Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with NumPy.
Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
Dive into data science using Python and learn how to effectively analyze and visualize your data. No coding experience or skills needed.
Use the world’s most popular Python data science package to manipulate data and calculate summary statistics.
Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.
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 to combine data from multiple tables by joining data together using pandas.
Learn how to create, customize, and share data visualizations using Matplotlib.
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.
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.
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.
Learn how to use NumPy arrays in Python to perform mathematical operations and wrangle data with the best of them!
Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.
Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!
Learn about the world of data engineering with an overview of all its relevant topics and tools!
Learn to use best practices to write maintainable, reusable, complex functions with good documentation.
Improve your Python data importing skills and learn to work with web and API data.
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 how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Use Seaborn's sophisticated visualization tools to make beautiful, informative visualizations with ease.
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in Python.
Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.
Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.
Learn how to work with dates and times in Python.
Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.
Learn to retrieve and parse information from the internet using the Python library scrapy.
This course introduces Python for financial analysis.
Learn to acquire data from common file formats and systems such as CSV files, spreadsheets, JSON, SQL databases, and APIs.
Build the foundation you need to think statistically and to speak the language of your data.
Learn how to write unit tests for your Data Science projects in Python using pytest.
Learn how to implement and schedule data engineering workflows.
Learn the fundamentals of working with big data with PySpark.
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.
In this course you'll learn the basics of working with time series data.
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
This course focuses on feature engineering and machine learning for time series data.
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Learn to process, transform, and manipulate images at your will.
Learn about string manipulation and become a master at using regular expressions.
Learn to start developing deep learning models with Keras.
In this course you'll learn how to get your cleaned data ready for modeling.
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.
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.
Use a variety of data manipulation techniques to explore different aspects of Lego's history!