Job Oriented Training-Noida-GRAStech
Data Engineering Program
Data Engineering Program
Description
The Data Engineering Program is designed to equip professionals with the skills and knowledge to design, build, and manage data infrastructure. This program covers essential topics like data modeling, ETL (Extract, Transform, Load) processes, data warehousing, and cloud-based data solutions. Participants learn to work with large datasets, optimize data pipelines, and ensure data quality and accessibility for business intelligence and analytics. With hands-on projects and real-world case studies, the program prepares individuals to become proficient data engineers capable of supporting data-driven decision-making in organizations. Ideal for aspiring data professionals, this program provides the foundational and advanced skills necessary to thrive in the rapidly evolving field of data engineering.
What You will Learn?
- Data Engineering
- Python
- SQL
- AWS Solution Architect Associate
- ML
- Big Data
- Security
- AWS/Azure Certification Guidance
Topics for this course
Total learning: 144 Topics
Understanding Cloud Computing Concepts
o Definition and benefits of cloud computing
o Types of cloud computing: Public, Private, Hybrid
o Cloud service models: IaaS, PaaS, SaaS
Overview of Data Engineering
o What is Data Engineering?
o Key responsibilities of a Data Engineer
o The data lifecycle: Ingestion, Storage, Processing, and Access
Data Engineering Tools and Ecosystem
o Introduction to databases, ETL tools, data lakes, and data warehouses
o Overview of popular data engineering tools (Apache Spark, Hadoop, etc.)
Introduction to AWS
o Overview of AWS services and global infrastructure
o AWS Management Console, SDKs, and CLI
o Setting up AWS credentials and configuration
o Basic operations with S3, EC2, and other AWS services
AWS Solution Architect Associate
Securing AWS Account and Resources
o AWS Identity and Access Management (IAM)
o AWS Identity and Access Management Policies (Identity and Resource)
o AWS Permissions Boundary
AWS Basic Networking Concepts
o IP Addressing and Classless Interdomain Routing
o AWS Virtual Private Cloud (VPC)
o AWS Elastic IP and NAT Gateway Concepts
o AWS VPC Security (NACL and SG)
Computing in AWS
o AWS EC2 (Instance Type, Tenancy & Placement Group)
o AWS EC2 Pricing Options
o AWS Lambda
AWS Storage and Types
o AWS Storage Types
o AWS S3 Storage (Objects and Bucket Policy)
o AWS S3 Storage Classes and their Prices
o AWS S3 Transfer Acceleration and Versioning
o AWS Access Poins
o AWS Data Migration (Online & Offline)
AWS Database Services and Types
o AWS Database Portfolio (SQL and No SQL)
o AWS Relational Database Service (RDS)
o AWS Database Caching and Strategies
o AWS DynamoDB
Monitoring AWS Infrastructure and AWS Scaling
o AWS Autoscaling
o AWS Load balancing and Its Types
o AWS Cloudwatch and setting up Cloudwatch Alarms
o AWS Cloudtrail
Automating AWS Infrastructure and Application Creation | Microservices in AWS(Containers)
o AWS Cloudformation(IAC)
o AWS Elastic Beanstalk(PaaS)
o Monolithic and Microservices
o AWS Containers (ECS and EKS)
o AWS Container Lab using Fargate
AWS Advance Networking
o VPC Endpoints
o VPC Peering
o VPN and Direct Connect
o AWS Transit Gateway
AWS Anywhere Services
o AWS Edge Service Concept
o AWS Cloudfront
o AWS Route 53 and Routing Policies
AWS Disaster Recovery
o AWS Backup
o AWS Recovery Strategies
Python Fundamentals
o Data types, variables, and operators
o Control flow: if-else, loops
o Functions, modules, and packages
Working with Data Structures
o Lists, tuples, dictionaries, and sets
o List comprehensions and lambda functions
o Iterators and generators
Python Libraries
Working with Files
o Reading and writing files (CSV, JSON, XML)
o Working with large files and data streaming
Using Pandas for Data Manipulation
o DataFrames and Series
o Data cleaning and preprocessing
o Merging, joining, and concatenating datasets
o Grouping and aggregation
Data Transformation with NumPy
o Handling large datasets with NumPy arrays
o Mathematical operations on arrays
o Broadcasting and vectorization
Introduction to SQL
o Basic SQL queries: SELECT, INSERT, UPDATE, DELETE
o Joining tables and complex queries
o Indexing and query optimization
Using Python with Databases
o Connecting to databases
o Executing SQL queries and handling results in Python
1. Introduction to Machine Learning
What is Machine Learning?
o Definition and types of machine learning (supervised, unsupervised, reinforcement)
o History and applications of machine learning
o Differences between AI, ML, and deep learning
Key Concepts in Machine Learning
o Features, labels, training, testing, and validation sets
o Overfitting vs. underfitting
o Bias-variance tradeoff
Tools and Libraries for Machine Learning
o Introduction to Python libraries: NumPy, Pandas, Matplotlib
o Overview of ML frameworks: Scikit-learn, TensorFlow, PyTorch
2. Data Preprocessing and Exploration
Data Collection and Cleaning
Exploratory Data Analysis (EDA)
Data Splitting and Cross-Validation
o Train-test split
o K-fold cross-validation and stratified sampling
3. Supervised Learning
Regression Algorithms
Classification Algorithms
4. Unsupervised Learning
Relational Databases
o Database design and normalization
o Handling transactions and concurrency
Data Lakes and Data Warehousing
Concepts of data lakes and warehouses
Using AWS S3 or Google Cloud Storage as data lakes
Introduction to data warehouses (Amazon Redshift, Google BigQuery, Snowflake)
Introduction to Big Data
o Challenges and concepts in big data processing
o Distributed computing principles
Working with Apache Spark
o Introduction to Spark and PySpark
o DataFrames and RDDs in Spark
o Spark SQL for querying big data
o Optimizing Spark jobs and tuning performance
Working with Hadoop
o Overview of Hadoop ecosystem
o MapReduce programming model in Python
o Working with HDFS (Hadoop Distributed File System)
AWS Certification Paths with 100% passing support
o AWS Certified Cloud Practitioner
o AWS Certified Solutions Architect (Associate & Professional)
o AWS Certified Developer (Associate & Professional)
o AWS Certified SysOps Administrator (Associate)
o AWS Certified DevOps Engineer (Professional)
o Specialty Certifications
o AWS Certified Big Data
o AWS Certified Security
o AWS Certified Advanced Networking
Opportunities after this course completion
Price: INR 50000 INR 60000
This Course Includes
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Skill level : Advanced
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Duration : 6 Months
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Chapters : 8
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Topics : 144
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Language : English
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Certificate : Yes
