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DS 360: Complete Data Science Program (Weekday Batch)

36 Weeks

DS 360: Complete Data Science Program (Weekend Batch)

52 Weeks

DA 360: Complete Data Analytics Program (Weekday Batch)

20 Weeks

DA 360: Complete Data Analytics Program (Weekend Batch)

24 Weeks

Explore Data Science

DS 360: Complete Data Science Program (Weekend Batch)

Master Data Science in 52 Weeks with Mentors from Top Global Companies — Built to Make You Job-Ready from Day One

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10,000+ Enrolled Students

Duration

52 Weeks

Comprehensive learning journey

Learning Mode

Online

Online & Offline options

Live Sessions

200+ Hours

Interactive classes

Projects

10+

Hands-on experience

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Course Curriculum

Milestone 1 3 Weeks

Excel Essentials: From Spreadsheets to Superpowers

"Go beyond basic spreadsheets—Learn to use Excel’s formulas, pivot tables, and dashboards to turn raw data into smart, actionable insights."

Basic Excel

Key Areas of Learning

Introduction to Excel for Data Analysis
Working with Rows, Columns & Cell Formatting
Sorting & Filtering Data
Basic Formulas & Functions
Relative and Absolute Cell Referencing
Data Validation & Drop-down Lists
Basic Charts & Visual Representations
Introduction to Pivot Tables
Basic Error Handling in Formulas
Printing & Page Setup

Advance Excel

Key Areas of Learning

Advanced Formula Writing Techniques
Lookup & Reference Functions
Text Functions for Data Cleaning
Advanced Pivot Tables & Pivot Charts
Logical & Statistical Functions
Excel Data Tables & What-If Analysis
Working with Named Ranges & Dynamic Lists
Automating Tasks with Macros (Intro to VBA)
Creating Interactive Dashboards
Importing & Exporting Data in Excel

Milestone 2 5 Weeks

The SQL Way: From Raw Data to Real Answers

Learn to design efficient databases, write powerful SQL queries, and create systems that turn data into action.

Basic SQL

Key Areas of Learning

Introduction to Databases & Setup
Visualizing Data with ER Diagrams
Data Structuring with Normalization
Building Reliable Databases with ACID
Exploring SQL Data Types
Designing Tables with DDL Commands
Managing Table Data with DML
Controlling Access with DCL
Handling Transactions with TCL
Maintaining Data Accuracy with Constraints
Mastering SQL Operators
Powerful Querying with SQL Clauses

Advance SQL

Key Areas of Learning

Mastering SQL Joins
Mathematical Functions in SQL
Data Type Conversion Functions
Essential Utility Functions
Conditional Logic in SQL
Date & Time Handling
Numeric Processing Functions
String Manipulation Functions
Working with Subqueries
Ranking & Window Functions
Connecting SQL with Python

Milestone 3 5 Weeks

Python Prodigy: From Basics to Brilliance

Learn Python syntax, structures, and advanced features while strengthening your logic-building skills

Python

Key Areas of Learning

Why Python?
Setting Up Your Python IDE
Writing Your First Program – Hello World
Variables & Naming Rules
Working with Strings
Understanding Lists
Introduction to Tuples
Using Sets in Python
Dictionaries – Key-Value Data Pairs
Conditional Statements
Loops – For & While
Built-in Functions – Numbers & Math
Creating Your Own Functions
Modules and Packages
Common Python Errors

Advanced Python

Key Areas of Learning

Smart Coding with List Comprehensions
Working with Files and Data Storage
Debugging Like a Pro
Object-Oriented Programming with Classes
Quick Functions: Lambda, Map & Filter
Text Pattern Matching with Regular Expressions
Managing Libraries with PIP
Automating Excel Tasks with Python
Advanced Flow Control: Iterators & Generators
Reusable Code with Decorators
Object Serialization with Pickle
Integrating JSON for Web & APIs

Milestone 4 4 Weeks

Pandas Powerhouse: From Dataframes to Insights

Master data handling with Pandas "From working with Series and DataFrames to creating pivot tables and analyzing time-based data.

