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

Begin your journey into Data Science! Even if you've never written a line of code in your life, you'll be able to follow this course and witness the power of Python to perform Data Science. You'll use data to solve the mystery of Bayes, the kidnapped Golden Retriever, and along the way you'll become familiar with basic Python syntax and popular Data Science modules like Matplotlib (for charts and graphs) and pandas (for tabular data).

Dive into data science using Python and learn how to effectively analyze and visualize your data. No coding experience or skills needed.

Dive into data science using Python and learn how to effectively analyze and visualize your data using Pandas and Matplotlib. No coding experience needed.

Introduction to Data Science in Python

SQL is an essential skill for data scientists, and Microsoft SQL Server is one of the world's most popular database systems. In this course, you'll start with simple SELECT statements, and refine these queries with ORDER BY and WHERE clauses. You'll learn how to group and aggregate your results, and also how to work with strings.  You'll also cover the most common way to join tables, how to create tables, and inserting and updating data. You'll mainly work with the Chinook digital media database, representing sales of various artists and tracks. The music theme continues with the classic rock and Eurovision datasets, while we also look at trends in power outages in the US. Overall, you'll become proficient in the most common data manipulation tasks in SQL Server and build a solid foundation for the upcoming T-SQL courses here on DataCamp!

Become proficient at using SQL Server to perform common data manipulation tasks.

Learn how to use SQL Server to do common data manipulation tasks, like insert data, joining tables,  group and aggregate results, and work with strings.

Introduction to SQL Server

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

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

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

After all of the hard work of acquiring data and getting them into a form you can work with, you ultimately want to make clear, succinct conclusions from them. This crucial last step of a data analysis pipeline hinges on the principles of statistical inference. In this course, you will start building the foundation you need to think statistically, speak the language of your data, and understand what your data is telling you. The foundations of statistical thinking took decades to build, but can be grasped much faster today with the help of computers. With the power of Python-based tools, you will rapidly get up-to-speed and begin thinking statistically by the end of this course.

Build the foundation you need to think statistically and to speak the language of your data.

Statistical Thinking in Python (Part 1)

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

In this course, you'll learn natural language processing (NLP) basics, such as how to identify and separate words, how to extract topics in a text, and how to build your own fake news classifier. You'll also learn how to use basic libraries such as NLTK, alongside libraries which utilize deep learning to solve common NLP problems. This course will give you the foundation to process and parse text as you move forward in your Python learning.

Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.

Learn fundamental natural language processing (NLP) techniques using Python and how to apply them to extract insights from real-world text data in Python.

Introduction to Natural Language Processing in Python

From stock prices to climate data, time series data are found in a wide variety of domains, and being able to effectively work with such data is an increasingly important skill for data scientists. This course will introduce you to time series analysis in Python. After learning about what a time series is, you'll learn about several time series models ranging from autoregressive and moving average models to cointegration models. Along the way, you'll learn how to estimate, forecast, and simulate these models using statistical libraries in Python. You'll see numerous examples of how these models are used, with a particular emphasis on applications in finance.

In this course you'll learn the basics of analyzing time series data.

Learn the basics of analyzing time series data using several time series models including autoregressive, moving average, and cointegration in Python.

Time Series Analysis in Python

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

Version control is one of the power tools of programming. It allows you to keep track of what you did when, undo any changes you decide you don't want, and collaborate at scale with other people. This course will introduce you to Git, a modern version control tool that is very popular with data scientists and software developers, and show you how to use it to get more done in less time and with less pain.

This course is an introduction to version control with Git for data scientists. 

Discover Git, a popular and modern version control tool for data scientists, and how you can use it work more efficiently, quickly, and with fewer errors.

Introduction to Git

The ability to build tools capable of retrieving and parsing information stored across the internet has been and continues to be valuable in many veins of data science. In this course, you will learn to navigate and parse html code, and build tools to crawl websites automatically. Although our scraping will be conducted using the versatile Python library scrapy, many of the techniques you learn in this course can be applied to other popular Python libraries as well, including BeautifulSoup and Selenium. Upon the completion of this course, you will have a strong mental model of html structure, will be able to build tools to parse html code and access desired information, and create a simple scrapy spiders to crawl the web at scale.

Learn to retrieve and parse information from the internet using the Python library scrapy.

