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This is a repository for storing and sharing data resulting from working on projects and materials in Dataquest



Dataquest is an online data learning portal that provides thousands of material and quizzes that can improve your data science skills like Python, R, SQL, Git, and more!


Learn by watching videos coding!

Most people learn best by doing, yet many data science courses only ask you to memorize information, never to apply it. We took a different approach:

  1. In interactive lessons, you'll learn a new concept, write code to apply it, and get feedback all on the same screen.
  2. In guided projects, you'll use the concepts you just learned to answer real-world questions on real datasets.
  3. In a typical Dataquest session, you won't go more than five minutes without writing code!

Skill Paths

[🔍] [❌] SQL Fundamentals

Module Lists

  • [❌] [🔍] [❌] Fundamentals of SQL I

  • [❌] [🔍] [❌] Fundamentals of SQL II

  • [❌] [🔍] [❌] From Reports to Insights with SQL

[🔍] [📃] Python Basics for Data Analysis

Module Lists

[🔍] [❌] R Basics for Data Analysis

Module Lists

[🔍] [❌] Data Analysis and Visualization with Python

Module Lists

[🔍] [📃] Data Visualization with R

Module Lists

[🔍] [📃] APIs and Web Scraping with Python

Module Lists

[🔍] [❌] APIs and Web Scraping with R

Module Lists
  • [❌] [🔍] [❌] APIs in R

[🔍] [❌] Machine Learning Introduction with Python

Module Lists

[🔍] [📃] Machine Learning Intermediate with Python

Module Lists

[🔍] [📃] Probability and Statistics with Python

Module Lists

[🔍] [📃] Probability and Statistics with R

Module Lists

Career Paths

[🔍] [📃] Data Analyst in Python

Material Lists

Step 1 - Introduction to Python

Module Lists

Step 2 - Intermediate Python and Pandas

Module Lists

Step 3 - The Command Line

Module Lists

Step 4 - Working with Data Sources

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Step 5 - Probability and Statistics

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Step 6 - Advanced Topics in Data Analysis

Module Lists

[🔍] [📃] Data Scientist In Python

Material Lists

Step 1 - Python Introduction

Module Lists

Step 2 - Data Analysis and Visualization

Module Lists

Step 3 - The Command Line

Module Lists

Step 4 - Working with Data Sources

Module Lists

Step 5 - Probability and Statistics

Module Lists

Step 6 - Machine Learning Introduction

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Step 7 - Machine Learning Intermediate

Module Lists

Step 8 - Advanced Topics in Data Science

Module Lists

[🔍] [❌] Data Engineer

Material Lists

Step 1 - Introduction to Python

Module Lists

  • [❌] [🔍] [❌] Python Fundamentals

  • [❌] [🔍] [❌] Python Intermediate

  • [❌] [🔍] [❌] Programming Concepts with Python

Step 2 - Introduction to Algorithms

Module Lists
  • [❌] [🔍] [❌] Algorithm Complexity

Step 3 - Working with Data Sources

Module Lists

  • [❌] [🔍] [📃] SQL Fundamentals

  • [❌] [🔍] [❌] Intermediate SQL

Step 4 - Production Databases

Module Lists

  • [❌] [🔍] [❌] Postgres for Data Engineers

  • [❌] [🔍] [❌] Optimizing Postgres Databases

Step 5 - Handling Large Data Sets in Python

Module Lists

  • [❌] [🔍] [❌] Numpy for Data Engineers

  • [❌] [🔍] [❌] Processing Large Datasets In Pandas

  • [❌] [🔍] [❌] Parallel Processing

  • [❌] [🔍] [❌] Data Structures Fundamentals

  • [❌] [🔍] [❌] Recursion and Trees

Step 6 - Data Pipelines

Module Lists
  • [❌] [🔍] [❌] Building a Data Pipeline

[🔍] [📃] Data Analyst in R

Material Lists

Step 1 - Introduction to R

Module Lists

Step 2 - Data Visualization in R

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Step 3 - Data Cleaning in R

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Step 4 - Working with Data Sources

Module Lists

Step 5 - Probability and Statistics

Module Lists

Step 6 - Predictive Modeling and Machine Learning in R

Module Lists

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This is a repository for storing and sharing data resulting from working on projects and materials in Dataquest

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