Milestone 5 4 Weeks

Stats & Probability Prodigy: From Fundamentals to Forecasts

Learn probability, counting, and random variables" Then apply them using NumPy, distributions, and real-world simulations. Dive into stats with hands-on hypothesis testing

Statistics & Probability with Numpy- Basic

Key Areas of Learning

Why Probability Matters in Data Science
Understanding Samples, Outcomes & Events
The Building Blocks: Axioms of Probability
Bayes' Theorem & Total Probability
Introduction to Random Variables
Discrete Probability Distributions
Expected Value & Variance: Measuring Uncertainty
Continuous Probability Distributions
Sampling from Distributions
Simulation with NumPy

Statistics & Probability with Numpy - Advanced

Key Areas of Learning

Understanding Samples vs. Populations
Central Limit Theorem Explained
Chi-Square Distribution & Its Use Cases
Estimating Parameters: Point vs. Interval
Maximum Likelihood Estimation (MLE)
Confidence Intervals with Unknown Variance
Working Examples of Estimators
Introduction to Hypothesis Testing
One-Tailed & Two-Tailed Tests
Types of Errors & Decision Making

Milestone 6 5 Weeks

Data Visualization Decoded: Basics to Brilliant Dashboards

Create powerful visuals with charts and custom styling, then use Power BI and Tableau to build dashboards and apply data analytics

Data Visualization using Python

Key Areas of Learning

Reading & Parsing Complex JSON Data
Styling & Formatting Tabular Outputs
Exploring Distributions with Histograms
Summarizing with Box Plots
Quick Recap: Visualization Basics
Pie & Donut Charts for Proportional Data
Stacked & Relative Bar Charts
Stacked Area Plots for Trend Analysis
Scatter Plots for Relationship Mapping
Standard & Grouped Bar Plots
Continuous Variable Comparison Plots
Line Plots for Time Series Data

Data Visualization Using Power BI

Key Areas of Learning

Getting Started with Power BI
Importing, Cleaning & Filtering Data
Creating Basic Visualizations
Designing Reports in Power BI
Interactive Dashboards & Publishing

Data Visualization Using Tableau

Key Areas of Learning

Introduction to Tableau Interface
Connecting & Managing Data Sources
Visual Analytics with Tableau
Applying Basic Predictive Features
Building & Publishing Dashboards

Milestone 7 9 Weeks

ML Maverick: From Fundamentals to Smart Predictions

Learn core ML types: Supervised, Unsupervised and reinforcement while prepping data and building models for prediction, classification and clustering

Introduction to Machine Learning

Key Areas of Learning

Introduction to Machine Learning
Types of Learning in ML
Real-World Applications of ML
The Machine Learning Workflow

Machine Learning: Data Collection

Key Areas of Learning

Types of Data: Structured vs. Unstructured
Modern Data Collection Methods
Ethics in Data Acquisition

Machine Learning: Data Cleaning & Pre-Processing

Key Areas of Learning

Cleaning Data for Accuracy
Transforming Data for Modeling
Feature Engineering Essentials
Handling Imbalanced Data

Machine Learning: Exploratory Data Analysis

Key Areas of Learning

Visualizing Data Effectively
Analyzing Data at Multiple Levels
Understanding Distributions & Relationships
Detecting Patterns & Trends in Data
Evaluating Feature Importance
Ensemble Learning with Random Forests

Machine Learning: Model Building (Supervised Learning)

Key Areas of Learning

Getting Started with Supervised Learning
Predicting with Linear Regression
Binary & Multi-Class Classification with Logistic Regression
Decision Trees for Transparent Modeling
Support Vector Machines (SVM)
Naive Bayes Classifier
Boosting with XGBoost
Instance-Based Learning with K-Nearest Neighbors (KNN)
Forecasting with ARIMA
loying models.

