Master the basics of data analysis in Python. Expand your skill set by learning scientific computing with numpy.
Level up your data science skills by creating visualizations using matplotlib and manipulating data frames with Pandas.
Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.
Learn how to use the industry-standard pandas library to import, build, and manipulate DataFrames.
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
Learn more complex data visualization techniques using Matplotlib and Seaborn.
Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.
Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
Learn how to build and tune predictive models and evaluate how well they will perform on unseen data.
This course will equip you with all the skills you need to clean your data in Python.
Build the foundation you need to think statistically and to speak the language of your data.
Dive into data science using Python and learn how to effectively analyze and visualize your data.
Improve your Python data importing skills and learn to work with web and API data.
You will learn how to tidy, rearrange, and restructure your data using versatile pandas DataFrames.
Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.
Learn efficient techniques in pandas to optimize your Python code.
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
This course is all about the act of combining, or merging, DataFrames, an essential part your Data Scientist's toolbox.
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Learn how to use Python to analyze customer churn and build a model to predict it.
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
Learn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning.
Learn how to create versatile and interactive data visualizations using Bokeh.
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'll learn the basics of relational databases and how to interact with them.
Learn the fundamentals of object-oriented programming: classes, objects, methods, inheritance, polymorphism, and others!
This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.
Learn to build pipelines that stand the test of time.
Learn to retrieve and parse information from the internet using the Python library scrapy.
Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.
Learn how to create, customize, and share data visualizations using Matplotlib.
Learn how to build a model to automatically classify items in a school budget.
Use your knowledge of common spreadsheet functions and techniques to explore Python!
Learn how to import, clean and manipulate IoT data in Python to make it ready for machine learning.
In this course you'll learn the basics of analyzing time series data.
This course introduces Python for financial analysis.
Learn to reduce dimensionality in Python.
Learn the fundamentals of working with big data with PySpark.
Convolutional neural networks are deep learning algorithms that are particularly powerful for analysis of images.
Build multiple-input and multiple-output deep learning models using Keras.
Create new features to improve the performance of your Machine Learning models.
Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.
This course focuses on feature engineering and machine learning for time series data.
Learn about string manipulation and become a master at using regular expressions.
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
In this course, you'll learn how to import and manage financial data in Python using various tools and sources.
In this course you will learn the details of linear classifiers like logistic regression and SVM.
Learn how to create informative and attractive visualizations in Python using the Seaborn library.
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.
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.
Process ingredient lists for cosmetics on Sephora then visualize similarity using t-SNE and Bokeh.
Load, clean, and explore Super Bowl data in the age of soaring ad costs and flashy halftime shows.
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 understand 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.
Plot Google Trends data to find the most famous Kardashian/Jenner sister. Is it Kim? Kendall? Kylie?
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 pandas muscles on breath alcohol test data from Ames, Iowa, USA.
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 Herman Melville's novel, Moby Dick.
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.
Recreate John Snow's famous map of the 1854 cholera outbreak in London.
Find out about the development of the Linux operating system by exploring its Git repository history.
In this project we will explore a database of every LEGO set ever built.
Intro to Python
Intro to R
Intro to SQL
SQL