Master Data Science with AWS

Become a AWS certified expert in Data Science, AI &ML and Generative AI.

Enroll now!
390+ Hours

Live Instructor-led Training

2 Live

Capstone Projects

100%

Placement Assistance

20+

Data Science Tools Covered

About Master Data Science with AWS

The future is data and AI driven. CloudThat's comprehensive data science and AI courses equip you with the in-demand skills for today's job market. Our data science and artificial intelligence online courses are designed to provide the foundational knowledge you need to analyze, understand, and harness the potential of data and AI. Whether you're a beginner or looking to advance your current skills, our training caters to all levels. Prepare for industry-recognized certifications like AWS Certified AI Practitioner and unlock new opportunities in the exciting fields of data science and artificial intelligence.

Key Highlights of the program

390+ Hours of Live Instructor-led Training

100% Placement Assistance

Experiential Case Study-based Learning

Training equipped with Hands-on Labs

Weekly Assessments, Hands-on Exercises

Realtime scenario-based Capstone project

Mentorship by industry expert in Key highlights`

Flexible Batch Options on Weekdays & Weekends

Our Learners Work At

Program Curriculum

  • Getting Started with Excel & Python for Data Science
  • 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.
  • 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
  • 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)
  • Model Evaluation Metrics: Accuracy, Precision, Recall, F1 Score, and AUC-ROC
  • 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.
  • 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
  • Module 1: Amazon SageMaker Studio Setup
  • Module 2: Data Processing
  • Module 3: Model Development
  • Module 4: Deployment and Inference
  • Module 5: Monitoring
  • Module 6: Managing SageMaker Studio Resources and Updates
  • A Capstone project focused on applying AWS AI/ML tools and services to solve real-world challenges.
  • Module-1 Fundamentals of ML and AI
  • Module-2 Fundamentals of Generative AI
  • Module-3 AWS Generative AI Services
  • Module-4 Use cases of Generative AI
  • Module-5 Prompt Engineering
  • Module-6 Responsible AI
  • Module-7 AWS AI services
  • Exam Readiness Session
  • An informative session led by an industry expert to share insights and expertise.
  • Interview sessions designed to practice and improve interview skills.
  • A capstone project focused on using AWS AI/ML tools, specifically Generative AI (GenAI), to create content on the AWS platform.

Master Top Tools

Eligibility Criteria of Our Cloud Training and Placement Program

  • Final year students from STEM background (CS, IT, Electronics) with >50% marks
  • Fresh graduates/Postgraduates in STEM (CS, IT, Electronics) with >50% marks
  • Working professionals in any non-IT role and any IT roles

How to Enroll and Get Started?

This program is designed for people with 1 - 12 years of working experience looking to move their career on most in-demand technologies and in Data Science, AI &ML & Generative AI.

01

Fill & Submit Your Application

02

Clear Eligibility Test and Assessment

03

Sign the Program Offer Letter

04

Complete the Program Enrollment

05

Join the Program in Upcoming Batch

Program Fee

The entire Master Data Science with AWS Program fee can be paid in easy installments with EMI benefits. To make the program payment, candidates get the following payment options:

EMI Starting From

INR 4,999* per month

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Why Choose CloudThat?

12+

Years in Business

750K+

Professionals Trained

350+

Projects Delivered

600+

Cloud Certifications

30+

Countries Served

500+

Workforce

Partners

FAQ's

Absolutely! Data science and AI courses are designed to accommodate learners from all backgrounds, including non-technical fields. Many programs offer introductory modules that cover the fundamentals, making it accessible for everyone to build a strong foundation in data science and artificial intelligence.

The right course aligns with your goals and current skill level. Consider your interests: do you want to specialize in machine learning, deep learning, or natural language processing? Research different programs, compare their curricula, and look for reviews from past students. Many platforms offer free trials or introductory modules, allowing you to get a feel for the course before committing.

Most data science and AI courses assume basic mathematical and statistical knowledge, along with some programming experience (usually Python or R). However, many programs offer refresher courses or introductory modules to help you catch up if you lack any prerequisites.

Online courses offer flexibility and convenience, allowing you to learn at your own pace and schedule. They are often more affordable than traditional on-campus programs and provide access to a global community of learners and instructors.

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