Machine Learning: Model Building (Unsupervised Learning)

Key Areas of Learning

Introduction to Unsupervised Learning
K-Means Clustering: Grouping Similar Data
Hierarchical Clustering: Building Cluster Trees
DBSCAN: Density-Based Clustering
Principal Component Analysis (PCA): Dimensionality Reduction

Introduction to Reinforcement Learning

Key Areas of Learning

Introduction to Reinforcement Learning

Machine Learning: Model Evaluation

Key Areas of Learning

Introduction to Model Evaluation
Evaluating Regression Models

Machine Learning: Hyper Parameter Tuning

Key Areas of Learning

Introduction to Hyperparameter Tuning
Randomized Search Optimization
Grid Search Strategy
Bayesian Optimization
Cross-Validation Techniques
Early Stopping in Model Training

Machine Learning: Model Deployment

Key Areas of Learning

Saving & Reusing Trained Models
Preparing Models for Production
Introduction to MLflow for Model Management
Model Monitoring & Performance Tracking

Milestone 8 3 Weeks

Deep Learning Dynamo: From Neural Nets to Real-World AI

Master deep learning using PyTorch Build neural networks, work with CNNs for images, RNNs and LSTMs for sequences and explore Transformers for NLP and transfer learning

Deep Learning with Pytorch: NN & ANN

Key Areas of Learning

Why Neural Networks?
Building Blocks of Neural Networks
Feedforward Neural Networks Explained
Gradient Descent & Learning Rules
Training Neural Networks
Avoiding Overfitting in Neural Models
PyTorch Fundamentals
Building the Training Pipeline
Implementing Regression Models
Working with Datasets in PyTorch
Advanced Training Concepts

Deep Learning with Pytorch: CNN

Key Areas of Learning

Getting Started with CNNs
Image Filters and Feature Extraction
Understanding the Convolution Layer
Pooling Layers for Dimensionality Reduction

Deep Learning with Pytorch: RNN

Key Areas of Learning

Understanding RNN Architecture
Language Modeling with RNNs
Text Generation with RNNs
Limitations of Traditional RNNs

Deep Learning with Pytorch: LSTM

Key Areas of Learning

Enhancing Memory in RNNs: Introduction to LSTM
Real-World Applications of LSTM Models
Challenges and Limitations of LSTM

Deep Learning with Pytorch: Transformers & GAN

Key Areas of Learning

Introduction to Transformer Models
Understanding Self-Attention Mechanism
Encoder Module: Context Extraction
Decoder Module: Generating Output Sequences
Sequence-to-Sequence Modeling
Transfer Learning with Pre-trained Models

Milestone 9 4 Weeks

Natural Language Processing (Text Processing)

Prep text with preprocessing and embeddings, then use Hugging Face models to tackle advanced NLP tasks easily and accurately

Natural Language Processing (Text Processing)

Key Areas of Learning

Breaking Text into Tokens
Text Normalization Techniques
Removing Irrelevant Words
Root Word Extraction: Stemming & Lemmatization

Natural Language Processing (Text Vectorization and Embedding)

Key Areas of Learning

Text Representation with Bag-of-Words
TF-IDF: Term Frequency–Inverse Document Frequency
Word Embeddings for Semantic Understanding
Sentence-Level Embeddings

Applications of Pre-Trained NLP Models

Key Areas of Learning

Text Classification with Transformers
Language Translation Made Easy
Contextual Question Answering
Summarizing Long-Form Content
Creative Text Generation

Milestone 10 7 Weeks

Computer Vision: Image Pre-Processing

Learn image preprocessing with annotation, resizing, and augmentation, then build models for classification, detection, and segmentation to help machines understand visual data

Computer Vision: Image Pre-Processing

Key Areas of Learning

Image Annotation Essentials
Data Augmentation Strategies
Image Normalization Techniques
Resizing for Model Compatibility

Computer Vision: Image Classification

Key Areas of Learning

Foundations of Convolutional Neural Networks (CNNs)
Deep Learning with Residual Networks (ResNet)
Multi-Scale Learning with Inception Networks
Lightweight Models with MobileNets
Scalable and Efficient Models with EfficientNet

