Data Science Master Course

Master Data Science with Python, Statistics, Machine Learning, Deep Learning, and Real-world projects. Become a job-ready Data Scientist.

Duration 5 Months (180 Hours)

Mode Live Online / Offline

4,200+ Students

380+ Partners

90% Placement

Fresher / Entry Level

₹5 - 8 LPA

0-2 years experience

Junior Data Scientist
Data Analyst Trainee

⭐ Most Common

Mid Level

₹9 - 18 LPA

2-5 years experience

Data Scientist
Machine Learning Engineer

Senior / Expert Level

₹20 - 45+ LPA

5+ years experience

Lead Data Scientist
AI Research Scientist

Data Scientist

Machine Learning Engineer

Data Analyst

Business Intelligence Analyst

Deep Learning Engineer

NLP Engineer

Python

Pandas & NumPy

Machine Learning

Deep Learning

Statistics

Data Visualization

SQL

TensorFlow/Keras

NLP

Git

Module 1: Python Programming for Data Science

Python Basics - Variables, Data Types, Operators
Control Flow - if, else, loops
Functions & Lambda Expressions
Data Structures - Lists, Tuples, Dictionaries, Sets
File Handling - CSV, JSON, Excel
NumPy - Arrays, Mathematical Operations
Pandas - Series, DataFrame, Data Manipulation
Exception Handling & Debugging
Object Oriented Programming in Python

Module 2: Mathematics & Statistics

Linear Algebra - Vectors, Matrices, Eigenvalues
Calculus - Derivatives, Integrals, Gradients
Descriptive Statistics - Mean, Median, Mode, Variance
Probability - Distributions, Bayes Theorem
Inferential Statistics - Hypothesis Testing
Correlation & Covariance
Normal Distribution, Central Limit Theorem
ANOVA & Chi-Square Tests

Module 3: Data Manipulation & Visualization

Matplotlib - Line, Bar, Scatter, Histogram Plots
Seaborn - Heatmaps, Pairplots, Boxplots
Plotly - Interactive Dashboards
Exploratory Data Analysis (EDA)
Handling Missing Values & Outliers
Data Transformation & Scaling

Module 4: Machine Learning - Supervised Learning

Introduction to Machine Learning
Linear Regression - Simple & Multiple
Logistic Regression for Classification
Decision Trees & Random Forest
Support Vector Machines (SVM)
K-Nearest Neighbors (KNN)
Naive Bayes Classifier
Gradient Boosting - XGBoost, LightGBM

Module 5: Machine Learning - Unsupervised Learning

Clustering - K-Means, Hierarchical
DBSCAN & Gaussian Mixture Models
Principal Component Analysis (PCA)
t-SNE & UMAP
Anomaly Detection

Module 6: Feature Engineering & Model Selection

Feature Scaling - Standardization, Normalization
Feature Encoding - One-Hot, Label Encoding
Feature Selection Techniques
Handling Imbalanced Data
Model Evaluation Metrics - Accuracy, Precision, Recall, F1
Cross-Validation & Hyperparameter Tuning
Ensemble Methods - Bagging, Boosting, Stacking

Module 7: Deep Learning & Neural Networks

Introduction to Neural Networks
Activation Functions - ReLU, Sigmoid, Tanh
Backpropagation & Optimization
TensorFlow & Keras Basics
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN, LSTM)
Transfer Learning & Pre-trained Models
Model Deployment with TensorFlow Serving

Module 8: Natural Language Processing (NLP)

Text Preprocessing - Tokenization, Stemming, Lemmatization
Bag of Words & TF-IDF
Word Embeddings - Word2Vec, GloVe
Sentiment Analysis
Named Entity Recognition (NER)
Text Classification & Clustering
Transformers & BERT

Module 9: Big Data & Cloud for Data Science

Introduction to Big Data & Hadoop
Apache Spark for Data Science
SQL for Data Science
AWS Cloud - S3, EC2, SageMaker
MongoDB & NoSQL Databases
Data Pipeline with Airflow

Module 10: Real-Time Projects

Project 1: Sales Prediction using Regression
Project 2: Customer Churn Prediction
Project 3: Image Classification using CNN
Project 4: Sentiment Analysis on Reviews
Project 5: Credit Card Fraud Detection
Project 6: Recommendation System
Capstone Project - End to End Data Science Solution

Expert Trainers

Learn from industry professionals with 10+ years of Data Science experience

Hands-on Projects

Work on 7+ real-time Data Science projects with live datasets

Industry Certification

Get globally recognized Data Science certification

100% Placement Support

Tie-ups with 380+ companies for Data Science roles

Resume Building

Professional resume & portfolio with Data Science projects

Mock Interviews

Regular mock interviews with detailed feedback

Customer Churn Prediction

Build a machine learning model to predict customer churn for a telecom company using classification algorithms.

Python Pandas Scikit-learn XGBoost Matplotlib

Image Classification with CNN

Create a deep learning model to classify images into multiple categories using Convolutional Neural Networks.

Python TensorFlow Keras CNN OpenCV

Recommendation System

Develop a personalized recommendation system using collaborative filtering and matrix factorization techniques.

Python Pandas Surprise Flask NLP

Placement Support Includes:

Resume & LinkedIn Profile Building
Aptitude & Technical Training
Mock Interviews with Industry Experts
Soft Skills & Communication Training

Our Hiring Partners:

500+ Hiring Partners
Unlimited Interview Opportunities
Job Portal Access
Life-long Placement Support

What are the prerequisites for Data Science course?

Basic programming knowledge is helpful. We cover Python from scratch, so no prior experience is required.

What is the duration of the course?

The course duration is 5 months (180 hours) with flexible batch timings.

Will I learn both ML and Deep Learning?

Yes, the course covers Machine Learning, Deep Learning, NLP, and Big Data technologies.

What projects will I build?

You will build 7+ projects including Churn Prediction, Image Classification, and Recommendation System.

Is placement assistance provided?

Yes, we provide 100% placement assistance with 380+ hiring partners.

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