables, data types & operators — the building blocks
chevron_right Control flow & functions — logic that drives programs
chevron_right Lists, dicts & comprehensions — Python data structures
chevron_right File I/O & error handling — working with real data

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Data Stack

chevron_right NumPy for numerical computing — arrays, broadcasting & vectorization
chevron_right pandas for data manipulation — DataFrames, groupby & merging
chevron_right Jupyter Notebooks — interactive data exploration

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Hands-on

Set up your data science environment and wrangle your first real-world dataset.

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Descriptive Stats

chevron_right Central tendency & spread — mean, median, variance, std dev
chevron_right Distributions — normal, binomial, Poisson
chevron_right Correlation & covariance — measuring relationships

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Inferential Stats

chevron_right Hypothesis testing — t-tests, chi-square, ANOVA
chevron_right Confidence intervals — quantifying uncertainty
chevron_right p-values & significance — making data-driven decisions

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Hands-on

Perform a full statistical analysis on a real business dataset.

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Data Wrangling

chevron_right Handling missing data — imputation strategies
chevron_right Outlier detection — identifying anomalies
chevron_right Feature engineering — creating meaningful features

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Visualization

chevron_right Matplotlib & Seaborn — static, publication-quality plots
chevron_right Plotly for interactivity — dashboards & dynamic charts
chevron_right Data storytelling — presenting insights that drive action

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Hands-on

Build a complete EDA report with actionable insights.

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Models

chevron_right Linear & polynomial regression — the classics, done right
chevron_right Ridge, Lasso & ElasticNet — regularization for better models
chevron_right Decision tree & random forest regression — non-linear power

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Evaluation

chevron_right MAE, MSE, RMSE, R² — picking the right metric
chevron_right Cross-validation — reliable model assessment
chevron_right Hyperparameter tuning — GridSearch & RandomSearch

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Hands-on

Build a house-price prediction model and deploy it as an API.

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Models

chevron_right Logistic regression — the workhorse of classification
chevron_right SVM & k-NN — distance-based classifiers
chevron_right Random forests & gradient boosting — ensemble power
chevron_right XGBoost & LightGBM — competition-winning models

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Advanced

chevron_right Confusion matrix & ROC/AUC — beyond simple accuracy
chevron_right Handling imbalanced data — SMOTE & class weights
chevron_right Feature importance — understanding model decisions

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Hands-on

Build a customer churn predictor with business-ready metrics.

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Clustering

chevron_right K-Means & K-Medoids — partitioning algorithms
chevron_right DBSCAN & hierarchical clustering — density-based discovery
chevron_right Silhouette analysis — evaluating cluster quality

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Dimensionality Reduction

chevron_right PCA — principal component analysis
chevron_right t-SNE & UMAP — visualizing high-dimensional data

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Hands-on

Segment real customers for a marketing campaign.

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Text Processing

chevron_right Tokenization & stemming — breaking text into features
chevron_right TF-IDF & bag of words — classic text representations
chevron_right Sentiment analysis — opinion mining at scale

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Modern NLP

chevron_right Word embeddings — Word2Vec & GloVe
chevron_right Text classification — spam, sentiment, topic detection

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Hands-on

Build a product-review sentiment classifier.

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Core SQL

chevron_right SELECT, JOIN, GROUP BY — the essential toolkit
chevron_right Window functions — advanced analytics in SQL
chevron_right Subqueries & CTEs — complex query patterns

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Integration

chevron_right Python + SQL together — SQLAlchemy & pandas integration
chevron_right Database design basics — normalization & indexing

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Hands-on

Analyse a multi-table business database and surface key KPIs.

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Deployment

chevron_right FastAPI & Flask — serve predictions as REST APIs
chevron_right Docker basics — containerize your ML pipeline
chevron_right Streamlit dashboards — interactive ML web apps

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MLOps

chevron_right Model versioning — tracking experiments with MLflow
chevron_right CI/CD for ML — automated retraining pipelines

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Hands-on

Deploy your best model as a live API with monitoring.

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Neural Network Basics

chevron_right Perceptrons & activation functions — how a neuron decides
chevron_right Backpropagation & gradient descent — how networks learn
chevron_right Overfitting, dropout & regularisation — models that generalise
chevron_right Building networks with Keras / PyTorch — hands-on frameworks

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Applied Deep Learning

chevron_right CNNs for image classification — computer vision essentials
chevron_right Transfer learning with pre-trained models — state-of-the-art, fast
chevron_right Intro to transformers & embeddings — the tech behind LLMs

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Hands-on

Train an image classifier with transfer learning and compare it against a classical ML baseline.

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Time Series Foundations

chevron_right Trends, seasonality & noise — reading time-based data
chevron_right Resampling, rolling windows & lags — feature engineering over time
chevron_right Stationarity & decomposition — preparing data for models

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Forecasting Models

chevron_right ARIMA & exponential smoothing — classical forecasting
chevron_right Prophet for business forecasting — fast, explainable forecasts
chevron_right ML models for forecasting — gradient boosting on time features
chevron_right Evaluating forecasts — MAE, MAPE & back-testing

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Hands-on

Forecast monthly sales for a retail dataset and present the forecast with confidence intervals.

Everything from all nine modules, brought together into one comprehensive, real-world project.

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Project Scope

chevron_right Real Business Problem — define one that needs data science
chevron_right Data Pipeline — collection, cleaning & feature engineering

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What You Build

chevron_right Complete EDA & statistical analysis — deep data understanding · Modules 2–3
chevron_right ML Model Pipeline — train, tune & evaluate · Modules 4–6
chevron_right NLP Component — text analysis if applicable · Module 7
chevron_right SQL Analytics — database queries & reporting · Module 8
chevron_right Deployed API/Dashboard — live, shareable output · Module 9

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Deliverables

chevron_right Working ML Product — a deployed, usable data science solution
chevron_right Jupyter Notebook — documented analysis + code
chevron_right Demo — presentation or video walkthrough
chevron_right GitHub Repo — clean, reproducible code

Tools you'll work with

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Real learners, in their own words: what they learned, what they built and how the classes felt.

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Built live in class

Real products you’ll build — not toy demos

Every project is working software you build live with the mentor — and you keep the full source code.

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