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.
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 how to create, customize, and share data visualizations using Matplotlib.
Learn the nuts and bolts of LLMs and the revolutionary transformer architecture they are based on!
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.
Learn how to create informative and attractive visualizations in Python using the Seaborn library.
Improve your Python data importing skills and learn to work with web and API data.
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
Master the fundamentals of programming in Python. No prior knowledge required!
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.
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
Dive in and learn how to create classes and leverage inheritance and polymorphism to reuse and optimize code.
Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0 in Python.
Learn how to clean and prepare your data for machine learning!
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.
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.
This introductory course will help you hone the skills to build effective, performant, and reliable data pipelines.
Explore data structures such as linked lists, stacks, queues, hash tables, and graphs; and search and sort algorithms!
Learn to retrieve and parse information from the internet using the Python library scrapy.
Learn to use Python for financial analysis using basic skills, including lists, data visualization, and arrays.
Discover how to build AI-powered applications using LLMs, prompts, chains, and agents in LangChain.
Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.
In this course you will learn the details of linear classifiers like logistic regression and SVM.
In this course you'll learn the basics of working with time series data.
Learn how to manipulate and visualize categorical data using pandas and seaborn.
Learn about the world of data engineering in this short course, covering tools and topics like ETL and cloud computing.
Learn to start developing deep learning models with Keras.
Learn how to work with dates and times in Python.
In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.
Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Data Science problems.
Learn about string manipulation and become a master at using regular expressions.
Navigate and use the extensive repository of models and datasets available on the Hugging Face Hub.
This course focuses on feature engineering and machine learning for time series data.
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
In this four-hour course, you’ll learn the basics of analyzing time series data in Python.
Learn to create your own Python packages to make your code easier to use and share with others.
In this project, we will use data manipulation skills to zoom in on a time when Lego explored a new direction for their toy line!
Analyze the gender distribution of children's book writers and use sound to match names to gender.
You will explore the market capitalization of Bitcoin and other cryptocurrencies.
Learn to analyze Twitter data and do a deep dive into a hot trend.
Analyze the network of characters in Game of Thrones and how it changes over the course of the books.
Explore Disney movie data, then build a linear regression model to predict box office success.
Import, clean, and analyze seven years worth of training data tracked on the Runkeeper app.
Process ingredient lists for cosmetics on Sephora then visualize similarity using t-SNE and Bokeh.
Use data manipulation and visualization to explore one of two different television broadcast datasets: The Super Bowl and hit sitcom The Office!
Use pandas to calculate and compare profitability and risk of different investments using the Sharpe Ratio.
Use NLP and clustering on movie plot summaries from IMDb and Wikipedia to quantify movie similarity.
Build a binary classifier to predict if a blood donor is likely to donate again.
Load, clean, and visualize scraped Google Play Store data to gain insights into the Android app market.
Scrape news headlines for FB and TSLA then apply sentiment analysis to generate investment insight.
Build a book recommendation system using NLP and the text of books like "On the Origin of Species."
Build a deep learning model that can automatically detect honey bees and bumble bees in images.
Manipulate and plot time series data from Google Trends to analyze changes in search interest over time.
Build a convolutional neural network to classify images of letters from American Sign Language.
Play bank data scientist and use regression discontinuity to see which debts are worth collecting.
Use pandas and Bayesian statistics to see if left-handed people actually die earlier than righties.
Flex your data manipulation muscles on breath alcohol test data from Ames, Iowa, USA.
Build a machine learning classifier that knows whether President Trump or Prime Minister Trudeau is tweeting!
How can we find a good strategy for reducing traffic-related deaths?
Rock or rap? Apply machine learning methods in Python to classify songs into genres.
Build a model that can automatically detect honey bees and bumble bees in images.
Automatically generate keywords for a search engine marketing campaign using Python.
Load, transform, and understand images of honey bees and bumble bees in Python.
Use MLB's Statcast data to compare New York Yankees sluggers Aaron Judge and Giancarlo Stanton.
Join us at a leading insurance company, where we'll craft a model to predict customer charges and test it with new client data
Define functions to catch errors when new users register for an app!
Create custom Python functions to validate user input!
Manipulate date and time using Python
Leverage machine learning algorithms and models for marketing analytics tasks in a streaming platform.
Automate e-commerce processes with image classification.
Solve the Taxi-v3 environment using Q-learning, ensuring efficient AI-driven transportation.
Use MLB's Statcast data to compare New York Yankees sluggers Aaron Judge and Giancarlo Stanton.
Help the bank monitoring their fraud detection model and figuring out why it's not performing as expected.
Automatically generate keywords for a search engine marketing campaign using Python to send website visitors to the right landing page.
Use LLMs to solve diverse language tasks for a car dealership company.
Dive into sleep data and gain insights about factors that impact sleep quality
Analyze clothing reviews on an e-commerce platform to explore different topics and similarities among them.
Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.
Build models predicting customer churn for Indian telecom customers.
Chart electric vehicle charging trends to inform strategic planning.
Examine flight delays & cancellations to uncover insights.
Sometimes, things that once worked perfectly suddenly hit a snag. Practice your knowledge of DataFrames to find the problem and fix it!
Build a machine learning model to predict if a credit card application will get approved.
Explore a dataset containing a century's worth of Nobel Laureates. Who won? Who got snubbed?
Review a data analysis workflow for adherence to Python standards and best-practices.
Find out when and where crime is most likely to occur, along with the types of crimes commonly committed in LA.