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

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

This is an introduction to the programming language R, focused on a powerful set of tools known as the Tidyverse. You'll learn the intertwined processes of data manipulation and visualization using the tools dplyr and ggplot2. You'll learn to manipulate data by filtering, sorting, and summarizing a real dataset of historical country data in order to answer exploratory questions. You'll then learn to turn this processed data into informative line plots, bar plots, histograms, and more with the ggplot2 package. You’ll get a taste of the value of exploratory data analysis and the power of Tidyverse tools. This is a suitable introduction for those who have no previous experience in R and are interested in performing data analysis.

Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collection of data science tools within R.

Discover the fundamentals of the Tidyverse, and learn all about renaming and reordering variables, while becoming familiar with binomial distribution.

Introduction to the Tidyverse

Say you've found a great dataset and would like to learn more about it. How can you start to answer the questions you have about the data? You can use dplyr to answer those questions—it can also help with basic transformations of your data. You'll also learn to aggregate your data and add, remove, or change the variables. Along the way, you'll explore a dataset containing information about counties in the United States. You'll finish the course by applying these tools to the babynames dataset to explore trends of baby names in the United States.

Learn to transform and manipulate your data using dplyr. 

Learn how to use dplyr to transform and aggregate data, then add, remove, or change variables. You'll then apply your skills to a real-world case study.

Data Manipulation with dplyr

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

The Unix command line has survived and thrived for almost 50 years because it lets people do complex things with just a few keystrokes. Sometimes called "the universal glue of programming," it helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds that may be halfway around the world. This course will introduce its key elements and show you how to use them efficiently.

The Unix command line helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds. 

Discover the Unix command line and how to use it to combine existing programs, automate repetitive tasks, and run programs on clusters and clouds in Shell.

Introduction to Shell

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

Being able to write your own functions makes your analyses more readable, with fewer errors, and more reusable from project to project. Function writing will increase your productivity more than any other skill!  In this course you'll learn the basics of function writing, focusing on the arguments going into the function and the return values. You'll be writing useful data science functions, and using real-world data on Wyoming tourism, stock price/earnings ratios, and grain yields.

Take your R skills up a notch by learning to write efficient, reusable functions. 

Learn how to write efficient, reusable functions in R by understanding the basics of function writing, focusing on the arguments and the return values.

Introduction to Writing Functions in R

The beauty of R is that it is built for performing data analysis. The downside is that sometimes R can be slow, thereby obstructing our analysis. For this reason, it is essential to become familiar with the main techniques for speeding up your analysis, so you can reduce computational time and get insights as quickly as possible.

Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.

Writing Efficient R Code

Dates and times are abundant in data and essential for answering questions that  start with when, how long, or how often.  However, they can be tricky, as they come in a variety of formats and can behave in unintuitive ways. This course teaches you the essentials of parsing, manipulating, and computing with  dates and times in R. By the end, you'll have mastered the lubridate package, a member of the tidyverse, specifically designed to handle dates and times.  You'll also have applied your new skills to explore how often R versions are released,  when the weather is good in Auckland (the birthplace of R), and how long monarchs ruled in Britain.

Learn the essentials of parsing, manipulating and computing with dates and times in R.

Dates and Times in R

Working with Dates and Times in R

Character strings can turn up in all stages of a data science project. You might have to clean messy string input before analysis, extract data that is embedded in text or automatically turn numeric results into a sentence to include in a report. Perhaps the strings themselves are the data of interest, and you need to detect and match patterns within them. This course will help you master these tasks by teaching you how to pull strings apart, put them back together and use stringr to detect, extract, match and split strings using regular expressions, a powerful way to express patterns.

Learn how to pull character strings apart, put them back together and use the stringr package.

In this course you will learn how to pull character strings apart, put them back together and use the stringr package.

String Manipulation with stringr in R

Object-oriented programming (OOP) lets you specify relationships between functions and the objects that they can act on, helping you manage complexity in your code. This is an intermediate level course, providing an introduction to OOP, using the S3 and R6 systems. S3 is a great day-to-day R programming tool that simplifies some of the functions that you write. R6 is especially useful for industry-specific analyses, working with web APIs, and building GUIs.

Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.

Object-Oriented Programming with S3 and R6 in R

Most of the useful data in the world, from economic data to news content to geographic information, lives somewhere on the internet - and this course will teach you how to access it.

You'll explore how to work with APIs (computer-readable interfaces to websites), access data from Wikipedia and other sources, and build your own simple API client. For those occasions where APIs are not available, you'll find out how to use R to scrape information out of web pages. In the process you'll learn how to get data out of even the most stubborn website, and how to turn it into a format ready for further analysis. The packages you'll use and learn your way around are rvest, httr, xml2 and jsonlite, along with particular API client packages like WikipediR and pageviews.

Learn how to efficiently import data from the web into R.

Discover how to work with APIs, build your own API client, and access data from Wikipedia and other sources by using R to scrape information from web pages.

Working with Web Data in R

In this course, you will learn the end-to-end process for creating an R package from scratch.  You will start off by  creating the basic structure for your package, and adding in important details like functions and metadata. Once the basic components of your package are in place, you will learn about how to document your package, and why this is important for creating quality packages that other people - as well as your future self - can use with ease. Once you have created the components of your package, you will learn how to test they work properly, by creating tests, running checks, and building your package.  By the end of this course you can expect to have all the necessary skills to create and share your own R packages.

Learn how to create and share your own R Packages, from setting up the structure and writing functions to creating tests to run and check its functionality.

Developing R Packages

With an increasing amount of data and more complex algorithms available to scientists and practitioners today, parallel processing is almost always a must, and in fact, is expected in packages implementing time-consuming methods. This course introduces you to concepts and tools available in R for parallel computing and provides solutions to a few important non-trivial issues in parallel processing like reproducibility, generating random numbers and load balancing.

This course covers in detail the tools available in R for parallel computing.

Parallel Programming in R

Writing R scripts is easy. Writing good R code is hard. In this course, we'll discuss defensive programming - a set of standard techniques that will help reduce bugs and aid working in teams. We examine techniques for avoiding common errors and also how to handle the inevitable error that arises in our code. The course will conclude looking at when to make the transition from script to project to package.

Learn defensive programming in R to make your code more robust.

Learn how to make create robust code using defensive programming techniques in R to avoid errors when transitioning from script to project to package.

Defensive R Programming

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

Gain the career-building programming skills you need to successfully develop software, wrangle data, and perform advanced data analysis in R. No prior coding experience required.

In this track, you'll learn how to manipulate data, write efficient R code, and work with challenging data, including date and time data, text data, and web data using APIs. As you become more comfortable with these skills, you'll move on to learn about writing functions and object-oriented programming—an essential skill when working with large and complex programs. Through interactive exercises, you'll also gain experience working with powerful R libraries, including devtools, testthat, and rvest, that will help you perform key programmer tasks, such as web development, data analysis, and task automation. Start this track and embark on your journey to becoming a R programmer.

From data wrangling to developing packages, gain the career-building skills you need to succeed as an R programmer. No prior coding experience needed.

R Programmer

It is estimated that over 70% of potentially usable business information is unstructured, often in the form of text data. Text mining provides a collection of techniques that allows us to derive actionable insights from unstructured data. In this course, we explore the basics of text mining using the bag of words method. The first three chapters introduce a variety of essential topics for analyzing and visualizing text data. The final chapter allows you to apply everything you've learned in a real-world case study to extract insights from employee reviews of two major tech companies.

Learn the bag of words technique for text mining with R.

Discover the basics of text mining using the bag of words method and a variety of other essential topics for analyzing and visualizing text data in R.

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