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Power BI

1. Introduction to Power BI

1.1. Overview of Power BI

• What is Power BI?
• Key features and benefits
• Power BI ecosystem (Desktop, Service, Mobile)

1.2. Getting Started with Power BI Desktop

• Installing Power BI Desktop
• Interface and navigation
• Connecting to data sources

2. Data Preparation and Transformation

2.1. Data Loading

• Importing data from various sources (Excel, SQL, Web, etc.)
• Direct Query vs Import Mode

2.2. Data Transformation using Power Query Editor

• Understanding Power Query Editor
• Basic transformations (filtering, sorting, renaming)
• Advanced transformations (merging, appending, pivoting, unpivoting)
• Data types and data profiling

3. Data Modeling

3.1. Creating Relationships

• Understanding relationships
• Creating and managing relationships
• Cardinality and cross-filter direction

3.2. Creating Calculated Columns and Measures

• Introduction to DAX (Data Analysis Expressions)
• Basic DAX functions (SUM, AVERAGE, COUNT)
• Advanced DAX functions (CALCULATE, FILTER, ALL)

3.3. Optimizing Data Models

• Star schema design
• Data granularity
• Performance optimization tips

4. Data Visualization

4.1. Creating Basic Visualizations

• Types of visualizations (bar chart, line chart, pie chart, etc.)
• Choosing the right visualization for the data

4.2. Advanced Visualization Techniques

• Custom visuals
• Conditional formatting
• Drill-through and drill-down
• Bookmarks and buttons for interactivity

4.3. Design and Formatting

• Designing effective reports
• Theming and branding
• Best practices for visual design

5. Advanced Topics

5.1. Power BI Service

• Publishing reports to Power BI Service
• Sharing and collaboration
• Understanding workspaces, dashboards, and apps

5.2. Power BI Mobile

• Overview of Power BI Mobile
• Optimizing reports for mobile view

5.3. Power BI and Excel Integration

• Using Power BI Publisher for Excel
• Analyzing Power BI data in Excel

5.4. Row-Level Security (RLS)

• Implementing RLS in Power BI
• Managing roles and permissions

5.5. Data Refresh and Gateway

• Scheduled data refresh
• Configuring and using data gateways

6. Real-World Case Studies and Projects

6.1. Industry-specific use cases

• Examples from finance, marketing, sales, etc.

6.2. Capstone Project

• Building a comprehensive Power BI report/dashboard from scratch
• Presenting the project and receiving feedback

7. Resources and Next Steps

7.1. Continuing Education

• Recommended books, blogs, and online courses
• Joining Power BI community forums and user groups

7.2. Certification Preparation

• Overview of Power BI certification exams (PL-300, etc.)
• Study tips and resources

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DevOps Curriculum

1. Introduction to DevOps

1.1. Overview of DevOps

• What is DevOps?
• History and evolution of DevOps
• Key principles and benefits of DevOps
• DevOps culture and mindset

1.2. DevOps Lifecycle

• Continuous Integration (CI)
• Continuous Delivery (CD)
• Continuous Deployment
• Continuous Monitoring

2. Version Control Systems

2.1. Introduction to Version Control

• What is version control?
• Benefits of version control
• Types of version control systems

2.2. Git and GitHub

• Introduction to Git
• Basic Git commands
• Branching and merging
• Using GitHub for collaboration

3. Continuous Integration

3.1. CI Concepts

• Introduction to CI
• Benefits of CI
• CI tools overview

3.2. Jenkins

• Introduction to Jenkins
• Installing and configuring Jenkins
• Creating Jenkins pipelines
• Integrating Jenkins with other tools

4. Continuous Delivery and Deployment

4.1. CD Concepts

• Introduction to CD
• Benefits of CD
• CD tools overview

4.2. Kubernetes and Docker

• Introduction to containerization
• Docker basics
• Creating and managing Docker containers
• Introduction to Kubernetes
• Deploying applications on Kubernetes

5. Infrastructure as Code

5.1. Introduction to IaC

• What is Infrastructure as Code?
• Benefits of IaC
• Tools for IaC

5.2. Terraform

• Introduction to Terraform
• Installing and configuring Terraform
• Writing Terraform configurations
• Provisioning infrastructure with Terraform

6. Continuous Monitoring

6.1. Monitoring Concepts

• Importance of monitoring in DevOps
• Types of monitoring (infrastructure, application, etc.)
• Monitoring tools overview

6.2. Prometheus and Grafana

• Introduction to Prometheus
• Setting up Prometheus
• Introduction to Grafana
• Visualizing metrics with Grafana

7. Security in DevOps

7.1. DevSecOps Concepts

• What is DevSecOps?
• Benefits of integrating security into DevOps
• DevSecOps tools and practices

7.2. Security Tools

• Overview of security tools (e.g., SonarQube, OWASP ZAP)
• Integrating security tools into CI/CD pipeline

8. DevOps Best Practices

8.1. Agile and Scrum

• Introduction to Agile methodologies
• Scrum framework
• Implementing Agile in DevOps

8.2. Collaboration and Communication

• Importance of collaboration in DevOps
• Tools for effective communication (e.g., Slack, Microsoft Teams)

9. Real-World Case Studies and Projects

9.1. Industry-specific use cases

• Examples from finance, healthcare, e-commerce, etc.

