CognitiaX AI · Program

Full Stack Data Science Program

Python • Statistics • Machine Learning • Deep Learning • NLP • MLOps – complete end-to-end Data Science mastery.

Duration: 9–10 monthsOffline: ₹1,30,000Online: ₹1,00,000Next batch: 5th December

Program Highlights

• From Python basics to advanced ML & Deep Learning
• Hands-on projects in regression, classification, NLP & CV
• End-to-end ML & DL projects with deployment

Tools you’ll master

PythonNumPyPandasMatplotlibSeabornScikit-LearnTensorFlow / KerasNLTK / SpaCySQLGit & GitHubDockerMLflowAWS / AzureStreamlitFlask

Detailed Syllabus

Here’s how we take you from basics to a job-ready Data Science.

Python Foundations for Data Science

Environment setup with Anaconda & VS Code
Python syntax, variables, data types & operators
Control flow – if/elif/else, for & while loops
Inbuilt data structures – lists, tuples, sets, dictionaries
Functions, lambda, map, filter & list comprehensions
Modules, packages & virtual environments
File handling, exception handling & logging
OOP in Python – classes, objects, inheritance, polymorphism
Advanced concepts – iterators, generators & decorators

Python Practice & Problem Solving

Function-based coding exercises
List / tuple / dict coding challenges
File & exception handling mini-projects
Real-world Python utility scripts

Data Analysis with Python & SQL

NumPy arrays & numerical operations
Pandas Series & DataFrames – indexing & selection
Data cleaning: missing values, duplicates, outliers
Feature engineering basics – new columns, aggregations, joins
Exploratory Data Analysis (EDA) with Pandas
Visualisation with Matplotlib & Seaborn
Introduction to SQLite / SQL – CRUD operations with Python

Statistics & Probability for Data Science

Descriptive statistics – mean, median, mode, variance, SD
Measures of dispersion – range, IQR, percentiles, quartiles
Correlation & covariance
Probability basics – rules, independent & dependent events
Probability distributions – binomial, Poisson, normal, uniform
Central Limit Theorem & sampling
Inferential statistics – hypothesis testing & p-value
Z test, t test, chi-square & ANOVA – intuition & use cases
Type I & Type II error, confidence intervals & margin of error

Feature Engineering & Advanced EDA

Handling missing data (mean/median/advanced methods)
Outlier detection & treatment techniques
Imbalanced data handling (SMOTE & other techniques)
Categorical encoding – one-hot, label, ordinal, target-guided
Domain-driven feature engineering on real datasets
EDA case studies: flights, wine quality, app store, etc.

Machine Learning Foundations

What is Machine Learning? Types of ML (supervised, unsupervised, etc.)
Model-based vs instance-based learning
Equation of line, plane & hyperplane
Bias-variance, underfitting & overfitting
ML workflow & lifecycle overview

Regression Algorithms

Simple & Multiple Linear Regression – maths & intuition
Cost functions, gradient descent & convergence
Polynomial Regression & feature scaling
Regularisation – Ridge, Lasso & ElasticNet
Evaluation metrics – MSE, MAE, RMSE, R²
Cross-validation, hyperparameter tuning (GridSearch / RandomizedSearch)

Classification Algorithms

Logistic Regression – math intuition & implementation
Performance metrics – accuracy, precision, recall, F1, ROC-AUC
Support Vector Machines (SVM) – margin, kernels, SVR
Naive Bayes – Bayes theorem & variants
K-Nearest Neighbours (KNN) – intuition & optimisation
Decision Trees – entropy, gini, information gain
Tree pruning & handling numerical/categorical features

Ensemble Methods

Bagging vs boosting – concepts & intuition
Random Forests – regression & classification
AdaBoost, Gradient Boosting & XGBoost
Feature importance & model interpretation
End-to-end ensemble project with tuning

Unsupervised Learning & Clustering

Introduction to unsupervised learning
Dimensionality reduction – PCA intuition & implementation
K-Means clustering & elbow / silhouette methods
Hierarchical clustering – dendrograms, linkage methods
DBSCAN clustering & anomaly detection
Isolation Forest & Local Outlier Factor for anomaly detection

End-to-End ML Projects & Deployment

Structuring ML projects – data, src, notebooks, models
Logging & exception handling in projects
Pipelines for data transformation & model training
Model persistence with pickle / joblib
Deployment with Flask / Streamlit
Deploying models on AWS / Azure (Beanstalk, EC2, containers)

Tools: Git, Docker & MLOps Basics

Git & GitHub – repo setup, branching, merge conflicts
Docker fundamentals – images, containers & Dockerfile
Docker Compose & pushing images to Docker Hub
MLflow / DagsHub basics for experiment tracking & versioning
Intro to production-grade ML project lifecycle

Natural Language Processing (Classical)

NLP roadmap & real-world use cases
Tokenisation, stopwords, stemming & lemmatisation
POS tagging & Named Entity Recognition
Text representation – One-Hot, Bag of Words, n-grams
TF-IDF – intuition & implementation
Classical NLP projects – spam/ham, sentiment analysis, etc.

NLP with Word Embeddings

Word embeddings – intuition & need
Word2Vec – CBOW & Skip-Gram
AvgWord2Vec & practical implementation
Text classification with Word2Vec / embeddings
End-to-end sentiment analysis project

Deep Learning & Neural Networks

Why Deep Learning? Perceptron & ANN intuition
Activation functions – Sigmoid, Tanh, ReLU, Leaky ReLU, ELU, Softmax
Loss vs cost functions for regression & classification
Optimisers – SGD, Momentum, RMSProp, Adam, etc.
Vanishing/exploding gradients & weight initialisation
Regularisation – dropout & other techniques

Deep Learning with ANN – End-to-End

Building ANN for classification & regression in Keras
Feature scaling & transformations for ANN
Hyperparameter tuning – layers, neurons, learning rate
Deploying ANN models with Streamlit / web app

CNNs, RNNs, LSTMs & Sequence Models

CNN architecture – convolution, pooling, flattening & FC layers
Image-based DL projects (classification / detection basics)
RNN basics – sequence modelling & limitations
LSTM & GRU – intuition & architecture
Text generation / sequence prediction projects with RNN/LSTM

Advanced NLP with Deep Learning & Transformers (Overview)

Word embeddings with DL – embedding layers
Sequence-to-sequence models & attention mechanism (conceptual)
Transformer architecture – encoder/decoder & self-attention (overview)

Capstone Projects & Interview Preparation

End-to-end ML project with deployment (regression/classification)
NLP project – spam/ham or sentiment analysis with deployment
Deep Learning project – ANN/CNN use case
Best practices for DS interviews & portfolio building
Resume & LinkedIn optimisation for Data Scientist roles

Real-World Projects You’ll Build

• House Price / Flight Price Prediction – Regression Project
• Customer Churn / Credit Risk – Classification Project
• Clustering & Segmentation Project (Unsupervised ML)
• Spam/Ham or Review Sentiment Analysis – NLP Project
• End-to-end ML or DL Project with AWS / Azure Deployment

+ many more industry-relevant projects included in the program.