Welcome to Python for Data Analytics, the third course in the data analytics specialization from DeepLander AI. You've learned about hypothesis testing, statistical analysis, graphing, simulations, and you've done all the neat things in spreadsheets. In this course, you'll learn how to do them in Python, the most popular programming language worldwide. Among global data professionals surveyed by the platform called Kaggle, over 88% use Python on a regular basis, which is the highest of any programming language. You'll find that Python augments your skills as a data analyst and allows you to automate repetitive tasks and handle larger data sets, tasks you're familiar with, like filtering data, or grouping, or summarization, or plotting. All of these have direct parallels in Python, but with even more power and even more flexibility than what you can do in a spreadsheet. I'm thrilled to welcome back Sean Barnes, who will be your instructor again for this course. Thank you, Andrew. It's great to be here. For analytics work, Python takes you from a rigorous but smaller scale analysis, like analyzing 10,000 customer reviews, to automatically processing millions of them. At Netflix, we use Python for so many applications, from informing which shows to greenlight, to serving up video content for more than 250 million users. Python helps us capture insights at this massive scale versus limiting our insights to just a sample of members. This course is designed to teach you the fundamentals of Python programming, from potentially your first lines of code to descriptive statistics, visualization, inferential statistics, and time series analysis. You already have the core analytical skills to solve complex problems. This course focuses on efficiency, repeatability, and applying those skills to larger and larger data sets. So I'm excited for you to take this course and develop your skills to the next level. So people have often asked me if it's still worth learning to code since the rise of large language models like ChaiGP that can sometimes write code. To me, the answer is very clearly yes. Programming is a skill that has become both easier to learn and more useful because of the rise of large language models. It's easy to learn because now you can have an AI assistant that can answer questions for you about your code. They give you feedback. And it's also become more useful than ever before because for roles like being a data analyst, you can now write code faster with the help of your AI assistants. Previously, if a programmer made a mistake, they might spend half an hour to do a lot of web searches to find a solution. But now an AI assistant can help you fix errors faster than even very good programmers maybe could do previously. In fact, Sean, have you been seeing friends in your circles using chatbots to help with their coding as well? Yes, definitely. This is a big trend I'm seeing across data analytics. You'll often hear people talk about the efficiency gains that chatbots have added, not replacing their skills, but making them faster at the jobs they already do. For example, our operations managers use AI to help write code to access data. Throughout the video demos and labs, you'll use a chatbot as a coding buddy, following best practices currently used by data analysts for code advice, error finding, and even generating code. Rather than teaching you to memorize commands, this course develops your code sense, your pattern recognition abilities, and intuitive understanding of how programs work. So, Andrew, what advice would you give someone learning to code now with all the resources that they have available to them? One of the reasons I code a lot is because, frankly, I think it's just really fun. And so if you can find the fun in coding, that may help you to keep going and encourage you when you're learning the code to always try to code yourself. In this course, we provide the notebooks together with the videos, so maybe you can open up the Jupyter Notebook if you're on a computer in a different web browser window and follow along while Sean is walking through the codes. Or you can also run the code yourself afterward, after you've seen Sean do it the first time. I'd encourage you to also test your own understanding of how the code works, because it's often by that playing around, messing around, changing the code, that you hone your intuitions about what the code will do. It's also okay if something doesn't work the first time. When I code, my code has errors or bugs all the time. Pretty much never works the first time, frankly. And so errors are expected. And when you find an error, you can try to fix it yourself or ask your AI chatbot buddy. If you have one point to take away from this, I would say, get your hands dirty. Go do it yourself. I wrote my first real piece of software to simulate the paths of electropowered satellites in space. I think I've made hundreds of mistakes before it finally worked properly. So Andrew, I can totally relate to that process of trial and error. This course focuses on practical application rather than the deep inner workings of the code. You'll learn how to navigate this trial and error process using a chatbot, which you'll see can give you a lot of support. Your goal is to get to the insights, not to write the perfect code. You'll use real-world data sets throughout the video demos and labs, exploring public library revenue, loan profitability, coral reef sustainability, stock prices, and more. Each module takes you through a case study of a data analytics project you may find yourself working on. By the end, you'll be comfortable with both Python coding for analytics and using a chatbot as a coding buddy. You know, I had no idea your first program was satellites. That's very cool. I think my first program when I was a kid was to have a computer think of a number. And then I, as a human, would have to do binary search to try to guess the number the computer was thinking of. I thought it was brilliant. So I think there'll be a lot of value here for a new data analyst. If you already know Python, that's great. You'll be able to go through some of these materials more quickly than go well beyond that. Or if you're learning Python for the first time, that's great too. You gain a really valuable set of skills. So with that, let's go on to the next video and get started.