**PATH**

# Data Scientist

Learn how to make inferences and predictions from data.

This path covers everything you need to learn to work as a data scientist using Python.

You'll learn the Python fundamentals, dig into data analysis and data viz, query databases with SQL, study statistics, and dig into building machine learning models all over the course of this carefully designed course path.

It's designed so that there are no prerequisites and no prior experience required. Everything you need to learn, you'll learn on this path!

As you learn, you'll apply each concept immediately by writing code right in your browser that's automatically checked by our system to give you near-instant feedback on your progress.

We think the best way to learn is to learn by doing, so you'll be challenged every step of the way to really apply the concepts you're learning, and you'll build a variety of projects using real-world data to solve real data science problems.

By the end of this path, you'll have the skills you need to work as a data scientist, and you'll be comfortable with things like:

## START LEARNING

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## Become a Data Scientist

### Python for Data Science: Fundamentals

Learn the basics of Python programming and data science.

FREE

### Python for Data Science: Intermediate

Learn the basics of Python programming and data science.

FREE

### Pandas & NumPy Fundamentals

Learn how to analyze data using the pandas and NumPy libraries.

FREE + BASIC

### Exploratory Data Visualization

Learn how to explore data by creating and interpreting data graphics. This course is taught using matplotlib and pandas.

BASIC

### Storytelling Through Data Visualization

Learn how to communicate insights and tell stories using data visualization.

BASIC

### Data Cleaning and Analysis

Learn how to clean and combine datasets, then practice your skills.

BASIC

### Data Cleaning in Python: Advanced

Learn advanced techniques for cleaning data in Python.

BASIC

### Data Cleaning Project Walkthrough

Learn how to clean and combine datasets, then practice your skills.

BASIC

### Elements of the Command Line

Learn the basics of the Bash to establish a foundation of working the command line as a springboard to using the command line for data science

BASIC

### Text Processing in the Command Line

Learn more about the command line and how to use it in your data science workflow.

BASIC

### SQL Fundamentals

Learn the basics of working with SQL databases.

BASIC

### SQL Intermediate: Table Relations & Joins

Learn to work with multi-table databases.

BASIC

### SQL & Databases: Advanced

Learn how to work with PostgreSQL, customize databases using indexing and how to improve database performance.

BASIC

### APIs & Web Scraping

Learn how to acquire data from APIs and the web.

BASIC

### Statistics: Fundamentals

Learn about sampling, variables and distributions.

BASIC

### Statistics Intermediate: Averages & Variability

Learn to summarize distributions, measure variability using variance or standard deviation, and compare values using z-scores.

BASIC

### Probability Fundamentals

Learn the fundamentals of probability theory using Python

BASIC

### Hypothesis Testing: Fundamentals

Learn more advanced statistical concepts including A/B tests and chi-squared tests for more powerful data analysis.

BASIC

### Machine Learning Fundamentals

Learn the fundamentals of machine learning using k-nearest neighbors.

PREMIUM

### Calculus for Machine Learning

Learn the calculus necessary for intermediate machine learning techniques like linear regression.

PREMIUM

### Linear Algebra for Machine Learning

Learn the linear algebra necessary for intermediate machine learning techniques like linear regression.

PREMIUM

### Linear Regression for Machine Learning

Learn how to use the linear regression machine learning model.

PREMIUM

### Machine Learning in Python: Intermediate

Dive more into Machine learning.

PREMIUM

### Decision Trees

Learn how to construct and interpret decision trees.

PREMIUM

### Deep Learning: Fundamentals

Learn the basics of deep neural networks. Includes graph representation, activation functions, multiple hidden layers, and image classification.

PREMIUM

### Machine Learning Project

Learn what a complete data science project looks like, from data cleaning to machine learning.

PREMIUM

### Kaggle Fundamentals

Learn how to get started with and participate in Kaggle competitions with Kaggle's 'Titanic' competition.

PREMIUM

### Exploring Topics in Data Science

Explore other topics in data science, like NLP and clustering.

PREMIUM

### Natural Language Processing

Learn how to analyze and make predictions on textual data.

PREMIUM

### Data Structures & Algorithms

Learn how computers work and how they work with data.

PREMIUM

### Python Programming Advanced

Learn advanced concepts in Python, including more on object-oriented programming, lambda functions, and exception handling.

PREMIUM

### Command Line: Intermediate

Learn more about the command line and how to use it in your data analysis workflow.

BASIC

### Git & Version Control

Learn the basics of Python programming and data science.

BASIC

### Spark & Map-Reduce

Learn how to use Apache Spark and the map-reduce technique to clean and analyze large datasets.

PREMIUM