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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

[🔍] [📃] 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

[🔍] [❌] Machine Learning Introduction with Python

Module Lists

[🔍] [📃] Machine Learning Intermediate with Python

Module Lists

[🔍] [📃] Probability and Statistics with Python

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[🔍] [📃] 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

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Step 3 - The Command Line

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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

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[🔍] [📃] Data Scientist In Python

Material Lists

Step 1 - Python Introduction

Module Lists

Step 2 - Data Analysis and Visualization

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Step 3 - The Command Line

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Step 4 - Working with Data Sources

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Step 5 - Probability and Statistics

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Step 6 - Machine Learning Introduction

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Step 7 - Machine Learning Intermediate

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Step 8 - Advanced Topics in Data Science

Module Lists

[🔍] [❌] Data Engineer

Material Lists

Step 1 - Introduction to Python

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Step 2 - Introduction to Algorithms

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Step 3 - Working with Data Sources

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Step 4 - Production Databases

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Step 5 - Handling Large Data Sets in Python

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Step 6 - Data Pipelines

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[🔍] [📃] 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

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Step 5 - Probability and Statistics

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Step 6 - Predictive Modeling and Machine Learning in R

Module Lists