Python is a general-purpose programming language that is becoming ever more popular for data science. Companies worldwide are using Python to harvest insights from their data and gain a competitive edge. Unlike other Python tutorials, this course focuses on Python specifically for data science. In our Introduction to Python course, you’ll learn about powerful ways to store and manipulate data, and helpful data science tools to begin conducting your own analyses. Start DataCamp’s online Python curriculum now.

Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with NumPy.

Discover how to use Python for data science, learning about the ways to store and manipulate data in the environment to begin conducting your own analyses.

Introduction to Python

Learning Python is crucial for any aspiring data science practitioner. Learn to visualize real data with Matplotlib's functions and get acquainted with data structures such as the dictionary and the pandas DataFrame. After covering key concepts such as boolean logic, control flow, and loops in Python, you'll be ready to blend together everything you've learned to solve a case study using hacker statistics.

Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.

In this intermediate Python course, learn to visualize real data with Matplotlib's functions and get to know new data structures such as the pandas DataFrame.

Intermediate Python

In 2019, the average salary for data engineers overtook data scientists. How did this happen? Companies wanting to find the gold within their data realized it wasn’t possible if they hadn’t yet built the mine. Data engineers lay the foundations that make data science possible.
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In this course, you’ll learn about a data engineer’s core responsibilities, how they differ from data scientists, and facilitate the flow of data through an organization. Through hands-on exercises you’ll follow Spotflix, a fictional music streaming company, to understand how their data engineers collect, clean, and catalog their data. By the end of the course, you’ll understand what your company's data engineers do, be ready to have a conversation with a data engineer, and have a solid foundation to start your own data engineer journey.


Discover how data engineers lay the groundwork that makes data science possible. No coding involved!

Data Engineering for Everyone

In this course, you'll learn how to use Spark from Python! Spark is a tool for doing parallel computation with large datasets and it integrates well with Python. PySpark is the Python package that makes the magic happen. You'll use this package to work with data about flights from Portland and Seattle. You'll learn to wrangle this data and build a whole machine learning pipeline to predict whether or not flights will be delayed. Get ready to put some Spark in your Python code and dive into the world of high-performance machine learning!

Learn to implement distributed data management and machine learning in Spark using the PySpark package.

Learn to wrangle data and build a machine learning pipeline to make predictions with  PySpark Python package. Practice your skills with real-world data.

Introduction to PySpark

Decision trees are supervised learning models used for problems involving classification and regression. Tree models present a high flexibility that comes at a price: on one hand, trees are able to capture complex non-linear relationships; on the other hand, they are prone to memorizing the noise present in a dataset. By aggregating the predictions of trees that are trained differently, ensemble methods take advantage of the flexibility of trees while reducing their tendency to memorize noise. Ensemble methods are used across a variety of fields and have a proven track record of winning many machine learning competitions.
In this course, you'll learn how to use Python to train decision trees and tree-based models with the user-friendly scikit-learn machine learning library. You'll understand the advantages and shortcomings of trees and demonstrate how ensembling can alleviate these shortcomings, all while practicing on real-world datasets. Finally, you'll also understand how to tune the most influential hyperparameters in order to get the most out of your models.

In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn. 

Machine Learning with Tree-Based Models in Python

Before you can analyze data, you first have to acquire it. This course teaches you how to build pipelines to import data kept in common storage formats. You’ll use pandas, a major Python library for analytics, to get data from a variety of sources, from spreadsheets of survey responses, to a database of public service requests, to an API for a popular review site. Along the way, you’ll learn how to fine-tune imports to get only what you need and to address issues like incorrect data types. Finally, you’ll assemble a custom dataset from a mix of sources.

Learn to acquire data from common file formats and systems such as CSV files, spreadsheets, JSON, SQL databases, and APIs.

Streamlined Data Ingestion with pandas

In this course, you will learn techniques that will allow you to extract useful information from text and process them into a format suitable for applying ML models. More specifically, you will learn about POS tagging, named entity recognition, readability scores, the n-gram and tf-idf models, and how to implement them using scikit-learn and spaCy. You will also learn to compute how similar two documents are to each other.  In the process, you will predict the sentiment of movie reviews and build movie and Ted Talk recommenders. Following the course, you will be able to engineer critical features out of any text and solve some of the most challenging problems in data science!

Learn techniques to extract useful information from text and process them into a format suitable for machine learning.

