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Manual TestingWe will cover the topics
What is Manual testing
Verification and Validation
Software Development Life Cycle and its Models
Agile model
Waterfall model
Why testing
Testing life cycle
Bug Life Cycle
Types of testing
Testing techniques
Boundary value analysis
Equivalence class partitioning
Decision Table based testing
State transition
Error guessing
UI Automation using Selenium
/ Cucumber
Selenium WebDriver Testing using Java
Manual testing
Process
Disadvantages
Automation testing
Process
Disadvantages
What can be automated in an application
How to plan automation tool for a project
Selenium and its advantages
Project setup
Maven, Selenium and maven integration
Project structure
Automation process
Automation life Cycle.
Identify test cases that need to be automated
Authoring the scripts.
Executing the scripts,
Selenium WebDriver Testing using Java
Manual testing
Process
Disadvantages
Automation testing
Process
Disadvantages
What can be automated in an application
How to plan automation tool for a project
Selenium and its advantages
Project setup
Maven, Selenium and maven integration
Project structure
Automation process
Automation life Cycle.
Identify test cases that need to be automated
Authoring the scripts.
Executing the scripts,
TestNG
What is TestNG?
Install TestNG
Features in TestNG
Assertions
TestNG class
TestNG reports
Synchronizations [waits].
Implicit
Explicit
Fluent
Framework Setup
Hybrid framework
Page Object Model
Locating Elements in WebDriver.
Handling Elements.
Browser navigation.
Running test in multiple browsers.
Handling JavaScript alerts.
Handling multiple frames.
Handling multiple windows.
Capturing Screenshots.
Handling Keyboard and mouse events.
Handling auto suggestions.
Handling Web tables.
Finding Broken Links.
File upload and Download.
WebDriver - Framework
Introduction to various Frameworks.
Data Driven tests using POI
Reading, Writing data into Excel.
Database Connection (JDBC).
Page Object Model Framework (POM).
TestNG configurations
Configuring Test Suits.
Passing parameters to Tests.
Parallel Test Execution Capability.
Re-run failed test Scripts.
Attributes of @Test
Running TestNG suite from command prompt
Cucumber - Framework
Overview of BDD, TDD.
Cucumber Project Setup.
Gherkin Keywords.
Working with simple Scenario.
Cucumber options.
Continuous integration tool (Jenkins)
Configuring Jenkins.
Executing the windows commands in Jenkins free.
Creating a Maven Job and Scheduling the jobs.
Manage Plug -Ins.
GitHub
What is the version control system?
What is GitHub? Git Commands
Pushing our project into GitHub.
Git vs GitHub.
Api Testing Essentials
Web Services Testing Introduction.
Why do we need web Service?
Types of Web Services.
SOAP.
REST.
Difference between SOAP AND REST More.
What is API.
What is REST API.
Why REST is Architecture.
URL vs URI vs API.
HTTP Introduction
HTTP Methods: POST, PUT, GET, DELETE, PATCH
POSTMAN tool Introduction.
Practice different APIs in POSTMAN tool.
Create – Collections in POSTMAN tool.
Collection Variables vs Global Variables.
Cucumber - Framework
Overview of BDD, TDD.
Cucumber Project Setup.
Gherkin Keywords.
Working with simple Scenario.
Cucumber options.
Explore Header.
Header Types..
Fixed.
Dynamic Headers.
Parameters
Different types of Parameters.
Path Parameter.
Query Parameter.
Header Parameter.
Body parameter.
Different Authorizations in Postman.
HTTP Status Code.
Informational.
Success.
BI- direction.
Client Said Error.
Server said Error.
