Introduction to Python
Master the basics of data analysis with Python in just four hours. This online course will introduce the Python interface and explore popular packages.
Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
Investigating Netflix Movies
Apply the foundational Python skills you learned in Introduction to Python and Intermediate Python by manipulating and visualizing movie data.
Data Manipulation with pandas
Learn how to import and clean data, calculate statistics, and create visualizations with pandas.
Exploring NYC Public School Test Result Scores
Use data manipulation and summary statistics to analyze test scores across New York City's public schools!
Joining Data with pandas
Learn to combine data from multiple tables by joining data together using pandas.
Introduction to Statistics in Python
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.
Introduction to Data Visualization with Matplotlib
Learn how to create, customize, and share data visualizations using Matplotlib.
Introduction to Data Visualization with Seaborn
Learn how to create informative and attractive visualizations in Python using the Seaborn library.
Visualizing the History of Nobel Prize Winners
Explore a dataset containing a century's worth of Nobel Laureates. Who won? Who got snubbed?
Python Data Science Toolbox (Part 1)
Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.
Python Data Science Toolbox (Part 2)
Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
Intermediate Data Visualization with Seaborn
Use Seaborn's sophisticated visualization tools to make beautiful, informative visualizations with ease.
Exploratory Data Analysis in Python
Learn how to explore, visualize, and extract insights from data using exploratory data analysis (EDA) in Python.
Analyzing Crime in Los Angeles
Find out when and where crime is most likely to occur, along with the types of crimes commonly committed in LA.
Working with Categorical Data in Python
Learn how to manipulate and visualize categorical data using pandas and seaborn.
Customer Analytics: Preparing Data for Modeling
Apply your knowledge of data types and categorical data to prepare a big dataset for modeling!
Data Communication Concepts
No one enjoys looking at spreadsheets! Bring your data to life. Improve your presentation and learn how to translate technical data into actionable insights.
Introduction to Importing Data in Python
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
Cleaning Data in Python
Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!
Exploring Airbnb Market Trends
Apply your importing and cleaning data and data manipulation skills to explore New York City Airbnb data.
Writing Functions in Python
Learn to use best practices to write maintainable, reusable, complex functions with good documentation.
Introduction to Regression with statsmodels in Python
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.
Modeling Car Insurance Claim Outcomes
Clean customer data and use logistic regression to predict whether people will make a claim on their car insurance!
Sampling in Python
Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.
Hypothesis Testing in Python
Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.
Hypothesis Testing with Men's and Women's Soccer Matches
Perform a hypothesis test to determine if more goals are scored in women's soccer matches than men's!
Supervised Learning with scikit-learn
Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!
Predictive Modeling for Agriculture
Dive into agriculture using supervised machine learning and feature selection to aid farmers in crop cultivation and solve real-world problems.
Unsupervised Learning in Python
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
Clustering Antarctic Penguin Species
Arctic Penguin Exploration: Unraveling Clusters in the Icy Domain with K-means Clustering
Machine Learning with Tree-Based Models in Python
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
Prepare for certification by completing this track, or target the lessons you need with a personal learning plan.Learn More