Learn the techniques in Python to extract useful information from text and process them into a format suitable for applying to machine learning models.

Feature Engineering for NLP in Python

If you're working with a lot of text, you'll eventually want to know more about it. For example, what's it about? What do the words mean in context? Who is doing what to whom? What companies and products are mentioned? Which texts are similar to each other? In this course, you'll learn how to use spaCy, a fast-growing industry standard library for NLP in Python, to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

Advanced NLP with spaCy

Do you find yourself copying and pasting the same code between files, wishing it was easier to reuse and share your awesome snippets? Wrapping your code into Python packages can help! In this course, you’ll learn about package structure and the extra files needed to turn loose code into convenient packages. You'll also learn about import structure, documentation, and how to maintain code style using flake8. You’ll then speed up your package development by building templates, using cookiecutter to create package skeletons. Finally, you'll learn how to use setuptools and twine to build and publish your packages to PyPI—the world stage for Python packages.

Learn to create your own Python packages to make your code easier to use and share with others.

Learn about package structure, import structure, documentation, and how to maintain code style then build templates and use setup tools to publish them.

Developing Python Packages

Twitter produces hundreds of million messages per day, with people around the world discussing sports, politics, business, and entertainment. You can access thousands of messages flowing in this stream in a matter of minutes. In this course, you will learn how to collect Twitter data and analyze tweet text, Twitter networks, and the geographical origin of the tweet. We'll be doing this with datasets on tech companies, data science hashtags, and the 2018 State of the Union address. Using these methods, you will be able to inform business and political decision-making by discovering the prevalence of important topics, the diversity of discussion networks, and a topic's geographical reach.

In this course, you'll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.

Analyzing Social Media Data in Python

Time series data is all around us; some examples are the weather, human behavioral patterns as consumers and members of society, and financial data. In this course, you'll learn how to calculate technical indicators from historical stock data, and how to create features and targets out of the historical stock data. You'll understand how to prepare our features for linear models, xgboost models, and neural network models. We will then use linear models, decision trees, random forests, and neural networks to predict the future price of stocks in the US markets. You will also learn how to evaluate the performance of the various models we train in order to optimize them, so our predictions have enough accuracy to make a stock trading strategy profitable.

Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.

Learn to model & predict stock data values using linear models, decision trees, random forests, and neural networks.

Machine Learning for Finance in Python

Are you looking to land that next job or hone your statistics interview skills to stay sharp? Get ready to master classic interview concepts ranging from conditional probabilities to A/B testing to the bias-variance tradeoff, and much more! You’ll work with a diverse collection of datasets including web-based experiment results and Australian weather data. Following the course, you’ll be able to confidently walk into your next interview and tackle any statistics questions with the help of Python!

Prepare for your next statistics interview by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more. 

Practicing Statistics Interview Questions in Python 

In this introduction to R course, you'll master the basics of this widely used open source language&mdash;including vectors, factors, lists, and data frames. With the coding skills you'll gain in this course, you'll be ready to undertake your own data analysis in R. There are millions of R users worldwide, cementing it as a leading programming language in statistics and data science. More and more organizations are using R, so begin your coding journey in one of DataCamp's most popular courses today!

Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.

DataCamp's Into to R training course teaches you how to use R programming for data science at your own pace with video tutorials & interactive challenges.

Introduction to R Online

Introduction to R

In this second Python Data Science Toolbox course, you'll continue to build your Python data science skills. First, you'll learn about iterators, objects you have already encountered in the context of for loops. You'll then learn about list comprehensions, which are extremely handy tools for all data scientists working in Python. You'll end the course by working through a case study in which you'll apply all the techniques you learned in both parts of this course.

Continue to build your modern Data Science skills by learning about iterators and list comprehensions.

Python Data Science Toolbox (Part 2)

It's time to push forward and develop your Python chops even further. There are tons of fantastic functions in Python and its library ecosystem. However, as a data scientist, you'll constantly need to write your own functions to solve problems that are dictated by your data. You will learn the art of function writing in this first Python Data Science Toolbox course. You'll come out of this course being able to write your very own custom functions, complete with multiple parameters and multiple return values, along with default arguments and variable-length arguments. You'll gain insight into scoping in Python and be able to write lambda functions and handle errors in your function writing practice. And you'll wrap up each chapter by using your new skills to write functions that analyze Twitter DataFrames.

Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.

