Master Data Science with Azure

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

Enroll now!
390+ Hours

Live Instructor-led Training

2 Live

Capstone Projects

100%

Guaranteed Job Opportunities

20+

Data Science Tools Covered

About Master Data Science with Azure

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 Microsoft Certified: Azure Data Scientist Associate 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% Guaranteed Job Opportunities with upto 10 Guaranteed interviews

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

Successful Cloud Training and Placements Guaranteed with Industry Giants

Program Curriculum

Introduction to Mathematics and Statistics

Getting Started with Excel & Python for Data Science

  • 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

Building Data Driven models with ML& AI

  • 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.

Mastering Data Science ML-AI on AZURE

  • Module 01 : Cloud concepts
  • Module 02 : Azure architecture and services
  • Module 03 : Management and governance
  • Module 01 : Explore fundamentals of data
  • Module 02 : Explore fundamentals of relational data in Azure
  • Module 03 : Explore fundamentals of non-relational data in Azure
  • Module 04 : Explore fundamentals of data analytics
  • Module 1 : Microsoft Azure AI Fundamentals: AI Overview
  • Module 2 : Microsoft Azure AI Fundamentals: Computer Vision
  • Module 3 : Microsoft Azure AI Fundamentals: Natural Language Processing
  • Module 4 : Microsoft Azure AI Fundamentals: Document Intelligence and Knowledge Mining
  • Module 5 : Microsoft Azure AI Fundamentals: Generative AI
  • Module 01 : Design a machine learning solution
  • Module 02 : Explore and configure the Azure Machine Learning workspace
  • Module 03 : Experiment with Azure Machine Learning
  • Module 04 : Optimize model training with Azure Machine Learning
  • Module 05 : Manage and evaluate models in Azure Machine Learning
  • Module 06 : Deploy and consume models with Azure Machine Learning
  • Developing a comprehensive AI solution using Microsoft Azure, covering key areas such as computer vision, natural language processing, and generative AI. It includes designing machine learning models, configuring Azure Machine Learning workspaces, optimizing model training, and deploying models for real-world applications. The project aims to provide hands-on experience with Azure’s AI capabilities while ensuring effective management and evaluation of machine learning models.
  • An informative session led by an industry expert to share insights and expertise.
  • Interview sessions designed to practice and improve interview skills.
  • Building a generative AI-powered platform on Azure for creating multimedia content, including text, images, and audio. Using cloud-based AI tools and models, the project will enable automated content generation, streamline creative workflows, and provide users with easy-to-use tools for producing high-quality, custom content. This solution will showcase the capabilities of generative AI in content creation while leveraging the scalability and integration features of cloud platforms.

Build Expertise on Top Technologies and Platforms

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 Azure 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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