PATH
Data Scientist
Learn how to make inferences and predictions from data.
This path will teach you the basics of Python and how to use it for data science
You’ll learn how to work with data sources, data cleaning techniques, how to perform statistical analyses, data visualization, and predictive analysis. In addition, at the of the path, you will learn how to:
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This path includes 51 free missions and 105 premium missions. [See plans]
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 Project Walkthrough
Learn how to clean and combine datasets, then practice your skills.
BASIC
Command Line: Beginner
Learn the basics of the command line, a critical part of any data science workflow.
BASIC
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
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 & Statistics: Intermediate
Learn more advanced statistical concepts 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
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
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
Spark & Map-Reduce
Learn how to use Apache Spark and the map-reduce technique to clean and analyze large datasets.
PREMIUM