Docs

Comprehensive guide on handling missing values and outliers in datasets including detection, imputation, and removal techniques with practical Python examples

Importance of data cleaning in machine learning, common issues with messy data, and methods for handling duplicate data to ensure reliable model outcomes

Methods for retrieving data from SQL and NoSQL databases, APIs, and Cloud data sources with practical considerations and Python code examples

Methods for retrieving data from various sources including CSV and JSON files with practical considerations using Python and Pandas

Foundational concepts, workflow, and vocabulary of machine learning providing clear understanding of tools and processes for building and deploying ML models

Current advancements in artificial intelligence highlighting transformative impacts across industries and practical applications shaping everyday life

Comprehensive overview of artificial intelligence history from its origins in the 1940s through modern developments and future possibilities

Comparing conventional engineering disciplines with modern software development methodologies. Explores how adopting a product-oriented approach leads to better software outcomes than traditional project management.

Explores how Taylorism's industrial-era siloed approach fails modern software development needs. Highlights why DevOps culture with collaboration and craftwork mindset delivers better outcomes than traditional command-and-control structures.

A collection of multiple-choice questions covering Module 2 topics including social coding, repository guidelines, small batch processing, MVP, test-driven development, behavior-driven development, microservices, and design for failure concepts.

This document explains why failures happen in cloud-native applications, how to design systems that recover quickly, and how to use strategies like retry circuit breaker, bulkhead, and chaos engineering to build systems that can handle failures gracefully.

Explores cloud native microservices architecture and its impact on modern application design. Covers stateless services, independent scalability resilience, and compares microservices with traditional monolithic architectures to highlight benefits in flexibility, scalability, and collaboration.

Explains Behavior-Driven Development (BDD) as an approach focusing on system behavior from the user's perspective. Covers the BDD workflow, Gherkin syntax for defining acceptance criteria, and how this methodology improves communication between technical and non-technical stakeholders while enabling automated testing.

Explains Test-Driven Development (TDD) as a software development approach where tests drive code design. Covers the Red-Green-Refactor workflow benefits including higher code quality and faster development, and the crucial role TDD plays in enabling effective CI/CD pipelines in DevOps environments.

Explains the concept of Minimum Viable Product (MVP) as a tool for learning and experimentation. Covers how MVPs help test hypotheses with minimal effort gather customer feedback, and enable iterative development to deliver products that truly meet customer needs.

Explores the concept of working in small batches and single piece flow in DevOps. Discusses how these Lean Manufacturing principles enable faster feedback loops, minimize waste, and support continuous integration and delivery practices for more efficient software development.

Outlines best practices for organizing Git repositories and implementing the Feature Branch Workflow. Covers guidelines for creating modular repositories using short-lived feature branches, and leveraging pull requests for collaborative code reviews to enhance code quality.

Explores social coding principles that bring open-source collaboration into enterprise environments. Covers the benefits of public repositories, code reuse, and pair programming practices that improve code quality and facilitate knowledge sharing between team members.

A comprehensive set of multiple-choice questions covering key concepts from Module 1 of the IBM DevOps Software Engineering course. Tests understanding of DevOps definitions, characteristics, business benefits, and historical development approaches.

Traces the evolution from Extreme Programming (XP) to Agile and DevOps. Examines how XP introduced iterative approaches and feedback loops, how Agile formalized these concepts with its manifesto, and why DevOps emerged to address the gap between development and operations teams.