API Automation Using Karate
/ Rest Assured
Introduction to API Automation
Overview of API Automation
Importance of API testing
Benefits of automation in API testing
Introduction to Karate and Rest Assured
What is Karate
What is Rest Assured
Key differences and use cases
Installing and Configuring Karate
Prerequisites and dependencies
Installing Karate with Maven/Gradle
Configuring Karate in your IDE
Installing and Configuring Rest Assured
Prerequisites and dependencies
Adding Rest Assured to Maven/Gradle
Configuring Rest Assured in your IDE
Karate Basics
Karate Overview
Karate’s architecture and components
Understanding Karate DSL (Domain Specific Language)
Writing Your First Karate Test
Creating feature files
Writing basic test scenarios using Gherkin syntax
Executing Karate Tests
Running tests from the command line
Integrating with build tools (Maven/Gradle)
Rest Assured Basics
Rest Assured Overview
Rest Assured architecture and components
Understanding Rest Assured DSL
Writing Your First Rest Assured Test
Constructing and sending HTTP requests
Validating responses using Rest Assured
Running Rest Assured Tests
Integrating with JUnit/TestNG
Executing tests from the command line
Advanced Karate Features
Data-Driven Testing
Using scenario outlines and examples
Parameterizing tests
Handling API Responses
Validating JSON and XML responses
Database Connection (JDBC).
Mocking and Stubbing
Creating mock services
Stubbing responses for testing
Cucumber - Rest Assured Basics
Advanced Request Handling
Customizing request headers and parameters
Handling authentication and authorization
Response Validation
Using advanced matchers for validation
Parsing and validating complex JSON/XML responses
Data-Driven Testing
Parameterizing tests using data providers
Running tests with different datasets
Mobile Automation using
Appium
Introduction to Mobile Automation
Overview of Mobile Automation
Importance of mobile testing
Benefits of automation in mobile testing
Introduction to Appium
What is Appium?
Key features and advantages
Appium architecture
Advanced Appium Features
Handling Different Mobile Contexts
Native vs. WebView contexts
Switching between contexts
Managing Mobile Devices
Working with emulators vs. real devices
Handling multiple devices in parallel
Gesture and Action Automation
Simulating gestures (swipes, taps, etc.)
Handling complex user interactions
Setting Up the Environment
Installing Appium
Prerequisites and dependencies
Installation on different operating systems (Windows, macOS, Linux)
Configuring Appium
Setting up Appium server
Configuring Android and iOS environments
Connecting mobile devices/emulators
Integrating with IDEs
Configuring IDEs (e.g., IntelliJ, Eclipse)
Setting up Appium plugins and extensions
Appium with Android
Android-Specific Configuration
Setting up Android SDK
Configuring Appium for Android devices/emulators
Writing and Running Android Tests
HTTP Methods: Creating test cases for Android apps
Debugging and troubleshooting Android tests
Appium Basics
Understanding Appium’s Architecture
Appium server, client, and drivers
How Appium interacts with mobile apps
Writing Your First Test
Basic test structure
Using Appium’s client libraries (Java, Python, JavaScript, etc.)
Locating Elements
Identifying UI elements
Using different locator strategies (ID, XPath, ClassName, etc.)
Appium with iOS
iOS-Specific Configuration
Setting up Xcode and iOS SDK
Configuring Appium for iOS devices/simulators
Writing and Running iOS Tests
Creating test cases for iOS apps
Debugging and troubleshooting iOS tests
Integrating Appium with CI/CD
Continuous Integration Tools
Integrating Appium with Jenkins, GitLab CI, etc.
Setting up automated test execution
Test Reporting and Analytics
Generating and interpreting test reports
Integrating with reporting tools (Allure, ReportPortal)
Locating Elements
Identifying UI elements
Using different locator strategies (ID, XPath, ClassName, etc.)
