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    Object-Oriented Analysis and Design (OOAD) with UML

    Mastering Software Design
    This immersive, hands-on course teaches the core techniques and best practices of object-oriented analysis and design (OOAD), empowering participants to build high-quality software systems that meet requirements and are delivered on time. Through a practical, applied approach, you’ll learn how to model, design, and communicate effectively using proven OO principles and industry-standard notations like UML.

    The course is built around three integrated pillars:

    Concepts – Core OO principles and design thinking

    Notation – Unified Modeling Language (UML) for clear documentation and communication

    Process – Modern development methodologies (e.g., Agile, RUP) for iterative, efficient delivery

    Each topic is introduced individually, then brought together through a team-based group project that simulates real-world development. You’ll gain experience using UML to create meaningful models, documenting requirements with use cases, applying design patterns, and understanding how OOAD supports modern software development lifecycles.

    Object-Oriented Analysis and Design (OOAD) with UML Objectives

    • Concepts & Notation
    • Grasp key OO concepts: types inheritance
    • polymorphism and interfaces
    • Understand the difference between structured design and OOAD
    • Develop intuition for applying OO techniques effectively (and when not to)
    • Learn to recognize and use OO architectural and design patterns
    • Master UML diagrams for modeling and communicating system structure and behavior
    • Create:
    • Use Cases to define system functionality
    • Static models (e.g. class diagrams)
    • Dynamic models (e.g. sequence state and activity diagrams)
    • Refine models using design patterns
    • Process
    • Understand what a software development process is and why it matters
    • Explore leading methodologies including the Unified Process RUP and Agile
    • Tailor processes to suit the scope and complexity of your project
    • Apply iterative incremental techniques to reduce risk and improve outcomes
    • Learn what pitfalls to avoid—such as analysis paralysis or over-design

    Need Assistance Finding the Right Training Solution

    Our Consultants are here to assist you
    Arrange a consultation

    Key Point of Training Programs

    We have different work process to go step by step for complete our working process in effective way.
    • Object-Oriented Analysis and Design (OOAD) with UML Prerequisites

      No programming experience necessary

    • Object-Oriented Analysis and Design (OOAD) with UML Training Format

      In-Person

      Online

    • Object-Oriented Analysis and Design (OOAD) with UML Outline

      Session 1: Introduction to OOAD

      Overview of Object-Oriented Thinking

      OO Concepts and Principles

      Making the Case for Object Orientation

      Lab: Exploring the OO Paradigm

      Session 2: Unified Modeling Language (UML)

      Introduction to UML and its role in OOAD

      Static Diagrams: Use Case, Class, Package, Component, Deployment

      Dynamic Diagrams: Collaboration, Sequence, State Chart, Activity

      Labs: Class, Sequence, State Diagram Practice

      Session 3: The Software Development Process

      Understanding Development Lifecycle Models

      Iterative & Agile Methodologies

      Unified Process: Phases, Workflows, Iterations

      Use Case–Driven, Architecture-Centric Approaches

      Labs: Comparing Civil Engineering vs. Software Development, Modeling Processes

      Session 4: Inception Phase

      Visioning, Business Modeling, Stakeholder Identification

      Risk Identification and Planning

      Labs: Vision Statement, System Context, Stakeholder Analysis

      Session 5: Introduction to Use Cases

      Actors and Use Case Relationships

      Writing Effective Use Case Descriptions

      Building the Initial Use Case Model

      Labs: Discovering Actors and Use Cases

      Session 6: Additional Modeling Techniques

      Domain Modeling: Identifying Business Concepts

      Technology Modeling

      Capturing Non-Functional Requirements

      Labs: Domain Model Creation, NFR Definition

      Session 7: Elaboration Phase – Analysis

      Detailing and Elaborating Use Cases

      Refining the Analysis Model

      Labs: Identifying Analysis Classes, Deepening Use Cases

      Session 8: Elaboration Phase – Design

      Dynamic Modeling and Object Design

      Working with Frameworks and Tiered Architectures

      Applying OO Design Principles

      Labs: Collaboration Modeling, Object Refinement

      Session 9: Introduction to Design Patterns

      Understanding Pattern Basics

      Exploring the Iterator Pattern

      Labs: Analyzing Collection Traversal and Iterators

      Session 10: Core GOF Patterns

      Gang of Four Patterns Overview

      Design Pattern Categories and Benefits

      Labs: Pattern Discussion and Analysis

      Session 11: Applying Key Design Patterns

      In-Depth Exploration of:

