https://github.com/ndomah1/kaggle-learn
This repo contains all the scripts I worked on from Kaggle Learn
https://github.com/ndomah1/kaggle-learn
ai-ethics computer-vision data-cleaning data-visualization deep-learning feature-engineering game-ai geospatial-analysis jupyter-notebook kaggle machine-learning pandas python reinforcement-learning sql time-series
Last synced: 7 months ago
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This repo contains all the scripts I worked on from Kaggle Learn
- Host: GitHub
- URL: https://github.com/ndomah1/kaggle-learn
- Owner: ndomah1
- Created: 2025-03-21T21:36:54.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-03-21T22:01:32.000Z (7 months ago)
- Last Synced: 2025-03-21T22:28:27.605Z (7 months ago)
- Topics: ai-ethics, computer-vision, data-cleaning, data-visualization, deep-learning, feature-engineering, game-ai, geospatial-analysis, jupyter-notebook, kaggle, machine-learning, pandas, python, reinforcement-learning, sql, time-series
- Homepage:
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- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Kaggle Learn
This repo contains all the scripts I worked on from [Kaggle Learn](https://www.kaggle.com/learn).
## Phase 1: Foundation in Programming & Data Handling
### [Intro to Programming]() - If you're completely new to coding.
1. [Arithmetic and Variables]() - Make calculations, and define and modify variables.
2. [Functions]() - Organize your code and avoid redundancy.
3. [Data Types]() - Explore integers, floats, booleans, and strings.
4. [Conditions and Conditional Statements]() - Modify how functions run, depending on the input.
5. [Intro to Lists]() - Organize your data so you can work with it efficiently.
### [Python]() - Essential for data science and machine learning.
1. [Hello, Python]() - A quick introduction to Python syntax, variable assignment, and numbers.
2. [Functions and Getting Help]() - Calling functions and defining our own, and using Python's builtin documentation.
3. [Booleans and Conditionals]() - Using booleans for branching logic.
4. [Lists]() - Lists and the things you can do with them. Includes indexing, slicing and mutating.
5. [Loops and List Comprehensions]() - For and while loops, and a much-loved Python feature: list comprehensions.
6. [Strings and Dictionaries]() - Working with strings and dictionaries, two fundamental Python data types.
7. [Working with External Libraries]() - Improts, operator overloading, and survival tips for venturing into the world of external libraries.
### [Pandas]() - For data manipulation and analysis.
1. [Creating, Reading and Writing]() - You can't work with data if you can't read it. Get started here.
2. [Indexing, Selecting & Assigning]() - Pro data scientists do this dozens of times a day. You can, too!
3. [Summary Functions and Maps]() - Extract insights from your data.
4. [Grouping and Sorting]() - Scale up your level of insight. The more complex the dataset, the more this matters.
5. [Data Types and Missing Values]() - Deal with the most common progress-blocking problems.
6. [Renaming and Combining]() - Data comes in from many sources. Help it all make sense together.
### [Intro to SQL]() - To work with databases.
1. [Getting Started with SQL and BigQuery]() - Learn the workflow for handling big datasets with BigQuery and SQL.
2. [Select, From & Where]() - The foundational components for all SQL queries
3. [Group By, Having & Count]() - Get more interesting insights directly from your SQL queries.
4. [Order By]() - Order your results to focus on the most important data for your use case.
5. [As & With]() - Organize your query for better readability. This becomes especially important for complex queries.
6. [Joining Data]() - Combine data sources. Critical for almost all real-world data problems.