Best Practices and Troubleshooting
Best Practices
Writing maintainable and scalable tests
Handling dynamic elements and synchronization issues
Common Issues and Solutions
Troubleshooting common Appium issues
Debugging and resolving test failures
Languages - Core Java/
Python
Introduction to Core Java
Overview of Java
History and evolution of Java
Features and benefits of Java
Setting Up the Java Environment
Installing Java Development Kit (JDK)
Setting up Integrated Development Environment (IDE) (Eclipse, IntelliJ IDEA)
Configuring environment variables
Introduction to Python
Overview of Python
History and evolution of Python statements)
Features and benefits of Python
Setting Up the Python Environment
Installing Python and IDEs (PyCharm, VS Code)
Configuring virtual environments and package management (pip, virtualenv)
Java Fundamentals
Basic Syntax and Structure
Java syntax basics (data types, operators, control statements)
Writing and running your first Java program
Object-Oriented Programming (OOP) Concepts
Classes and objects
Inheritance, polymorphism, encapsulation, and abstraction
Java Data Structures
Arrays and Collections (List, Set, Map)
Inheritance, polymorphism, encapsulation, and abstraction
Python Fundamentals
Basic Syntax and Structure
Python syntax basics (data types, operators, control statements)
Writing and running your first Python program
Object-Oriented Programming (OOP) Concepts
Classes and objects
Inheritance, polymorphism, encapsulation, and abstraction
Python Data Structures
Lists, tuples, sets, and dictionaries
Working with Python collections and comprehensions
Advanced Java Concepts
Exception Handling
Understanding exceptions and errors
Try-catch blocks and exception hierarchy
Custom exceptions
File I/O
Reading from and writing to files
Working with file streams and readers/writers
Multithreading and Concurrency
Threads and runnable interface
Synchronization and thread safety
Executors and concurrent utilities
Advanced Python Concepts
Exception Handling
Understanding exceptions and errors
Try-except blocks and custom exceptions
File I/O
Reading from and writing to files
Working with file operations and context managers
Multithreading and Concurrency
Threads and the threading module
Concurrent programming with asyncio and multiprocessing
Java APIs and Libraries
Working with Java Standard Library
Commonly used libraries (java.lang, java.util)
Date and time API
Introduction to Java Streams and Lambdas
Streams API basics
Lambda expressions and functional interfaces
Networking and Sockets
Basic networking concepts
Implementing client-server communication
Python Libraries and Frameworks
Working with Python Standard Library
Commonly used libraries (math, datetime, sys)
Introduction to Python’s built-in modules
Data Analysis and Visualization
Introduction to NumPy, pandas, and Matplotlib
Basic data manipulation and visualization
Web Development with Python
Basics of Flask and Django
Building a simple web application
Java Best Practices and Design Patterns
Best Practices
Coding standards and conventions
Code optimization and performance tuning
Design Patterns
Common design patterns (Singleton, Factory, Observer, etc.)
Implementing design patterns in Java
Python Best Practices and Design Patterns
Best Practices
Coding standards and conventions
Code optimization and performance tuning
Design Patterns
Common design patterns in Python (Singleton, Factory, Observer)
Implementing design patterns in Python
Real-World Applications
Building Java Applications
Developing desktop applications
Introduction to JavaFX for GUI applications
Java in Web Development
Basics of Java Servlets and JSP
Introduction to Spring Framework
Real-World Applications
Building Python Applications
Developing desktop and command-line applications
Working with APIs and web scraping
Python in Data Science and Machine Learning
Introduction to scikit-learn and TensorFlow
Basics of machine learning models and algorithms
NetworkNetwork and Types
LAN, MAN, CAN, WAN
Internet
Devices on Internet
IP Address
IPv4
IPv6
Error Control
Flow Control
OSI Model
Application Layer
Presentation Layer
Session Layer
Transport Layer
Network Layer
Data Link Layer
Physical Layer
Linux Commands for
CybersecurityCreating folder and files
Edit files and file operations
Echo, List, pwd, sudo, hostname, variables
Find, grep, tail, wget, gzip, ps, wc, escape characters, save output to a file
Sed, tee, awk, ping, curl, pipes, ssh
Functions, Loops, If, Cases.
