Big Data and AI

Turning Fibonacci from a visual crutch into a robust decision framework Fibonacci retracement is seductive: clean lines, iconic ratios, and the satisfying sense that price “respects” geometry. But markets are not Euclidean—they are noisy, adaptive, and reflexive. If you use Fibonacci as a drawing tool, you’ll get drawing-level results. If you use it as a framework—with standardized swing logic, probabilistic validation, confluence rules, and risk-first execution—you can turn those lines into a disciplined edge. This chapter dives very deep into where Fibonacci fails, why it fails, and how to engineer best practices that hold up in real trading and systematic testing. 6.1 Common pitfalls Subjectivity: swing points vary by timeframe and analyst Fibonacci analysis begins with picking a swing high and swing low, but that’s already a minefield. Ambiguous impulses: In real markets, you’ll often see overlapping swings, nested corrections, and failed breakouts. Your “obvious” swing might be a...

  Chapter 2: Setting Up Your Environment Before you write your first query, it’s essential to install and configure a relational database on your workstation. A solid environment ensures you spend time learning SQL—not battling installation errors. In this chapter, we’ll: Compare four popular RDBMS options Walk through installation on Windows, macOS, and Linux Demonstrate how to connect via command-line and graphical tools Load a sample database so you can start querying immediately By the end, your machine will be a fully functional SQL playground. 1. Choosing an RDBMS: MySQL, PostgreSQL, SQLite, SQL Server Different relational database systems excel in different scenarios. Here’s a quick comparison to help you pick one: Feature MySQL PostgreSQL SQLite SQL Server Use case Web apps, LAMP stacks Analytics, GIS, advanced SQL Lightweight, embedded apps Enterprise .NET, Windows ecosystems Licensing GPL (Community) / Commercial (Enterprise) Open Source (PostgreSQL License) Public domain...

  Chapter 1: Introduction to Databases and SQL In today’s data-driven world, information powers decisions at every level—from personal finance trackers to enterprise analytics platforms. Databases serve as the backbone for storing, organizing, and retrieving this information efficiently. In this chapter, we’ll explore why data lives in tables, familiarize you with core terminology, trace the origins of relational databases, and explain how SQL emerged as the universal language for data manipulation. What Is Data and Why We Store It in Tables Data represents facts, figures, and measurements collected from real-world activities. Without structure, raw data is difficult to query, analyze, or validate. Storing data in tables offers several advantages: Logical organization: Tables group related information into rows (records) and columns (attributes), making it easy to locate and interpret individual pieces of data. Consistency: Structured tables enforce uniform data types and formats, ...

  Welcome to Part I of our beginner-friendly SQL tutorial series on Data Analyst BI. In this first module, you’ll learn the fundamentals of relational databases and get your environment ready for hands-on SQL practice. Whether you aim to become a Data Analyst, BI Developer, or just want to query data like a pro, mastering these SQL basics will set you on the path to success. Introduction to Databases and SQL Structured Query Language (SQL) is the universal standard for interacting with relational databases. Before writing your first query, it’s essential to understand: What is a Database? A database stores information in tables made up of rows (records) and columns (fields). Common examples include customer lists, sales transactions, or inventory logs. Why Relational Databases? Relational databases enforce data integrity through primary keys, foreign keys, and constraints. This structure makes it easy to join related tables and maintain consistent, accurate data. The Role of SQL SQ...

  Welcome to Data Analyst BI! If you’ve ever felt overwhelmed by rows, columns, and cryptic error messages when trying to write your first SQL query, you’re in the right place. Today we’re launching a comprehensive SQL tutorial series crafted specifically for beginners. Whether you’re just starting your data career, pivoting from another field, or simply curious about how analysts slice and dice data, these lessons will guide you from day zero to confident query builder. In each installment, you’ll find clear explanations, annotated examples, and hands-on exercises. By the end of this series, you’ll be able to: Write efficient SQL queries to retrieve and transform data Combine multiple tables to uncover relationships Insert, update, and delete records safely Design robust database schemas with keys and indexes Optimize performance for large datasets Ready to master SQL in a structured, step-by-step way? Let’s explore the full roadmap ahead. Wh...