Start your Data Science
Career in 9 Months with AWS

Our Master Data Science with AWS Program is designed to take you from beginner to expert. Ensuring you land a high-paying job with the guaranteed placement opportunities.

390+
Hours of Living Training
20+
Tools Covered
2+
Capstone Project
300+
Learners Placed

Batch Starting from 20th Jan '25

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Master Data Science with AWS Program

Designed to fit working professionals’ needs.
Flexible batches, including weekends and holidays.
Support available every day from9 AM to 7 PM IST
Resume and career-building sessions.
Alumni status and lifetime placement opportunities.

15LPA

Average Offered Salary

115%

Average Salary Hike

300+

Hiring Partners

100+

Successful Batches

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Program Curriculum

Learn the foundational skills of Statistics, Excel and Python to kickstart your journey in Data Science, including data manipulation, analysis, and visualization techniques.

Probability & Applied Statistics
Calculus, Optimization & Linear Algebra

Exploring Data
Preparing Data
Analysing Data

Pivot tables and pivot charts
Conditional formatting
Remove duplicates
XLOOKUP
IFERROR
MATCH
COUNTBLANK

Basic syntax and data types in Python
Working with variables and operators
Using built-in functions and libraries
Conditional statements: IF Else

Numpy
Pandas
Matplotlib
Seaborn
Data Acquisition

An informative session led by an industry expert to share insights and expertise.

Interview sessions designed to practice and improve interview skills.

Develop strong analytical skills using SQL to effectively query, analyze, and interpret data for data-driven decision-making.

Overview of Database Management Systems, Types of DBMS: Hierarchical, Network,
Relational, and NoSQL, Database Models and Architecture, Introduction to Structured Query
Language (SQL), SQL Syntax and Structure, Data Definition Language (DDL), Creating and Modifying Tables, Data Manipulation Language (DML), Inserting, Updating, and Deleting Data, Data Query Language (DQL): Retrieving Data with SELECT Statements.

Introduction to SQL for Data Analysis,
Understanding Database ,Schema and Structure,
Data Retrieval Techniques: ,SELECT Statements
Filtering Data: WHERE Clause and Logical Operators
Sorting Data: ORDER BY Clause
Aggregating Data: GROUP BY and Aggregate Functions
Using HAVING for Conditional Aggregation
Joining Tables: INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN
Subqueries: Nested Queries for Complex Analysis
Data Transformation: CASE Statements and Common Table Expressions (CTEs)
Time-Series Analysis and Date Functions
Window Functions for Advanced Data Analysis
Best Practices for Writing Efficient SQL Queries

Master the process of building data-driven models using Machine Learning and Artificial Intelligence to solve complex problems and make informed predictions.

Overview of Machine Learning Concepts
Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
Key Terminology: Algorithms, Models, Features, and Labels
The Machine Learning Workflow: Data Collection, Preparation, and Preprocessing
Exploratory Data Analysis (EDA) for Machine Learning
Feature Engineering and Selection Techniques
Training and Testing Datasets: Importance and Best Practices
Common Machine Learning Algorithms:
Linear Regression
Logistic Regression
Decision Trees
Random Forests
Support Vector Machines (SVM)
K-Nearest Neighbors (KNN)
Neural Networks
Model Evaluation Metrics: Accuracy, Precision, Recall, F1 Score, and AUC-ROC
Overfitting and Underfitting: Understanding Bias-Variance Tradeoff
Hyperparameter Tuning and Model Optimization

Overview of Deep Learning Concepts
Key Differences Between Machine Learning and Deep Learning
Neural Networks: Structure and Function
Activation Functions and Their Importance
Training Neural Networks: Backpropagation and Optimization Algorithms
Types of Neural Networks:
Feedforward Neural Networks
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory (LSTM) Networks
Introduction to Natural Language Processing (NLP)
Text Preprocessing Techniques: Tokenization, Lemmatization, and Stemming
Feature Extraction Methods: Bag of Words, TF-IDF, and Word Embeddings (Word2Vec, GloVe)
Deep Learning Architectures for NLP: Sequence-to-Sequence Models
Transformer Architecture and Attention Mechanisms
Common NLP Tasks: Sentiment Analysis, Named Entity Recognition, and Machine Translation
Evaluation Metrics for NLP Models: Precision, Recall, and F1 Score
Frameworks and Libraries for Deep Learning and NLP

An informative session led by an industry expert to share insights and expertise.

Interview sessions designed to practice and improve interview skills.

Generative Models, GANs
Attention Models
Attention
Transformers
HuggingFace
BERT

Spark Architecture
Spark SQL
Spark Core
Spark Streaming

Introduction to Git
Introduction to Version Control
Git and Github basics
Git branching

A Capstone project in Data Science that applies learned concepts to solve real-world problems.

Gain expertise in Data Science, Machine Learning, and AI on AWS to build scalable, efficient models and leverage cloud technology for advanced data-driven solutions.

Module 1: Introduction to Amazon Web Services
Module 2: AWS Compute
Module 3: AWS Networking
Module 4: AWS Storage
Module 5: Databases
Module 6: Monitoring, Optimization, and Serverless
Module 7: Course Summary