Penetration TestingPenetration Tools
NMAP
BurpSuite
Wireshark
OWASP Top 10
WEB
API
Penetration Stages
Pre -engagement
Reconnaissance
Vulnerability Analysis
Exploitation
Reporting
Remediation
Mastering DevOps: From Principles to Practice
Introduction to DevOps: Principles and Practices
Overview of DevOps
Key principles: Collaboration, Automation, and Continuous Improvement
Benefits of implementing DevOps
Understanding Continuous Integration and Continuous Deployment (CI/CD)
CI/CD pipeline concepts
Tools and technologies for CI/CD
Best practices for CI/CD implementation
Monitoring and Logging: Ensuring System Reliability
Importance of monitoring and logging
Tools for monitoring: Prometheus, Grafana
Log management with ELK stack (Elasticsearch, Logstash, Kibana)
Containerization and Orchestration: Docker and Kubernetes
Basics of Docker and containerization
Introduction to Kubernetes and container orchestration
Deploying and managing containers with Kubernetes
Optimizing Infrastructure and Security
Security in DevOps: Integrating Security Practices
Overview of DevSecOps
Tools and techniques for securing applications and infrastructure
Best practices for continuous security integration
Infrastructure as Code (IaC): Automating Your Infrastructure
Introduction to IaC
Popular IaC tools: Terraform, Ansible, and Puppet
Implementing and managing IaC
Effective DevOps Collaboration
Collaboration and Communication in DevOps Teams
Enhancing team collaboration with DevOps
Tools for communication and collaboration: Slack, Jira
Effective DevOps culture and practices
DevOps Metrics and KPIs: Measuring Success
Key metrics for DevOps performance
Analyzing and interpreting DevOps KPIs
Improving performance based on metrics
Future-Proof Your Skills
Real-World DevOps Case Studies and Best Practices
Success stories from companies implementing DevOps
Lessons learned and best practices
Practical tips for overcoming common challenges
Future Trends in DevOps: What’s Next
Emerging technologies and practices in DevOps
Impact of AI and machine learning on DevOps
Preparing for the future of DevOps
Introduction to AWSOverview of AWS
Introduction to Amazon Web Services, its importance in the cloud industry, and a high-level overview of AWS services.
AWS Global Infrastructure: Regions, Availability Zones, Edge Locations
Core Services Overview: Compute, Storage, Database, Networking, and Content Delivery
Setting Up Your AWS Account
Guide on creating and configuring an AWS account
Creating an AWS Account
IAM Users and Roles: Setting up Identity and Access Management
Billing and Cost Management: Overview of AWS Free Tier and budget alerts
Compute
ServicesAmazon EC2
Detailed exploration of Elastic Compute Cloud (EC2).
Instance Types: General Purpose, Compute Optimized, Memory Optimized
Scaling EC2: Auto Scaling, Elastic Load Balancing (ELB)
AWS Lambda
Introduction to serverless computing with AWS Lambda.
Function Basics: Triggers, Execution Role, Basic Configuration
Use Cases and Examples: Event-driven computing, Cron jobs alerts
Storage
ServicesAmazon S3
Deep dive into Simple Storage Service (S3).
Buckets and Objects: Creation, Storage Classes, Versioning
Access Control: Bucket Policies, IAM Policies
Data Management: Lifecycle Policies, Replication
Amazon EBS
Understanding Elastic Block Store (EBS).
Volume Types: General Purpose, Provisioned IOPS, Magnetic
Snapshots and Backup: Creating and Managing Snapshots alerts
Database
ServicesAmazon RDS
Relational Database Service (RDS) fundamentals.
Database Engines: MySQL, PostgreSQL, MariaDB, Oracle, SQL Server
Database Management: Backup and Restore, Multi-AZ Deployments, Read Replicas
Amazon DynamoDB
NoSQL database service overview.
Tables and Items: Partition Key, Sort Key, Indexes
Operations and Performance: Query, Scan, Read/Write Capacity Units
Networking and Content
DeliveryAmazon VPC
Virtual Private Cloud (VPC) basics.