Computer Vision: Object Detection

Key Areas of Learning

Region-Based Detection with Faster R-CNN
Real-Time Detection using YOLO
Pixel-Level Detection with Mask R-CNN
Single Shot Detection with SSD

Computer Vision: Image Segmentation

Key Areas of Learning

Introduction to Semantic Segmentation

Milestone 11 3 Weeks

GenAI Genius: From Prompts to Possibilities

Learn to work with Large Language Models using prompt engineering and fine-tuning. Explore how Generative AI powers real-world applications in content creation, automation, and beyond

Large Language Models (LLMs) & Generative AI

Key Areas of Learning

Understanding Large Language Models
Inside GPT & ChatGPT Architectures
Applications of LLMs Across Industries
Overview of Other Prominent LLMs
Hands-On with GenAI Platforms

Prompt Engineering

Key Areas of Learning

Essentials of Prompt Engineering
How Language Models Understand Prompts
Analyzing and Interpreting Model Output
Designing Powerful Prompts for Specific Tasks
Steering and Controlling Output Behavior

Fine-Tuning Large Language Models

Key Areas of Learning

Foundations of Fine-Tuning
Task-Specific vs Domain Adaptation
Model Customization Techniques
Curating Datasets for Fine-Tuning
Building Fine-Tuning Pipelines with PyTorch
Optimizing Model Performance

DS 360: Complete Data Science Program (Weekend Batch)

Master Data Science in 52 Weeks with Mentors from Top Global Companies — Built to Make You Job-Ready from Day One

Total Course Fee One-time payment

₹ 150,000 Enroll Now

Duration

52 Weeks

Live Sessions

200+ Hours

Learning Mode

Online

Hands-on Projects

10+

Career Target

Data Scientist, Machine Learning Engineer, Senior Data Analyst, AI Specialist, Data Product Manager

Training Features

Instructor-Led Sessions

Experience the power of our instructor-led sessions, where engaging discussions and well-structured content delivery promote mastery of skills and knowledge.

Assignment

Elevate your learning journey with our wide range of assignments, curated to build adaptability and academic excellence.

Real-Life Case Studies

Begin your journey of learning with real-life case studies that illuminate the intersection of theory and reality.

Live Projects

Maximize your skill development with our live projects, providing hands-on experience and personalized guidance from industry professionals in real-time.

App-Based Learning

Stay connected to your studies with our learning app, optimized for both Android and iOS platforms, delivering learning resources at your convenience.

Dedicated Placement Support

Maximize your career opportunities with our dedicated placement assistance, guaranteeing your seamless transition into the professional world.

Tools & Technologies Covered

Learner Support

We are committed to your success. We understand that your learning journey may come with questions, challenges, and the need for assistance along the way. That’s why we have developed a robust and comprehensive learner support system to ensure you have all the resources and assistance you need to excel in your courses.

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Enables flexible working from everywhere.

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Assists you in landing your dream job.

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Build a Portfolio That Gets You Hired.

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After receiving an acceptance letter students can book their seats by paying the Booking amount.

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From startups to global enterprises, our learners power success stories everywhere

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From Learning to Earning — We Make It Happen

Guiding You Beyond the Syllabus.
We believe learning should feel personal, not overwhelming.

That’s why our programs are built around mentorship, not just modules. Whether you're a student exploring your path, a professional upskilling for your next move, or someone restarting after a break, we’re here to guide you with real support, hands-on learning, and skills that actually matter. Because when learning is guided by people who care, your goals stop feeling distant and start becoming real.

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Our mentors come from the real world, not just classrooms. They’ve faced the challenges, learned the ropes and now they’re here to guide you with insights that only experience can teach.

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Every learning path we offer is designed with one goal in mind, To prepare you for real roles, real work, and real opportunities.

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You’ll get to learn by doing — using real tools step by step, just like it’s done on the job. No experience? No problem — we’ll guide you through it.

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Upcoming Batches

DS 360: Complete Data Science Program (Weekday Batch)

18 Jul

Jul 2025

36 Weeks

Batch Size: 30 Students

Certificate: Included

Mode: Online

Seats Available: 8 Left

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