Master’s In Data Science

Masters In Data Science
Job Guarantee Program

A 6-Month Data Science Program by Codedge Academy Designed for Industry Readiness
Gain mastery over the complete Data Science and Machine Learning pipeline through hands-on tools, real-world projects, and global mentorship

Industry Ratings

In Association with

No cost EMI starting at

Rs. 4999/Month

Total Program Fee

Rs. 75,000/-

Overview

Master the world of Data Science with this 6-month weekend program designed for ambitious learners from all domains. Delivered through live expert-led classes, interactive LMS, and project-based learning, this course helps you build practical skills in Python, SQL, Machine Learning, Data Analysis, Visualization, and more.

Whether you’re starting from scratch or upskilling as a working professional, you’ll graduate with career confidence, industry-aligned skills, and a dual certification from IBM and XSAV LABS. This program focuses on employability making you ready for top roles in data analytics, data science, and machine learning.

Key Highlights

Real-World & Hands On Applied

Design, Develop and Deploy ML Models to Production

Top Industry
Tools

Frameworks like Kubeflow, MLflow, and Docker.

31+ Case Studies, Mini, Mega and Live Projects

Hands On Implementation

145+ Hours of Detailed Content

Designed with 12 MLOps Experts from FAANG companies

Expert Mentorship

Experienced high-profile ML Experts with guidance that can't be found in textbooks

Career-Boosting Skills

76+ Sessional Quizzes & 29+ Weekly Assignments

Monitor your progress, exploit your edge and focus attention on your shortcomings

Flexible Learning

Designed to fit your schedule - Live classes + On-demand Recordings

Why Learners Love This Program

Empowering learners through real-world Al mastery, industry-grade tools, and global career outcomes.

Advanced Curriculum with Real-World Tools

Master the entire Al pipeline- from Python, Data Science. and Machine Learning to Deep Learning, NLP, and Computer Vision. Our market-aligned curriculum ensures you're ready for both service-based and product-based roles.

Project-Based Learning & Expert Mentorship

Work on real-world industry projects, build capstones, and learn from global AI mentors. Collaborate with peers in cohorts, gain hands-on experience, and receive personalized feedback throughout your journey to ensure you're job-ready and confident.

100% Placement Assistance + Internship

Access our global job portal, resume-building support, mock interviews, and a 3-month guaranteed internship. Launch your dream career with confidence whether it's a startup, MNC, or MAANG-level company we equip you with the tools to succeed anywhere.

What Our Program Offers

12 Month Internship Exp.

Live and Recorded Sessions

Pre-Placement Facility

Totally Practical Exposer

1:1 Doubt Sessions

24°7 Career Support

Lifetime LMS Portal

E-Learning Study Material

90% Placement Rate

Designed by Recruiters

Interactive E-Learning Material

Global Career Opportunities

100% Placement Guarantee

5-Star Reviews by Students

Globally Recognized Certifications

Personality Development Training

All Tools Covered

10kt Learners Placed

Get Interview Preparation

Master Latest Technologies

What You ll learn

Course Curriculum Breakdown

Foundations of Data Science & Python Programming

Introduction to Data Science
1. - What is Data Science?
  - Career Tracks & Industry Applications
  - Types of Analytics: Descriptive, Predictive, Prescriptive
  - Real-world Use Cases
Python Programming Essentials
1. Python Syntax, Variables & Data Types
2. Control Structures: If-Else, Loops, Conditions
3. Functions, Scope, and Recursion
4. Data Structures: Lists, Tuples, Dictionaries, Sets
5. Problem Solving with Python
Working with Data in Python
1. File Handling (Read/Write CSV, Excel, JSON)
2. Working with APIs & JSON Data
3. Exception Handling, Modules & Packages
4. Pythonic Techniques: List Comprehensions, Lambda, Map-Reduce

Data Wrangling & Analysis with Pandas and NumPy

Data Manipulation Using Pandas
1. DataFrames and Series
2. Indexing, Filtering, Merging, Grouping
3. Handling Missing Data & Outliers
4. Reading/Writing Files (CSV, Excel)
Numerical Computing with NumPy
1. Arrays and Broadcasting
2. Mathematical Operations and Statistics
3. Array Slicing and Indexing
Exploratory Data Analysis (EDA)
1. Data Summarization Techniques
2. Trend & Pattern Identification
3. Mini Project: Exploratory Data Report

Statistics, Probability & Data Visualization

Statistical Foundations
1. Descriptive Statistics: Mean, Median, Mode, Variance
2. Probability Distributions (Normal, Binomial, Poisson)
3. Inferential Statistics: Hypothesis Testing, Confidence Intervals
4. p-values, t-tests, Chi-square tests, ANOVA
Business Experimentation
1. A/B Testing Concepts
2. Interpreting Test Results in Business Context
Data Visualization & Storytelling
1. Visualizing Data using Matplotlib and Seaborn
2. Power BI & Tableau Basics
3. Creating Interactive Dashboards
4. Data Storytelling & Communication Skills
5. Mini Project: Statistical Insights & Dashboard Presentation

SQL & Advanced Data Handling in Python

SQL for Data Analysis
1. SELECT, WHERE, GROUP BY, ORDER BY
2. JOINS, Subqueries, Aggregations
3. JOINS, Subqueries, Aggregations
4. Window Functions for Analytics
Advanced Python Techniques
1. Functional Programming (Map, Filter, Reduce)
2. Working with APIs
3. Automation Scripts
Version Control & Collaboration
1. Git & GitHub Basics
2. Commits, Branches, Pull Requests
3. Best Practices in Code Collaboration
Integrated Project
1. End-to-End Data Analysis using SQL + Python + Visualization

Machine Learning & Model Building

Supervised Learning
1. Regression (Linear, Logistic)
2. Classification (KNN, SVM, Decision Trees, Random Forests)
3. Scikit-learn for Model Building
Unsupervised Learning
1. Clustering (K-Means, Hierarchical)
2. Dimensionality Reduction (PCA)
Model Evaluation
1. Confusion Matrix, Precision, Recall, F1 Score
2. ROC-AUC, Accuracy
3. Cross Validation
4. Hyperparameter Tuning (GridSearchCV)
Machine Learning Project
1. Real-World ML Pipeline from Preprocessing to Reporting

Deep Learning & Model Deployment

Deep Learning Foundations
1. Introduction to Neural Networks
2. Activation Functions, Loss Functions
3. TensorFlow & Keras Basics
Advanced Architectures
1. Convolutional Neural Networks (CNNs) for Image Data
2. Recurrent Neural Networks (RNNs) for Time Series
Model Deployment
1. Deploying ML/DL Models using Flask
2. Streamlit for Interactive Model Interfaces
3. End-to-End Deployment Workflow
4. Capstone Project: Build & Deploy a Deep Learning Model

Domain Specializations & Career Preparation

Deep Learning Foundations
1. Introduction to Neural Networks
2. Activation Functions, Loss Functions
3. TensorFlow & Keras Basics
Advanced Architectures
1. Convolutional Neural Networks (CNNs) for Image Data
2. Recurrent Neural Networks (RNNs) for Time Series
Model Deployment
1. Deploying ML/DL Models using Flask
2. Streamlit for Interactive Model Interfaces
3. End-to-End Deployment Workflow
4. Capstone Project: Build & Deploy a Deep Learning Model