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

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

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

Spreadsheet software is one of the most popular and powerful tools in data analysis. Millions of people use tools like Google Sheets or Microsoft Excel on a daily basis. Even the most experienced data scientists often started their careers with spreadsheets and still use it to test assumptions or to look at data for the first time. In this course, you will learn the basics of spreadsheets by working with rows, columns, addresses, and ranges. You will create your own formulas and learn how to use references.

Learn the basics of spreadsheets by working with rows, columns, addresses, and ranges. 

Discover the basics of spreadsheets by working with rows, columns, addresses, and ranges, then create your own formulas and learn how to use references.

Introduction to Spreadsheets

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

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

Get an introduction to the programming language Scala. You'll learn why and how companies like Netflix, Airbnb, and Morgan Stanley are choosing Scala for large-scale applications and data engineering infrastructure. You'll learn the basics of the language, including syntax and style, focusing on the most commonly used features in the Scala standard library. You'll learn by writing code for a real program that plays a computer version of the popular card game Twenty-One. You’ll get a taste of the value of a hybrid object-oriented and functional programming language, of which Scala is the foremost example. We recommend this course for learners with intermediate-level programming experience, which can be acquired in the listed prerequisites.

Begin your journey with Scala, a popular language for scalable applications and data engineering infrastructure.

Learn the basics of the Sacla language, including syntax and style, focusing on the most commonly used features in the Scala standard library.

Introduction to Scala

Tableau is a widely used business intelligence (BI) and analytics software trusted by companies like Amazon, Experian, and Unilever to explore, visualize, and securely share data in the form of Workbooks and Dashboards. With its user-friendly drag-and-drop functionality it can be used by everyone to quickly clean, analyze, and visualize your team’s data.

You’ll learn how to navigate Tableau’s interface and connect and present data using easy-to-understand visualizations. By the end of this training, you’ll have the skills you need to confidently explore Tableau and build impactful data dashboards. Let’s dive in.

Get started with Tableau, a widely used business intelligence (BI) and analytics software to explore, visualize, and securely share data.

Learn to navigate Tableau’s interface, connect and present data, and build easy-to-understand visualizations to build impactful dashboards for business.

Introduction to Tableau

If you surveyed a large number of data scientists and data analysts about which tasks are most common in their workday, cleaning data would likely be in almost all responses. This is the case because real-world data is messy. To help you tame messy data, this course teaches you how to clean data stored in a PostgreSQL database. You’ll learn how to solve common problems such as how to clean messy strings, deal with empty values, compare the similarity between strings, and much more. You’ll get hands-on practice with these tasks using interesting (but messy) datasets made available by New York City's Open Data program. Are you ready to whip that messy data into shape?

Learn to tame your raw, messy data stored in a PostgreSQL database to extract accurate insights.

Cleaning Data in PostgreSQL Databases

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.

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