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.
Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
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.
Learn how to import and clean data, calculate statistics, and create visualizations with pandas.
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!
Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.
Learn the nuts and bolts of LLMs and the revolutionary transformer architecture they are based on!
Learn how to create, customize, and share data visualizations using Matplotlib.
Learn how to explore, visualize, and extract insights from data using exploratory data analysis (EDA) in Python.
Learn to combine data from multiple tables by joining data together using pandas.
Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
Improve your Python data importing skills and learn to work with web and API data.
Learn how to create informative and attractive visualizations in Python using the Seaborn library.
Master the fundamentals of programming in Python. No prior knowledge required!
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!
Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.
Dive into data science using Python and learn how to effectively analyze and visualize your data. No coding experience or skills needed.
Dive into the Python ecosystem, discovering modules and packages along with how to write custom functions!
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
Dive in and learn how to create classes and leverage inheritance and polymorphism to reuse and optimize code.
Learn how to clean and prepare your data for machine learning!
Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0 in Python.
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.
This introductory course will help you hone the skills to build effective, performant, and reliable data pipelines.
Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.
Learn to use best practices to write maintainable, reusable, complex functions with good documentation.
Master your skills in NumPy by learning how to create, sort, filter, and update arrays using NYC’s tree census.