9.2. Capstone Project

• Building a comprehensive DevOps pipeline from scratch
• Presenting the project and receiving feedback

10. Resources and Next Steps

10.1. Continuing Education

• Recommended books, blogs, and online courses
• Joining DevOps community forums and user groups

10.2. Certification Preparation

• Overview of DevOps certification exams (e.g., Docker Certified Associate, Kubernetes Certified Administrator)
• Study tips and resources

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Oracle OBIEE/OAS Curriculum

1. Introduction to OBIEE/OAS

1.1. Overview of OBIEE/OAS

• What is OBIEE/OAS?
• History and evolution of OBIEE/OAS
• Key features and benefits
• OBIEE/OAS architecture

1.2. OBIEE/OAS Components

• Oracle BI Server
• Oracle BI Presentation Services
• Oracle BI Scheduler
• Oracle BI Publisher

2. Installation and Configuration

2.1. Installing OBIEE/OAS

• System requirements
• Installation steps
• Post-installation configuration

2.2. Configuring OBIEE/OAS

• Configuration tools
• Setting up the RPD file
• Configuring BI Publisher

3. Repository (RPD) Development

3.1. Repository Basics

• Introduction to RPD
• Three layers of RPD: Physical, Business Model, Presentation

3.2. Building the Physical Layer

• Importing data sources
• Defining physical joins

3.3. Building the Business Model Layer

• Creating business models
• Defining logical tables and joins

3.4. Building the Presentation Layer

• Creating presentation tables and columns
• Organizing presentation layer

3.5. Testing and Validating the Repository

• Consistency checking
• Deploying RPD to the server
• Testing the repository

4. Creating Analyses and Dashboards

4.1. Creating Analyses

• Introduction to analyses
• Using the Analysis Editor
• Creating different types of analyses (tables, charts, pivot tables)

4.2. Creating Dashboards

• Introduction to dashboards
• Dashboard layout and design
• Adding analyses to dashboards
• Using prompts and filters in dashboards

5. Advanced OBIEE/OAS Features

5.1. Actionable Intelligence

• Introduction to actions
• Creating and configuring actions

5.2. Agents and Alerts

• Introduction to agents
• Creating and scheduling agents
• Configuring delivery options

5.3. BI Publisher

• Creating and managing reports
• Data modeling in BI Publisher
• Report layout and formatting

6. Security in OBIEE/OAS

6.1. Security Overview

• Introduction to OBIEE/OAS security
• Authentication and authorization

6.2. Implementing Security

• Creating users and roles
• Setting permissions and privileges
• Row-level security

7. Performance Tuning and Optimization

7.1. Performance Tuning Concepts

• Introduction to performance tuning
• Identifying performance bottlenecks

7.2. Optimizing OBIEE/OAS

• Optimizing repository design
• Query performance optimization
• Caching strategies

8. Real-World Case Studies and Projects

8.1. Industry-specific Use Cases

• Examples from finance, healthcare, retail, etc.

8.2. Capstone Project

• Building a comprehensive OBIEE/OAS solution from scratch
• Presenting the project and receiving feedback

9. Resources and Next Steps

9.1. Continuing Education

• Recommended books, blogs, and online courses
• Joining OBIEE/OAS community forums and user groups

9.2. Certification Preparation

• Overview of OBIEE/OAS certification exams
• Study tips and resources

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Java Curriculum

1. Introduction to Java

1.1. Java Basics

• Introduction to Java programming language
• History and evolution of Java
• Java platform and JVM
• Setting up Java Development Environment

1.2. Object-Oriented Programming (OOP) in Java

• Classes and objects
• Inheritance and polymorphism
• Encapsulation and abstraction

2. Core Java Programming

2.1. Language Fundamentals

• Variables, data types, and operators
• Control flow statements (if-else, switch-case)
• Loops (for, while, do-while)
• Arrays and strings

2.2. Methods and Functions

• Defining methods
• Method overloading and overriding
• Passing parameters
• Recursive functions