Web Scraping with Python

Web Scraping in Python

Deep learning methods use data to train neural network algorithms to do a variety of machine learning tasks, such as classification of different classes of objects. Convolutional neural networks are deep learning algorithms that are particularly powerful for analysis of images. This course will teach you how to construct, train and evaluate convolutional neural networks. You will also learn how to improve their ability to learn from data, and how to interpret the results of the training.

Learn powerful techniques for image analysis in Python using deep learning and convolutional neural networks in Keras.

Learn to construct, train and evaluate convolutional neural networks and improve their ability to learn from data to conduct image analysis using Keras.

Image Processing with Keras in Python

Linear algebra is one of the most important set of tools in applied mathematics and data science. In this course, you’ll learn how to work with vectors and matrices, solve matrix-vector equations, perform eigenvalue/eigenvector analyses and use principal component analysis to do dimension reduction on real-world datasets. All analyses will be performed in R, one of the world’s most-popular programming languages.

This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.


This data science course is an introduction to linear algebra. Learn how to use matrix-vector equations, perform eigenvalue/eigenvector analyses and pca.



Linear Algebra for Data Science in R

So you've learned how to aggregate and join data from tables in your database—now what? How do you manipulate, transform, and make the most sense of your data? This intermediate-level course will teach you several key functions necessary to wrangle, filter, and categorize information in a relational database, expand your SQL toolkit, and answer complex questions. You will learn the robust use of CASE statements, subqueries, and window functions—all while discovering some interesting facts about soccer using the European Soccer Database.

Master the complex SQL queries necessary to answer a wide variety of data science questions and prepare robust data sets for analysis in PostgreSQL.

Intermediate SQL

As a Data Scientist, the majority of your time should be spent gleaning actionable insights from data -- not waiting for your code to finish running. Writing efficient Python code can help reduce runtime and save computational resources, ultimately freeing you up to do the things you love as a Data Scientist. In this course, you'll learn how to use Python's built-in data structures, functions, and modules to write cleaner, faster, and more efficient code. We'll explore how to time and profile code in order to find bottlenecks. Then, you'll practice eliminating these bottlenecks, and other bad design patterns, using Python's Standard Library, NumPy, and pandas. After completing this course, you'll have the necessary tools to start writing efficient Python code!

Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.

Writing Efficient Python Code

You’ve already used SQL to query data from databases. But did you know that there's a lot more you can do with databases? You can model different phenomena in your data, as well as the relationships between them. This gives your data structure and consistency, which results in better data quality. In this course, you'll experience this firsthand by working with a real-life dataset that was used to investigate questionable university affiliations. Column by column, table by table, you'll get to unlock and admire the full potential of databases. You'll learn how to create tables and specify their relationships, as well as how to enforce data integrity. You'll also discover other unique features of database systems, such as constraints.

Learn how to create one of the most efficient ways of storing data - relational databases! 

Learn how to create tables and specify their relationships in SQL, as well as how to enforce data integrity and other unique features of database systems.

Introduction to Relational Databases in SQL

Visualizing data in plots and figures exposes the underlying patterns in the data and provides insights. Good visualizations also help you communicate your data to others, and are useful to data analysts and other consumers of the data. In this course, you will learn how to use Matplotlib, a powerful Python data visualization library. Matplotlib provides the building blocks to create rich visualizations of many different kinds of datasets. You will learn how to create visualizations for different kinds of data and how to customize, automate, and share these visualizations.

Learn how to create, customize, and share data visualizations using Matplotlib.

Introduction to Data Visualization with Matplotlib

Every day you read about the amazing breakthroughs in how the newest applications of machine learning are changing the world. Often this reporting glosses over the fact that a huge amount of data munging and feature engineering must be done before any of these fancy models can be used. In this course, you will learn how to do just that. You will work with Stack Overflow Developers survey, and historic US presidential inauguration addresses, to understand how best to preprocess and engineer features from categorical, continuous, and unstructured data. This course will give you hands-on experience on how to prepare any data for your own machine learning models.

Create new features to improve the performance of your Machine Learning models. 

Learn to prepare data for machine learning models by exploring how to preprocess and engineer features from categorical, continuous, and unstructured data.

