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

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

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

Now that you've learned the basics of SQL in our <a href="https://learn.datacamp.com/courses/introduction-to-sql">Introduction to SQL</a> course, it's time to supercharge your queries using joins and relational set theory. In this course, you'll learn all about the power of joining tables while exploring interesting features of countries and their cities throughout the world. You will master inner and outer joins, as well as self joins, semi joins, anti joins and cross joins—fundamental tools in any PostgreSQL wizard's toolbox. Never fear set theory again after learning all about unions, intersections, and except clauses through easy-to-understand diagrams and examples. Lastly, you'll be introduced to the challenging topic of subqueries. You will be able to visually grasp these ideas by using Venn diagrams and other linking illustrations.

Join two or three tables together into one, combine tables using set theory, and work with subqueries in PostgreSQL.

Discover all the fundamental in PostgreSQL, like inner, outer self, semi, anti, and cross joins, as well as unions, intersections, and except clauses.

Joining Data in SQL

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

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)

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.
 
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 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)

Have you ever wondered how data professionals use SQL to solve real-world business problems, like generating rankings, calculating moving averages and running totals, deduplicating data, or performing time intelligence? If you already know how to select, filter, order, join and group data with SQL, this course is your next step. By the end, you will be writing queries like a pro! You will learn how to create queries for analytics and data engineering with window functions, the SQL secret weapon! Using flights data, you will discover how simple it is to use window functions, and how flexible and efficient they are.

Learn how to create queries for analytics and data engineering with window functions, the SQL secret weapon! 

PostgreSQL Summary Stats and Window Functions

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

This course will provide you an understanding of how to use built-in PostgreSQL functions in your SQL queries to manipulate different types of data including strings, character, numeric and date/time. We'll travel back to a time where Blockbuster video stores were on every corner and if you wanted to
watch a movie, you actually had to leave your house to rent a DVD! You'll also get an introduction into the robust full-text search capabilities which provides a powerful tool for indexing and matching keywords in a PostgreSQL document. And finally, you'll learn how to extend these features by using PostgreSQL extensions.

Learn the most important PostgreSQL functions for manipulating, processing, and transforming data. 

Learn how to use built-in PostgreSQL functions in your SQL queries to manipulate different types of data like strings, character, numeric and date/time.

Functions for Manipulating Data in PostgreSQL

Data scientists can experience huge benefits by learning concepts from the field of software engineering, allowing them to more easily reutilize their code and share it with collaborators. In this course, you'll learn all about the important ideas of modularity, documentation, & automated testing, and you'll see how they can help you solve Data Science problems quicker and in a way that will make future you happy. You'll even get to use your acquired software engineering chops to write your very own Python package for performing text analytics.

Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.

Software Engineering for Data Scientists in Python

Confused about NoSQL and how it differs from SQL? You've come to the right place!
 
 
In this conceptual course (no coding required), you’ll be introduced to learn the four major NoSQL databases, including key-value, document, column family, and graph. You’ll learn about four popular NoSQL engines—including Redis, MongoDB, Apache Cassandra, and Neo4j—and when to apply them to achieve a specific business requirement. You’ll follow the data escapades of a fictional social network and learn how NoSQL can help them handle and extract insights from unstructured data like social posts. Lastly, you’ll study real use cases of when NoSQL databases were used—giving you the knowledge you need to effectively store data in any situation.

In this conceptual course (no coding required), you will learn about the four major NoSQL databases and popular engines.

NoSQL Concepts

Are you ready to gain the foundational skills you need to become a Python programmer? In this track, you'll learn the Python basics you need to start on your programming journey, including how to clean real-world data ready for analysis, use data visualization libraries, and even how to write your own Python functions.

Your instructor Hugo will introduce you to how companies worldwide use Python to gain a competitive edge. Through hands-on coding exercises you’ll then learn how to store, manipulate, and explore data using NumPy. Then it’s time to level-up as you learn how to visualize your data using Matplotlib, manipulate DataFrames and dictionaries using pandas, and write your own functions and list comprehension. Start this track to add these essential Python skills to your data science toolbox.

Grow your programmer skills. Discover how to manipulate dictionaries and DataFrames, visualize real-world data, and write your own Python functions.

Python Fundamentals

Gain the fundamental skills you need to interact with and query your data in SQL—a powerful language used by data-driven businesses large and small to explore and manipulate their data to extract meaningful insights.

In this track, you'll learn the skills you need to level up your data skills and leave Excel behind you. Through hands-on exercises, you’ll discover how to quickly summarize, join tables, and use window functions and built-in PostgreSQL functions to analyze your data. 

Learn the fundamental business skills you need to write SQL queries and start analyzing your data using this powerful language.

SQL Fundamentals

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

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

Have you ever come across a website that displays a lot of data such as statistics, product reviews, or prices in a format that’s not data analysis-ready? Often, authorities and other data providers publish their data in neatly formatted tables. However, not all of these sites include a download button, but don’t despair. In this course, you’ll learn how to efficiently collect and download data from any website using R. You'll learn how to automate the scraping and parsing of Wikipedia using the rvest and httr packages. Through hands-on exercises, you’ll also expand your understanding of HTML and CSS, the building blocks of web pages, as you make your data harvesting workflows less error-prone and more efficient.

Learn how to efficiently collect and download data from any website using R.

Learn how to use R to collect and download data from any website and automate the scraping and parsing of Wikipedia using the rvest and httr packages.

Web Scraping in R

Power BI has extraordinary visuals that can be used in reports and dashboards.
In this Power BI course, you’ll learn to create insightful visualizations through built-in and customized charts and conditional formatting. You’ll discover how to create a plethora of visualizations such as scatter plots, tornado charts, gauges, and how to visualize everything without overwhelming your audience.

Power BI is a powerful data visualization tool that can be used in reports and dashboards.

Learn how to use Power BI to create insightful dashboards and reports, as well as data visualization best practices.

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