3. Advanced Java Concepts

3.1. Exception Handling

• Understanding exceptions
• Handling exceptions using try-catch blocks
• Throwing and catching exceptions
• Custom exceptions

3.2. Multithreading

• Introduction to threads
• Creating and managing threads
• Synchronization and thread safety
• Concurrency issues and solutions

4. Java Collections Framework

4.1. Overview of Collections

• Collection interfaces (List, Set, Map)
• Understanding ArrayList, LinkedList, HashSet, HashMap
• Iterating through collections
• Sorting and searching collections

4.2. Generics

• Introduction to generics
• Generic classes and methods
• Wildcard types

5. Java EE (Enterprise Edition)

5.1. Servlets and JSP

• Introduction to web applications
• Servlet lifecycle and API
• JSP syntax and elements
• Handling form data

5.2. JDBC (Java Database Connectivity)

• Connecting to databases
• Executing SQL queries
• Handling transactions
• Using PreparedStatement and CallableStatement

6. Java Frameworks and Tools

6.1. Spring Framework

• Introduction to Spring
• Dependency Injection (DI) and Inversion of Control (IoC)
• Spring MVC architecture
• Spring Boot introduction

6.2. Hibernate Framework

• Introduction to ORM (Object-Relational Mapping)
• Mapping entities and relationships
• CRUD operations with Hibernate
• Transactions and caching

7. Java Testing and Debugging

7.1. Unit Testing with JUnit

• Introduction to unit testing
• Writing and running JUnit tests
• Test-driven development (TDD)

7.2. Debugging Techniques

• Debugging tools and practices
• Setting breakpoints and watching variables
• Handling runtime errors

8. Java Best Practices

8.1. Code Quality and Style

• Coding conventions
• Code refactoring techniques
• Writing clean and maintainable code

8.2. Performance Optimization

• Optimizing Java applications
• Memory management and garbage collection
• Profiling tools

9. Real-World Java Applications

9.1. Case Studies

• Examples from enterprise applications
• Scalability and performance challenges

9.2. Capstone Project

• Developing a full-stack Java application
• Implementing best practices
• Presenting the project and receiving feedback

10. Resources and Next Steps

10.1. Continuing Education

• Recommended books, blogs, and online courses
• Joining Java community forums and user groups

10.2. Certification Preparation

• Overview of Java certification exams (e.g., Oracle Certified Java Programmer)
• Study tips and resources

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Python and Machine Learning Curriculum

1. Introduction to Python

1.1. Python Basics

• Introduction to Python programming language
• Python syntax and data types
• Control flow statements (if-else, loops)
• Functions and modules

2. Advanced Python Programming

2.1. Object-Oriented Programming (OOP) in Python

• Classes and objects
• Inheritance and polymorphism
• Encapsulation and abstraction

2.2. File Handling and Exception Handling

• Reading and writing files
• Handling exceptions with try-except blocks
• Using context managers

3. Python Libraries for Data Science

3.1. NumPy

• Introduction to NumPy arrays
• Array operations and broadcasting
• Linear algebra with NumPy

3.2. Pandas

• Introduction to Pandas DataFrames
• Data manipulation and cleaning
• Grouping and aggregating data

3.3. Matplotlib and Seaborn

• Creating basic plots with Matplotlib
• Statistical data visualization with Seaborn
• Customizing plots and adding annotations

4. Introduction to Machine Learning

4.1. Machine Learning Basics

• Overview of supervised vs unsupervised learning
• Model training and evaluation
• Types of machine learning algorithms

4.2. Scikit-Learn

• Introduction to Scikit-Learn library
• Building and evaluating machine learning models
• Feature selection and preprocessing

4.3. TensorFlow and Keras

• Introduction to neural networks with TensorFlow
• Building deep learning models with Keras
• Training models and evaluating performance

5. Real-World Applications of Machine Learning

5.1. Natural Language Processing (NLP)

• Text preprocessing and tokenization
• Sentiment analysis and text classification
• Named Entity Recognition (NER)

5.2. Computer Vision

• Image preprocessing and feature extraction
• Object detection and image classification
• Face recognition and image segmentation

6. Deployment and Productionization

6.1. Model Deployment

• Deploying machine learning models with Flask
• Containerization with Docker
• Cloud deployment options (AWS, GCP, Azure)

6.2. Monitoring and Scaling

• Monitoring model performance
• Scaling machine learning applications
• Handling updates and maintenance

7. Resources and Next Steps

7.1. Continuing Education

• Recommended books, blogs, and online courses
• Joining Python and ML communities

7.2. Certification Preparation

• Overview of relevant certifications (e.g., TensorFlow Developer)
• Study tips and resources