Feature Engineering for Machine Learning in Python

One of the biggest challenges when studying data science technical skills is understanding how those skills and concepts translate into real jobs. Whether you're looking to level up in your marketing job by incorporating Python and pandas or you're trying to get a handle on what kinds of work a data scientist in a marketing organization might do, this course is a great match for you. We'll practice translating common business questions into measurable outcomes, including "How did this campaign perform?", "Which channel is referring the most subscribers?", "Why is a particular channel underperforming?" and more using a fake marketing dataset based on the data of an online subscription business. This course will build on Python and pandas fundamentals, such as merging/slicing datasets, groupby(), correcting data types and visualizing results using matplotlib.

Build up your pandas skills and answer marketing questions by merging, slicing, visualizing, and more!

Analyzing Marketing Campaigns with pandas

Continue your machine learning journey by diving into the wonderful world of ensemble learning methods! These are an exciting class of machine learning techniques that combine multiple individual algorithms to boost performance and solve complex problems at scale across different industries. Ensemble techniques regularly win online machine learning
competitions as well! In this course, you’ll learn all about these advanced ensemble techniques, such as bagging, boosting, and stacking. You’ll apply them to real-world datasets using cutting edge Python machine learning libraries such as scikit-learn, XGBoost, CatBoost, and mlxtend.

Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.

Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting and stacking.

Ensemble Methods in Python

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

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

Delivering data on a schedule can be a manual process. You write scripts, add complex cron tasks, and try various ways to meet an ever-changing set of requirements—and it’s even trickier to manage everything when working with teammates. Airflow can remove this headache by adding scheduling, error handling, and reporting to your workflows. In this course, you’ll master the basics of Airflow and learn how to implement complex data engineering pipelines in production. You'll also learn how to use Directed Acyclic Graphs (DAGs), automate data engineering workflows, and implement data engineering tasks in an easy and repeatable fashion—helping you to maintain your sanity.

Learn how to implement and schedule data engineering workflows.

Learn to implement and schedule data engineering workflows by adding scheduling, error handling, and reporting using Airflow and Airflow DAGs in Python. 

Introduction to Airflow in Python

Being able to leverage transactions and find and handle errors is critical to building resilient SQL scripts and working with databases. Transactions provide the protection needed to ensure that your data is consistent and operations work on the desired data in concurrent environments. Improper error handling can cause many serious and unexpected issues. Without the proper use of transactions and error handling, it's possible to make decisions based on incorrect data leading to false outcomes. In this course, we'll cover proper ways to use transactions and handle errors with a record of what went wrong. Additionally, we discuss how concurrently plays into the use of transactions and data outcomes. We'll practice these concepts on the FFEIC bank health data and with a patient data table.

Ensure data consistency by learning how to use transactions and handle errors in concurrent environments.

Transactions and Error Handling in PostgreSQL

Object-oriented programming (OOP) is a widely used programming paradigm that reduces development times—making it easier to read, reuse, and maintain your code. OOP shifts the focus from thinking about code as a sequence of actions to looking at your program as a collection of objects that interact with each other. In this course, you’ll learn how to create classes, which act as the blueprints for every object in Python. You’ll then leverage principles called inheritance and polymorphism to reuse and optimize code. Dive in and learn how to create beautiful code that’s clean and efficient!

Dive in and learn how to create classes and leverage inheritance and polymorphism to reuse and optimize code.

Object-Oriented Programming in Python

Take your Tableau skills up a notch with advanced analytics and visualizations. In this course, you’ll learn how to create detail-rich map visualizations, configure date and time fields to show trends over time, and extend your data using Calculated Fields. You’ll also apply your new skills to complete a customer analytics case study. Through hands-on activities, you’ll learn how to create bins, customize filters and interactions, and apply quick table calculations. Finally, you’ll learn power user techniques, including how to slice and dice data and apply dynamic sets and groups—bringing you one step closer to being Tableau Desktop Specialist certification-ready.

Take your Tableau skills up a notch with advanced analytics and visualizations.

Analyzing Data in Tableau

Over 50% of all corporate data is stored in the cloud. As cloud computing becomes more and more ubiquitous, mastering at least one cloud platform is now crucial for data practitioners. In this course, you’ll get to grips with AWS—the market-leading cloud provider. You’ll also learn about its main core services, best practices to design AWS applications, and the benefits of using AWS for businesses.
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This course is a great starting point to prepare you for the AWS Cloud Practitioner Certification.


Learn the fundamentals of cloud computing with AWS.

Learn about AWS cloud computing including benefits, applications, economics, and core services like storage, management, and well-architected framework.

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