      Factory Method

      Strategy

      Decorator

      Template Method

      Labs: Hands-on Use of Each Pattern

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    Introduction to Spark 3 with Python

    Master Distributed Data Processing with Spark
    This course provides an in-depth introduction to Apache Spark 3 for distributed computing. Designed for developers, data analysts, and architects, it focuses on leveraging Spark’s powerful engine for big data processing using Python (PySpark). The course covers core Spark concepts, Resilient Distributed Datasets (RDDs), DataFrames, Spark SQL, and Structured Streaming for real-time data processing.

    Through hands-on exercises, participants will learn how to interact with Spark efficiently, optimize queries, and integrate with Kafka for streaming data ingestion.

    Introduction to Spark 3 with Python Objectives

    • Participants will:
    • Understand Apache Spark’s architecture and its advantages over traditional big data frameworks.
    • Work with RDDs transformations and actions for distributed computations.
    • Utilize Spark SQL and the DataFrame API for structured data processing.
    • Leverage Spark’s Catalyst optimizer and Tungsten engine for query performance.
    • Process real-time streaming data with Spark Structured Streaming.
    • Integrate Kafka with Spark Streaming for event-driven data ingestion.
    • Optimize Spark applications using caching shuffling strategies and broadcast variables.
    • Develop standalone Spark applications using PySpark.

    Need Assistance Finding the Right Training Solution

    Our Consultants are here to assist you
    Arrange a consultation

    Key Point of Training Programs

    We have different work process to go step by step for complete our working process in effective way.
    • Introduction to Spark 3 with Python Prerequisites

      Who Should Attend: Developers, data engineers, and analysts working with big data.

      Prerequisites: Basic Python programming knowledge and familiarity with SQL.

    • Introduction to Spark 3 with Python Training Format

      In-Person

      Online

    • Introduction to Spark 3 with Python Outline

      Session 1: Introduction to Spark

      Overview of Apache Spark and its role in distributed computing

      Comparing Spark vs. Hadoop for big data processing

      Spark’s ecosystem and core components

      Setting up Spark and PySpark environment

      Lab: Running Spark in local and cluster mode

      Session 2: Understanding RDDs and Spark Architecture

      RDD concepts, transformations, and lazy evaluation

      Data partitioning, pipelining, and fault tolerance

      Applying map(), filter(), reduce(), and other RDD operations

      Lab: Creating and manipulating RDDs

      Session 3: Working with DataFrames and Spark SQL

      Introduction to Spark SQL and DataFrames

      Creating and querying DataFrames using SQL-based and API-based approaches

      Working with different data formats (JSON, CSV, Parquet, etc.)

      Lab: Querying structured data using Spark SQL

      Session 4: Performance Optimization in Spark

      Understanding shuffling and data locality

      Catalyst query optimizer (explain() and query execution plans)

      Tungsten optimizations (binary format, whole-stage code generation)

      Lab: Optimizing Spark queries for performance

      Session 5: Spark Structured Streaming

      Introduction to stream processing and event-driven architecture

      Working with Structured Streaming API

      Processing real-time data in a continuous query model

      Lab: Building a streaming data pipeline in Spark

      Session 6: Integrating Spark with Kafka

      Overview of Kafka and event-driven data streaming

      Using Spark to consume and process Kafka streams

      Configuring Kafka as a data source and sink

      Lab: Ingesting and processing real-time Kafka data with Spark

      Session 7: Advanced Performance Tuning

      Caching and data persistence strategies

      Reducing shuffling for efficient computation

      Using broadcast variables and accumulators

      Lab: Implementing caching and shuffling optimizations

      Session 8: Building Standalone Spark Applications

      Creating Spark applications using PySpark API

      Configuring SparkSession and application parameters

      Running Spark applications on local and cluster environments

      Lab: Developing a PySpark application and submitting jobs

      By the end of this course, participants will be able to develop scalable data processing applications using Apache Spark 3 and Python (PySpark) effectively.