BA / SA Materials

ТРАНСПОРТНЫЙ УРОВЕНЬ — TCP И UDP 🔗 Source: Вебинар "Иерархия и классификация технологий интеграции" 🧠 Complexity: ★★☆ ➜ Два основных транспортных протокола — UDP и TCP ➜ UDP: простота, отсутствие гарантий доставки, порядка и соединения ➜ TCP: установка соединения (трёхстороннее рукопожатие), надёжность, подтверждения, повторная отправка, контроль потока ➜ Выбор между UDP и TCP: низкая задержка против надёжности Auto description:

TCP и UDP — два основных протокола транспортного уровня. UDP простой и быстрый, но не гарантирует доставку, порядок и не хранит состояние соединения. TCP, напротив, устанавливает соединение и обеспечивает надёжную упорядоченную передачу данных с подтверждениями и повторными отправками. UDP выбирают для низкой задержки (например, стриминг), а TCP — когда важна целостность данных.

СЕТЕВЫЕ ПРОТОКОЛЫ И МОДЕЛЬ OSI 🔗 Source: Вебинар "Иерархия и классификация технологий интеграции" 🧠 Complexity: ★★☆ ➜ Уровни протоколов ➜ Понятие «транспорт» ➜ Как это выглядит на практике (матрешка протоколов) ➜ Транспортный уровень (формальный) vs сленговое «транспорт» Auto description:

Передача данных происходит иерархически, используя многоуровневую структуру протоколов (OSI/TCP-IP). Каждое сообщение "заворачивается" (инкапсулируется) в заголовки на каждом уровне — от Прикладного до Физического. Эти уровни обеспечивают модульность: каждый отвечает за свою функцию и использует нижележащий уровень как свой "транспорт". Промежуточные узлы считывают только служебную информацию для маршрутизации, тогда как полная расшифровка происходит только на конечной точке получения.

Soon I'm gonna move from digital garden to docusarus... Docusaurus seem to be more customizable, tbh It will looks something like: https://arlagonix.github.io/ba-sa-materials/docs/intro

DELEGATION 🔗 Source: Anthropic Free Course 🧠 Complexity: ★☆☆ ➜ A closer look at delegation ➜ Problem awareness ➜ Platform awareness ➜ Task delegation Auto description:

Delegation involves deciding which tasks to handle yourself and which to assign to AI, based on understanding the problem, the platform's capabilities, and effective division of work. Problem awareness requires clarifying your goals and breaking down complex tasks before involving AI. Platform awareness means knowing the strengths and limitations of different AI systems through experimentation. Effective task delegation divides work between yourself and AI by considering what can be automated, where augmentation adds value, and what should remain solely human.

REFERENCE MODELS AND TECHNIQUES IN THE BUSINESS ARCHITECTURE PERSPECTIVE 🔗 From: BABOK 👤 By: IIBA 🧠 Complexity: ★★☆ ➜ Reference Models ➜ Techniques Auto description:

Reference models are predefined architectural templates that provide standard viewpoints for common industry functions, serving as a baseline that business architects adapt. Common models exist for finance (ACORD), IT (COBIT, ITIL), communications (eTOM), government (FEA SRM), supply chain (SCOR), and cross-sector processes (PCF). Business architecture also uses techniques like capability maps, customer journey maps, value mapping, roadmaps, and project portfolio analysis, along with frameworks such as Archimate, BMM, TOGAF, and Zachman for modelling and structuring enterprise architecture.

THE EVOLUTIONARY NATURE OF ANALYTICS 🔗 From: Software Requirements, 3rd Edition, Chapter 25 👤 By: Karl Wiegers, Joy Beatty 🧠 Complexity: ★☆☆ ➜ There are back-and-forth interactions between data, its analysis, and its usage ➜ The key to defining requirements for analytics projects, therefore, is to start somewhere ➜ Understand how much the users expect their needs to evolve ➜ An analytics solution should take into consideration the forms and conditions the data is in at the times it is extracted from a source, analyzed, and viewed by a user Auto description

To elicit requirements for a business analytics project, start by defining business objectives to establish scope and prioritize work. Next, prioritize the business decisions the solution enables, then define how the resulting information will be used by people or systems. Finally, specify data needs and define the analyses that transform data into answers.

CAPABILITIES AND LIMITATIONS OF AI MODELS 🔗 Source: Anthropic Free Course 🧠 Complexity: ★☆☆ ➜ Capabilities ➜ Limitations Auto description:

Effective AI systems thrive when combining human critical thinking with the model's speed, scale, and pattern recognition abilities. Models can shift between different tasks, maintain conversational threads, and access information using external tools beyond their core knowledge base. However, they are fundamentally limited by a training data cutoff date and may reproduce inaccuracies or confidently "hallucinate" false information. Furthermore, AI models have limits on context window size, struggle with complex reasoning, and produce non-deterministic outputs.

REQUIREMENTS DEVELOPMENT FOR BUSINESS ANALYTICS PROJECTS 🔗 From: Software Requirements, 3rd Edition, Chapter 25 👤 By: Karl Wiegers, Joy Beatty 🧠 Complexity: ★★☆ ➜ Define business objectives ➜ Prioritize work by using decisions ➜ Define how information will be used ➜ Specify data needs ➜ Define analyses that transform the data Auto description

To elicit requirements for a business analytics project, start by defining business objectives to establish scope and prioritize work. Next, prioritize the business decisions the solution enables, then define how the resulting information will be used by people or systems. Finally, specify data needs and define the analyses that transform data into answers.

GENERATIVE AI FUNDAMENTALS 🔗 Source: Anthropic Free Course 🧠 Complexity: ★☆☆ ➜ Three pillars that made the LLM possible: Algorithms. Data. Computation ➜ Steps of AI models training: Pre-training. Fine-tuning ➜ Important terms: Prompt. Context window. ➜ Characteristics that make generative AI powerful Auto description:

Generative AI creates new content, whereas traditional AI analyzes existing data. Large Language Models (LLMs) became possible through breakthroughs in algorithms, massive amounts of data, and increased computational power. These models are developed through pre-training to learn language patterns and fine-tuning to follow instructions. Once deployed, users interact with them using prompts within a limited context window.

OVERVIEW OF BUSINESS ANALYTICS PROJECTS 🔗 From: Software Requirements, 3rd Edition, Chapter 25 👤 By: Karl Wiegers, Joy Beatty 🧠 Complexity: ★☆☆ ➜ On business analytics projects, complex reports and the ability to manipulate their contents constitute the core functionality ➜ Users organize, manipulate, and analyze information to predict what might happen in the future, as opposed to interpreting the past ➜ Go incremental. If an organization is new to analytics, it should pilot a few small projects to demonstrate the value of analytics and to learn from the experience ➜ Challenges with business analytics projects Auto description

Business analytics projects focus on complex reporting and predicting future outcomes. These projects require defining requirements for data, analysis, and information usage. An incremental approach helps teams manage these layers effectively. Business analysts often face challenges because stakeholders struggle to define problems or understand new technologies.