Network Architecture: Subnets, Route Tables, Internet Gateway
Security: Network ACLs, Security Groups, VPC Peering
Amazon CloudFront
Content Delivery Network (CDN) services
Distribution: Web and RTMP Distribution
Caching and Security: Edge Locations, Geo Restriction, SSL/TLS
Security, Identity, and
ComplianceAWS Identity and Access Management (IAM)
Comprehensive guide to IAM.
User and Group Management: Creating Users, Groups, and Policies
Role-Based Access Control: Roles, Trust Policies, Cross-Account Access
AWS Key Management Service (KMS)
Managing encryption keys.
Key Creation and Management: Customer Master Keys (CMKs), Key Policies
Data Encryption: Encrypting Data at Rest, Data in Transit
Monitoring and
ManagementAmazon CloudWatch
Monitoring AWS resources and applications.
Metrics and Alarms: Setting Up Custom Metrics and Alarms
Logs and Events: CloudWatch Logs, Event Rules
AWS CloudTrail
Tracking user activity and API usage.
Logging and Monitoring: Event History, CloudTrail Insights
Security Analysis: Detecting Unusual Activity, Compliance Audits
Architecting on AWS
Well-Architected Framework
Principles for designing reliable, secure, efficient, and cost-effective systems.
Five Pillars: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization
Best Practices: Design Principles, Case Studies
High Availability and Fault Tolerance
Designing resilient systems.
Disaster Recovery: Backup Strategies, Multi-AZ and Multi-Region Deployments
Load Balancing: ELB, Auto Scaling Groups
Introduction to Data
ScienceOverview of Data Science
Introduction to data science, its importance, and its applications in various industries.
Data Science Lifecycle : Data Collection, Data Cleaning, Data Exploration, Data Modeling, Model Deployment, Model Maintenance.
Roles in Data Science: Data Scientist, Data Analyst, Data Engineer, Machine Learning Engineer.
Introduction to Python for Data Science
Basic Python programming skills essential for data science.
Python Basics: Data Types, Control Structures, Functions
Libraries Overview: NumPy, Pandas, Matplotlib, Scikit-learn
Environment Setup: Jupyter Notebooks, Anaconda
Project: Simple Data Analysis Project analyzing a dataset (e.g., Titanic dataset) using basic Python and Pandas.
Data Understanding
and
PreprocessingTypes of Data
Understanding different types of data.
Structured Data: Tables, Spreadsheets
Unstructured Data: Text, Images, Audio
Semi-structured Data: JSON, XML
Data Collection and Cleaning
Techniques for collecting and cleaning data.
Data Collection Methods: APIs, Web Scraping, Databases
Data Cleaning: Handling Missing Values, Removing Duplicates, Data Transformation
Introduction to
Machine
LearningOverview of Machine Learning
Basic concepts and types of machine learning.
Supervised Learning: Classification, Regression
Unsupervised Learning: Clustering, Dimensionality Reduction
Reinforcement Learning: Basic Concepts
Project: Supervised Learning Project building and evaluating a predictive model (e.g., House Price Prediction using Linear Regression).
Supervised Learning Models
Common supervised learning models.
Linear Regression: Simple and Multiple Linear Regression
Logistic Regression: Binary and Multiclass Classification
Decision Trees and Random Forests: Concepts and Applications
Support Vector Machines (SVM): Kernel Trick, Applications
Unsupervised
Learning
ModelsClustering Algorithms
Techniques for grouping similar data points.
K-Means Clustering: Algorithm, Choosing the Number of Clusters
Hierarchical Clustering: Agglomerative and Divisive Methods
Dimensionality Reduction
Techniques to reduce the number of features.
Principal Component Analysis (PCA): Concepts and Applications
t-Distributed Stochastic Neighbor Embedding (t-SNE): Concepts and Applications
Unsupervised Learning Project: Customer Segmentation using Clustering Algorithms.
Advanced Machine
LearningEnsemble Methods
Techniques to combine multiple models.