Python Data Science Toolbox (Part 1)

Machine learning is the field that teaches machines and computers to learn from existing data to make predictions on new data: Will a tumor be benign or malignant? Which of your customers will take their business elsewhere? Is a particular email spam? In this course, you'll learn how to use Python to perform supervised learning, an essential component of machine learning. You'll learn how to build predictive models, tune their parameters, and determine how well they will perform with unseen data—all while using real world datasets. You'll be using scikit-learn, one of the most popular and user-friendly machine learning libraries for Python.

Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.

Supervised Learning with scikit-learn

The role of a data scientist is to turn raw data into actionable insights. Much of the world's raw data—from electronic medical records to customer transaction histories—lives in organized collections of tables called relational databases. To be an effective data scientist, you must know how to wrangle and extract data from these databases using a language called SQL . This course teaches syntax in SQL shared by many types of databases, such as PostgreSQL, MySQL, SQL Server, and Oracle. This course teaches you everything you need to know to begin working with databases today!

Master the basics of querying tables in relational databases such as MySQL, SQL Server, and PostgreSQL.

Learn the basics of syntax in SQL, how to extract and wrangle data, and use querying tables in relational databases like MySQL, SQL Server, and PostgreSQL.

Introduction to SQL

Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition, and artificial intelligence, including the famous AlphaGo. In this course, you'll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting-edge library for deep learning in Python.

Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.

Discover how you can use deep learning to run natural language processing, image recognition, and artificial intelligence with Python package, Keras 2.0.

Introduction to Deep Learning in Python

Say you have a collection of customers with a variety of characteristics such as age, location, and financial history, and you wish to discover patterns and sort them into clusters. Or perhaps you have a set of texts, such as Wikipedia pages, and you wish to segment them into categories based on their content. This is the world of unsupervised learning, called as such because you are not guiding, or supervising, the pattern discovery by some prediction task, but instead uncovering hidden structure from unlabeled data. Unsupervised learning encompasses a variety of techniques in machine learning, from clustering to dimension reduction to matrix factorization. In this course, you'll learn the fundamentals of unsupervised learning and implement the essential algorithms using scikit-learn and SciPy. You will learn how to cluster, transform, visualize, and extract insights from unlabeled datasets, and end the course by building a recommender system to recommend popular musical artists.

Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

Unsupervised Learning in Python

Do you know the basics of supervised learning and want to use state-of-the-art models on real-world datasets? Gradient boosting is currently one of the most popular techniques for efficient modeling of tabular datasets of all sizes. XGboost is a very fast, scalable implementation of gradient boosting, with models using XGBoost regularly winning online data science competitions and being used at scale across different industries. In this course, you'll learn how to use this powerful library alongside pandas and scikit-learn to build and tune supervised learning models. You'll work with real-world datasets to solve classification and regression problems.

Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.

Explore the fundamentals of gradient boosting, with a focus on Regression with XGBoost, using XGBoost in pipelines and how to fine-tune your XGBoost model.

Extreme Gradient Boosting with XGBoost

If you want to apply your new 'Python for Data Science' skills to real-world financial data, then this course will give you some very valuable tools.
First, you will learn how to get data out of Excel into pandas and back. Then, you will learn how to pull stock prices from various online APIs like
Google or Yahoo! Finance, macro data from the Federal Reserve, and exchange rates from OANDA. Finally, you will learn how to calculate returns for various time horizons,
analyze stock performance by sector for IPOs, and calculate and summarize correlations.


In this course, you'll learn how to import and manage financial data in Python using various tools and sources.

Learn how to pull stock prices from online APIs to calculate returns for time horizons, analyze stock performance by sector, and summarize correlations.

Importing and Managing Financial Data in Python

In Financial Forecasting in Python, you will step into the role of CFO and learn how to advise a board of directors on key metrics while building a financial forecast, the basics of income statements and balance sheets, and cleaning messy financial data. During the course, you will examine real-life datasets from Netflix, Tesla, and Ford, using the pandas package. Following the course, you will be able to calculate financial metrics, work with assumptions and variances, and build your own forecast in Python!

Step into the role of CFO and learn how to advise a board of directors on key metrics while building a financial forecast.

Learn how to clean messy financial data, calculate financial metrics, work with assumptions and variances, and build your own financial forecast in Python.

