دانلود کتاب تحلیلگر داده های خوب

When I graduated from data education and started working as a data scientist, I was shocked at how different the job was from what I expected based on my studies. Data was harder to come by than I imagined. There weren’t clean datasets just sitting around waiting for me to analyze them. When I did get my hands on some data, it was undocumented and full of problems. I soon found out I wasn’t the only one who had this experience, so when I started teaching data science alongside my day job, I wanted to bridge this gap between the classroom and the real world. I’ve been teaching data topics for a number of years now, and the single most frequently asked question I get after a course is, what should I learn next? Based on my own experiences, I usually give a standard answer: solve real problems and learn by doing. I’ve given this answer so many times now that I wanted to write it down somewhere. This book is my extended answer. To improve as an analyst, you need two things: get better at the process of analyzing data, regardless of what tools you use, and be immersed in a business environment where your work directly affects your surroundings. The market is saturated with introductory material. “Introduction to data science/analysis” books and courses are everywhere. What has always struck me was the lack of follow-up resources. What about intermediate or advanced data science? Everything that’s out there is exclusively about tools and algorithms. That’s great, but there is more to data science than the technical details. In fact, the technical details change, whereas the job of an analyst fundamentally does not. Analysts need to be problem solvers. People have questions that can be answered with data; analysts answer them and, in doing so, have to solve technical and organizational problems. At a high level, problem solving is the skill that analysts need to hone most after initial training. The best way to do that is to actually solve problems. But what problems? And with what projects? With this book, I want aspiring analysts to continue learning, while creating a portfolio of projects to show off their advanced skills. When choosing the projects, I was careful to focus on topics that don’t normally make it into an introductory syllabus but come up often in the real world. I also chose real-world datasets and made little or no modifications so the problems would be as true to life as possible. I hope you enjoy solving these problems as much as I enjoyed creating them.