Bagging: Bootstrap Aggregating, Random Forests
Boosting: AdaBoost, Gradient Boosting, XGBoost
Project: Deep Learning Project image Classification using CNNs.
Neural Networks and Deep Learning
Basics of neural networks and introduction to deep learning.
Neural Network Architecture: Neurons, Activation Functions, Layers
Deep Learning Frameworks: TensorFlow, Keras
Convolutional Neural Networks (CNNs): For Image Data
Recurrent Neural Networks (RNNs): For Sequential Data
Model Evaluation and
OptimizationModel Evaluation Techniques
Methods to evaluate the performance of machine learning models.
Metrics for Regression: Mean Absolute Error, Mean Squared Error, R²
Metrics for Classification: Accuracy, Precision, Recall, F1 Score, ROC-AUC
Model Optimization Techniques
Techniques to improve model performance.
Hyperparameter Tuning: Grid Search, Random Search
Cross-Validation: K-Fold Cross-Validation, Stratified Cross-Validation
Project: Model Evaluation and Optimization Project evaluating and optimizing a predictive model.
Natural Language
Processing
(NLP)Introduction to NLP
Basics of processing and analyzing text data.
Text Preprocessing: Tokenization, Stop Words Removal, Stemming, Lemmatization.
Feature Extraction: Bag of Words, TF-IDF, Word Embeddings
NLP Models
Common models used in NLP.
Sentiment Analysis: Analyzing Sentiment from Text
Text Classification: Naive Bayes, LSTM for Text Classification
Project: NNLP Project sentiment Analysis of Social Media Posts.
Time Series
Analysis
Introduction to Time Series Analysis
Basics of analyzing time-dependent data.
Time Series Components: Trend, Seasonality, Noise
Stationarity: Augmented Dickey-Fuller Test, Differencing
Time Series Forecasting Models
Common models used for time series forecasting.
ARIMA: Autoregressive Integrated Moving Average
Exponential Smoothing: Holt-Winters Method
Project: Time Series Forecasting Project forecasting Stock Prices using ARIMA.
Big Data and
Cloud
Computing
Introduction to Big Data
Understanding the concepts of big data and its importance.
Big Data Technologies: Hadoop, Spark
Data Storage Solutions: HDFS, NoSQL Databases
Data Science on the Cloud
Using cloud platforms for data science.
Introduction to AWS/GCP/Azure for Data Science
Cloud-based Data Processing and Storage
Project: Big Data Project analyzing Large Datasets using Apache Spark.
Introduction
to Data
EngineeringOverview of Data Engineering
Introduction to data engineering, its importance, and its applications in various industries.
Data Engineering Lifecycle: Data Ingestion, Data Storage, Data Processing, Data Integration, Data Delivery.
Roles in Data Engineering: Data Engineer, Data Architect, ETL Developer, Database Administrator.
Introduction to Python for Data Engineering
Basic Python programming skills essential for data engineering.
Python Basics: Data Types, Control Structures, Functions
Libraries Overview: Pandas, NumPy, SQLAlchemy, PySpark
Environment Setup: Jupyter Notebooks, Anaconda
Simple Data Manipulation Project: Using Pandas to manipulate and analyze a small dataset.
Data
Collection
and
IngestionData Collection Methods
Techniques for collecting data from various sources.
APIs and Web Scraping: Using requests, BeautifulSoup, and Scrapy
Database Connectivity: Connecting to Databases using SQLAlchemy
Project: Kafka Streaming Project set up a Kafka producer to ingest data from a public API and a consumer to process and store the data.
Data Ingestion with Apache Kafka
Introduction to data streaming and Kafka.
Kafka Basics: Topics, Partitions, Producers, Consumers
Setting Up Kafka: Installing and Configuring Kafka
Streaming Data: Producing and Consuming Data Streams
Data
Storage
SolutionsRelational Databases with PostgreSQL
Fundamentals of relational databases using PostgreSQL.
