🗺️ The Road to the "1 Million Row" Dashboard
Your interactive guide to mastering data science
"By Class 15, analyze a database with 1+ million records and present a professional dashboard identifying key business insights."
Course Structure at a Glance
The course is broken into five phases, each building on the last. The "Warehouse" (SQL) phase is the largest, as it provides the core data foundation for everything that follows.
Learning Dependency Flow
This shows how each phase connects. You'll start by building the "Warehouse" (Storage), then learn to "Process" the data, "Analyze" it for insights, and finally "Present" your findings as a dashboard.
🔵 Phase 2: Storage
C3: DB Intro
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C4: DDL
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C5: DML
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C7: Adv SQL
→
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🟠 Phase 3: Processing
C8: Numpy
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C10: Pandas
→
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🟣 Phase 4: Analysis
C11: EDA Basic
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C13: EDA Adv
→
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🔴 Phase 5: Presentation
C14: Data Viz
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🏆 C15: Dashboard
🟢 Phase 1: The Foundation
Before we touch the data, we need the tools and the strategy. This phase sets up your environment and the "Data Lifecycle" framework you'll use for the final project.
Class 01: Onboarding
🎯 Contribution: Sets up your NotebookLM & Discord to kick start your learning journey.
Class 02: Intro to Data Science
🎯 Contribution: Teaches you the Data Lifecycle so you know the steps to follow for the project (Collection → Cleaning → Viz).
🔵 Phase 2: The Warehouse (Getting the Data)
You can't analyze 1 million rows in Excel. You need a database. This phase builds the "Backend" of your project, teaching you how to create, manage, and query data with SQL.
Class 03: Intro to Database
🎯 Contribution: Helps you understand the schema (map) of the massive dataset you will be given.
Class 04: SQL Basic (DDL)
🎯 Contribution: Teaches you how to create the tables to store the 1 million rows.
Class 05: SQL Basic (DML)
🎯 Contribution: Teaches you how to fix errors in specific rows of the raw data.
Class 06: Coaching 1: GitHub & VS Code
🎯 Contribution: Ensures you can save your versions so you don't lose your work if your computer crashes.
Class 07: SQL Advanced
🎯 Contribution: Crucial! You will use JOINS to connect the "Customer" table to the "Transactions" table to find spending habits.
🟠 Phase 3: The Engine (Crunching the Numbers)
SQL gets the data, but Python does the heavy mathematical lifting. This phase introduces the core Python libraries for high-speed numerical analysis and data manipulation.
Class 08: Intro to Numpy
🎯 Contribution: Teaches you vectorization—the only way to calculate stats on 1 million rows without crashing your browser.
Class 09: Coaching 2
🎯 Contribution: Bridges the gap between "Querying" (SQL) and "Programming" (Python).
Class 10: Intro to Pandas
🎯 Contribution: The core tool. You will use DataFrames to hold the data in memory and perform complex filtering (e.g., "Show me top 10% of users").
🟣 Phase 4: The Detective Work (Finding Insights)
Now that the data is loaded, we need to find the "gold" hidden in the noise. This is the Exploratory Data Analysis (EDA) phase, where you clean messy data and engineer new features.
Class 11: EDA Basic
🎯 Contribution: Cleaning. Real data is dirty. You will use this to remove nulls and duplicates from the 1 million rows.
Class 12: Coaching 3
🎯 Contribution: A practice run to ensure your cleaning scripts are robust enough for the final dataset.
Class 13: EDA Advanced
🎯 Contribution: Feature Engineering. You will create new metrics (e.g., converting "Date of Birth" into "Age Group") to make your dashboard more useful.
🔴 Phase 5: The Story (The Dashboard)
Analysis is useless if no one understands it. This final phase is about "data storytelling." You will learn to create clear visualizations and combine them into a single dashboard to present your findings.
Class 14: Data Visualization
🎯 Contribution: Generates the Charts & Graphs (Bar, Line, Scatter) that go onto your dashboard.
Class 15: Assignment Project Presentation
🎯 Contribution: You combine everything into a single notebook and present your findings to the class.