Financial Forecasting in Python

There's been a lot of buzz about Big Data over the past few years, and it's finally become mainstream for many  companies. But what is this Big Data? This course covers the fundamentals of Big Data via PySpark. Spark is a "lightning fast cluster computing" framework for Big Data. It provides a general data processing platform engine and lets you run programs up to 100x faster in memory, or 10x faster on disk,  than Hadoop. You’ll use PySpark, a Python package for Spark programming and its powerful, higher-level libraries such as SparkSQL, MLlib (for machine learning), etc. You will explore the works of William Shakespeare, analyze Fifa 2018 data and perform clustering on genomic datasets. At the end of this course, you will have gained an in-depth understanding of PySpark and its application to general Big Data analysis.

Learn the fundamentals of working with big data with PySpark.

Learn to conduct Big Data analysis using the Python package for Spark programming, PySpark, as well as higher level libraries like SparkSQL and MLlib.

Big Data Fundamentals with PySpark

High-dimensional datasets can be overwhelming and leave you not knowing where to start. Typically, you’d visually explore a new dataset first, but when you have too many dimensions the classical approaches will seem insufficient. Fortunately, there are visualization techniques designed specifically for high dimensional data and you’ll be introduced to these in this course. After exploring the data, you’ll often find that many features hold little information because they don’t show any variance or because they are duplicates of other features. You’ll learn how to detect these features and drop them from the dataset so that you can focus on the informative ones. In a next step, you might want to build a model on these features, and it may turn out that some don’t have any effect on the thing you’re trying to predict. You’ll learn how to detect and drop these irrelevant features too, in order to reduce dimensionality and thus complexity. Finally, you’ll learn how feature extraction techniques can reduce dimensionality for you through the calculation of uncorrelated principal components.

Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.

Learn to reduce dimensionality in Python by detecting these features and dropping them from the dataset so that you can focus on the informative ones.

Dimensionality Reduction in Python

Spark is a powerful, general purpose tool for working with Big Data. Spark transparently handles the distribution of compute tasks across a cluster. This means that operations are fast, but it also allows you to focus on the analysis rather than worry about technical details. In this course you'll learn how to get data into Spark and then delve into the three fundamental Spark Machine Learning algorithms: Linear Regression, Logistic Regression/Classifiers, and creating pipelines. Along the way you'll analyse a large dataset of flight delays and spam text messages. With this background you'll be ready to harness the power of Spark and apply it on your own Machine Learning projects!

Learn how to make predictions with Apache Spark.

Learn how to make predictions from data with PySpark in Python, using decision trees, logistic regression, linear regression, ensembles, and pipelines.

Machine Learning with PySpark

Kaggle is the most famous platform for Data Science competitions. Taking part in such competitions allows you to work with real-world datasets, explore various machine learning problems, compete with other participants and, finally, get invaluable hands-on experience. In this course, you will learn how to approach and structure any Data Science competition. You will be able to select the correct local validation scheme and to avoid overfitting. Moreover, you will master advanced feature engineering together with model ensembling approaches. All these techniques will be practiced on Kaggle competitions datasets.

Learn how to approach and win competitions on Kaggle.

Find out what it takes to win Kaggle competitions, from having the right approach to choosing the right validation scheme for any data science competition.

Winning a Kaggle Competition in Python

As a data scientist, you will encounter many situations where you will need to extract key information from huge corpora of text, clean messy data containing strings, or detect and match patterns to find useful words. All of these situations are part of text mining and are an important step before applying machine learning algorithms. This course will take you through understanding compelling concepts about string manipulation and regular expressions. You will learn how to split strings, join them back together, interpolate them, as well as detect, extract, replace, and match strings using regular expressions. On the journey to master these skills, you will work with datasets containing movie reviews or streamed tweets that can be used to determine opinion, as well as with raw text scraped from the web.

Learn about string manipulation and become a master at using regular expressions.

Regular Expressions in Python

What's behind the machine learning hype? In this non-technical course, you’ll learn everything you’ve been too afraid to ask about machine learning. There’s no coding required. Hands-on exercises will help you get past the jargon and learn how this exciting technology powers everything from self-driving cars to your personal Amazon shopping suggestions.

How does machine learning work, when can you use it, and what is the difference between AI and machine learning? They’re all covered. Gain skills in this hugely in-demand and influential field, and discover why machine learning is for everyone!


An introduction to machine learning with no coding involved.

Discover what machine learning is, how it works, and how you can use it through hands-on exercise. There is no need to know how to code to learn about ML.

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