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 to combine data from multiple tables by joining data together using pandas.
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
Learn how to explore, visualize, and extract insights from data using exploratory data analysis (EDA) in Python.
Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
Learn how to create, customize, and share data visualizations using Matplotlib.
Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!
Learn how to create informative and attractive visualizations in Python using the Seaborn library.
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.
Learn the fundamentals of AI. No programming experience required!
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Learn about the world of data engineering in this short course, covering tools and topics like ETL and cloud computing.
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.
Improve your Python data importing skills and learn to work with web and API data.
Learn to use best practices to write maintainable, reusable, complex functions with good documentation.
Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.
Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.
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.
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
Build the foundation you need to think statistically and to speak the language of your data.
Use Seaborn's sophisticated visualization tools to make beautiful, informative visualizations with ease.
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.
Learn to retrieve and parse information from the internet using the Python library scrapy.
Learn how to manipulate and visualize categorical data using pandas and seaborn.
Learn how to work with dates and times in Python.
Learn to use Python for financial analysis using basic skills, including lists, data visualization, and arrays.
Master your skills in NumPy by learning how to create, sort, filter, and update arrays using NYC’s tree census.
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.
Learn the fundamentals of working with big data with PySpark.
Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Data Science problems.
In this course you'll learn the basics of working with time series data.
Learn about string manipulation and become a master at using regular expressions.
In this four-hour course, you’ll learn the basics of analyzing time series data in Python.
In this course you'll learn how to get your cleaned data ready for modeling.
In this course you will learn the details of linear classifiers like logistic regression and SVM.
Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.
Learn to process, transform, and manipulate images at your will.
Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.
Learn how to implement and schedule data engineering workflows.
Learn to create deep learning models with the PyTorch library.
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 machine learning model to predict if a credit card application will get approved.
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.
Explore a dataset from Kaggle containing a century's worth of Nobel Laureates. Who won? Who got snubbed?
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.
Use web scraping and NLP to find the most frequent words in one of two pieces of classic literature: Herman Melville's novel, Moby Dick, or Peter Pan by J. M. Barrie.
Load, transform, and understand images of honey bees and bumble bees in Python.
If you've never done a DataCamp project, this is the place to start!
Use MLB's Statcast data to compare New York Yankees sluggers Aaron Judge and Giancarlo Stanton.
Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.
Analyze an A/B test from the popular mobile puzzle game, Cookie Cats.
Find the true Scala experts by exploring its development history in Git and GitHub.
Use Natural Language Processing on NIPS papers to uncover the trendiest topics in machine learning research.
Use data manipulation, cleaning, and feature engineering skills to prepare a payment dataset for fraud prediction modeling.
Check what passwords fail to conform to the National Institute of Standards and Technology password guidelines.
Use joining techniques to discover the oldest businesses in the world.
Apply data importing and cleaning skills to extract insights about the New York City Airbnb market.
Recreate John Snow's famous map of the 1854 cholera outbreak in London.
Apply the foundational Python skills you learned in Introduction to Python and Intermediate Python by manipulating and visualizing movie and TV data.
Use coding best practices and functions to improve a script!
Use DataFrames to read and merge employee data from different sources.
Load, clean, and visualize scraped Google Play Store data to gain insights into the Android app market.
Manipulate and plot time series data from Google Trends to analyze changes in search interest over time.
Apply the foundational Python skills you learned in Introduction to Python and Intermediate Python by manipulating and visualizing movie and TV data.
Find out about the evolution of the Linux operating system by exploring its version control system.
In this project, you will use importing and text manipulation skills to find out the main protagonists in Peter Pan!
Check what passwords fail to conform to the National Institute of Standards and Technology password guidelines.