Course Includes:
- Duration:8 Weeks
- Lessons:75
- Language:English
- Certificate:yes
Elevate your Career with Data Engineering
Elevate your career with Stint Academy's program designed to equip you with the skills necessary for success and to prepare you for the esteemed Google Cloud Professional Data certification. This comprehensive certificate integrates hands-on labs, providing a dynamic learning experience.
Apply your acquired knowledge through practical, real-world projects, with a focus on key topics such as BigQuery. Expect to gain valuable hands-on experience, reinforcing the concepts covered in the modules and enhancing your readiness for industry challenges.
What You’ll Learn?
- Familiarize yourself with essential machine learning algorithms.
- Gain proficiency in building real-world models using Python.
- Clearly differentiate between what machine learning is and isn't.
- Be prepared for machine learning questions in the Google Certified Data Engineering Exam.
- Acquire in-demand skills guided by both university and industry experts.
- Solidify your knowledge by mastering tools in practical, hands-on projects.
- Develop a profound understanding of crucial concepts in data engineering and machine learning.
Career Outcome:
Data Engineer, Data Scientist, Data Architect, AI Engineer, ML Engineer, Big Data Engineer, IT Research Scientist.
Course Curriculum
Note:Assisted to Real-Time Industry Projects & Case Studies.
Phase 1: Foundation in Programming and Databases (8 Weeks -10 Weeks)
Python Programming - Comprehensive Introduction
- Basics: Syntax, Variables, Data Types, Operators, Comments
- Control Flow: Conditional Statements, Loops, Break, Continue
- Data Structures: Lists, Tuples, Sets, Dictionaries
- Functions: Defining Functions, Arguments, Return, Lambda, Recursion
- Modules, Libraries, and OOP: Importing Modules, Built-in Modules, External Libraries, Object-Oriented Programming (Classes, Inheritance, Encapsulation,Polymorphism)
- Advanced Topics: File Handling, Exception Handling, Regular Expressions,Collections Module, Functional Programming
SQL and Database Management
- Introduction to SQL: Role in Relational Databases, Database Basics, Data Types
- Core SQL Concepts: Queries, SELECT Statement, Filtering, Sorting, Aggregate Functions, GROUP BY, JOINS, Subqueries
- Database Management: INSERT, UPDATE, DELETE Statements, Indexes, Transactions, ACID Properties, Views, Constraints, Normalization, Database Design
- Advanced SQL: Functions, Triggers
Phase 2: Cloud Computing and Azure Data Engineering (3 Weeks- 5 Weeks)
Big Data Processing with Databricks and Apache Spark
- Databricks Unified Analytics Platform: Collaborative Data Science, Integration with Cloud Services
- Apache Spark in Depth: RDDs, DataFrames, Spark SQL, Spark Streaming
- Practical Data Processing: ETL Operations, Data Analysis, MLlib for Machine Learning
- Hands-on Projects: Real-world data processing and analytics projects using Databricks and Apache Spark
Azure Fundamentals and Data Services
- Cloud Computing Basics: Introduction to Azure, Azure Portal, Azure Resource Manager (ARM)
- Azure Data Storage: Azure SQL Database, Azure Data Lake
- Data Processing and Analytics: Azure Databricks, Azure Synapse Analytics,Data Modeling, Performance Tuning
Data Integration and Management
- Azure Data Factory: ETL Processes, Data Integration Runtimes, Monitoring ADF Pipelines
- Data Transformation: Using Azure Data Factory Data Flows, Integration with On-premises Data, Data Quality and Cleansing
- Security and Compliance: Data Encryption, Compliance Standards (GDPR,HIPAA), Azure Security Center
Advanced Data Engineering Concepts
- Monitoring and Troubleshooting: Azure Monitor, Log Analytics,Troubleshooting Data Issues
- Data Governance: Azure Purview for Data Cataloging, Data Lineage, Data Classification, Governance Policies
Rating & Testimonials:
4.4 average rating based on 10 rating
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