Database Design: Tables, Schemas, Relationships
SQL Queries: CRUD Operations, Joins, Aggregations
Advanced PostgreSQL Features: Indexes, Transactions, Stored Procedures
NoSQL Databases with MongoDB
Fundamentals of NoSQL databases using MongoDB.
Document-Oriented Databases: Collections, Documents, BSON
CRUD Operations: Insert, Update, Delete, Find
Advanced MongoDB Features: Indexes, Aggregation Framework, Sharding
Project: Database Integration Project design and implement a data pipeline that ingests data into both PostgreSQL and MongoDB.
Data
Processing
and
TransformationBatch Processing with Apache Spark
Introduction to big data processing using Apache Spark.
Spark Basics: RDDs, DataFrames, Spark SQL
Data Transformation: Map, Filter, Reduce, Aggregations
Spark with Python (PySpark): Writing Spark jobs in Python
Real-time Processing with Kafka Streams
Advanced data streaming with Kafka Streams.
Kafka Streams API: Streams, Tables, Topologies
Stream Processing: Filtering, Transforming, Aggregating
Stateful Processing: Windowing, Joins, Persistent State Stores
Project: Data Processing Project implement a batch processing pipeline using PySpark and a real-time processing pipeline using Kafka Streams.
Data
Integration
and
ETLETL Concepts and Tools
Fundamentals of Extract, Transform, Load (ETL) processes.
ETL Process: Extraction, Transformation, Loading
ETL Tools Overview: Apache Airflow, Talend, Informatica
Writing ETL Jobs in Python: Using Pandas, SQLAlchemy, PySpark
Data Integration with Apache Airflow
Workflow orchestration and scheduling with Apache Airflow.
Airflow Basics: DAGs, Tasks, Operators
Writing Airflow DAGs: Python Scripts for Data Pipelines
Monitoring and Managing Workflows: Airflow UI, Logging, Alerting
Project: ETL Project design and implement an ETL pipeline using Apache Airflow to integrate data from multiple sources and store it in a data warehouse.
Data
WarehousingIntroduction to Data Warehousing
Basics of data warehousing concepts and architectures.
Data Warehouse Design: Star Schema, Snowflake Schema
ETL vs. ELT: Differences and Use Cases
Popular Data Warehousing Solutions: Amazon Redshift, Google BigQuery, Snowflake
Building a Data Warehouse with PostgreSQL
Practical implementation of a data warehouse using PostgreSQL
Schema Design: Fact and Dimension Tables
Data Loading: Using SQL and ETL Tools
Query Optimization: Indexes, Partitioning, Query Plans
Project: Data Warehouse Project: Design and implement a data warehouse schema in PostgreSQL and load data into it using an ETL pipeline.
Data
Analytics
and
BIIntroduction to Data Analytics
Fundamentals of data analytics and business intelligence.
Descriptive Analytics: Summary Statistics, Data Visualization
Predictive Analytics: Basic Machine Learning Concepts
Prescriptive Analytics: Optimization, Simulation
Data Visualization with Python
Techniques for visualizing data using Python.
Matplotlib and Seaborn: Creating Plots and Charts
Plotly and Dash: Interactive Visualizations
Best Practices: Choosing the Right Visualization, Storytelling with Data
Project: Data Analytics Project perform data analysis and create visualizations using Python libraries on a real-world dataset.
Big
Data
and
Cloud
Computing
Introduction to Big Data Technologies
Overview of big data technologies and their use cases.
Hadoop Ecosystem: HDFS, MapReduce, Hive
Apache Spark: Batch and Real-time Processing
NoSQL Databases: HBase, Cassandra
Data Engineering on the Cloud
Using cloud platforms for data engineering.
AWS for Data Engineering: S3, Redshift, Glue
Google Cloud Platform: BigQuery, Dataflow, Dataproc
Microsoft Azure: Azure Data Lake, Synapse Analytics
Project: Big Data Project implement a data engineering pipeline on a cloud platform of your choice.