Hi. Great to have you back. Earlier, you learned that programming means giving instructions to a computer to perform an action or set of actions. Even if this is your first time programming, you already have plenty of experience telling a computer what to do. For example, you've probably used a spreadsheet function to sort your data or perform calculations, or you might have used SQL to tell a computer how to pull data from a database or join two different data tables. Programming goes even further. It gives you the highest level of control over your data. SQL can communicate with databases, but a general-purpose programming language lets you create your own applications and build your own functions from scratch. To program, you first need to know a programming language. In this video, we'll learn about the basics of programming languages and how they can help you work with your data. Programming languages are the words and symbols we use to write instructions for computers to follow. You can think of a programming language as a bridge that connects humans and computers, and allows them to communicate. Programming languages have their own set of rules for how these words and symbols should be used, called syntax. Syntax shows you how to arrange the words and symbols you enter so they make sense to a computer. Coding is writing instructions to the computer in the syntax of a specific programming language. Just like the variety of human languages around the world, there's lots of different programming languages available to communicate with computers. There's a language for almost anything you want to do, from designing websites, to developing video games, to working with data. For example, Python is a general-purpose language that can be used for all sorts of things, from working with artificial intelligence to creating virtual reality experiences. Javascript works well for developing online apps and is an essential part of web browsers. Some other popular programming languages for data analysis include SAS, Scala, and Julia. Personally speaking, R is my favorite language for data analysis, but you might want to explore other languages as well. While programming languages can look different on the surface, they all share similar structures and coding concepts. Once you learn your first language, you'll find it easier to learn others. Coming up, we'll explore R's many capabilities. Before that, let's talk about some benefits of using any programming language to work with your data. I'll highlight three. Programming helps you clarify the steps of your analysis, saves time, and lets you easily reproduce and share your work. Let's start with clarity. Programming languages have specific rules and guidelines for giving instructions to the computer. When you're telling a computer what to do, your instructions have to be very clear. There can't be any inconsistency in the way you write code. If there is, the code won't work. Translating your thoughts into code forces you to figure out exactly how to write each step of your analysis and how all the steps fit together. It gives your analysis a level of precision that makes it really powerful. Using a programming language for data analysis also saves you lots of time. For example, take the process of cleaning and transforming your data. With one line of code, you can create a separate dataset without any missing values. With another line, you can apply multiple filters on your data. This lets you spend less time preparing your data and more time on the analysis itself. Finally, programming languages make it easy to reproduce your analysis. Data analysis is most useful when you can reproduce your work and share it with other people. They can double-check it and help you solve problems. Code automatically stores all of the steps of your analysis so you can reproduce, and share your work at anytime in the future, weeks, months, or even years later. Here's an example. Let's say you're working on a project. You've collected and cleaned your data and started your analysis, but the results don't add up. You suspect a mistake was made in the process. You'd like to discuss the issue with a teammate and get their feedback. If you used a spreadsheet, you both might have to redo the entire analysis to discover the error. There's no easy way to record and reproduce your steps in a spreadsheet, but if you use a programming language, all your work can be reproduced and shared in a moment, from loading the data, to creating visualizations, to reporting the results. Plus, you can easily update your analysis and fix any errors simply by changing the code. I hope that gives you a better understanding of what programming languages are all about. Next up, we'll check out R in more detail. See you soon.
