|As all cookbooks, it won't make you a field expert by reading it, but it will surely carve an image of what Python can do regarding data visualization, and that's the most important thing, based on my personal experience. Learning what a tool can do means that whenever you may need it, the information is only a click or a page away. I'd recommend it for both professionals that already work in the subject, and beginner-intermediate curiosity-driven enthusiasts as myself.|
Having just started my career as a web developer two years ago, my weapon of choice was PHP. Naturally, not being a mature enough developer to fully understand the power of PHP, I quickly found caveats, even though the community was great, and the language and tools that came with it were oh so simple to understand and use. I quickly started to look into Python about a year ago, by starting to use the Django framework. I was quickly hooked by all that was Python, and found myself using it more and more, both casually and professionally.
Wanting to get better at Python, I looked around for a good book for me to read. I had gathered so much information about Python, but it had no structure, no fundamentals inside my head. I found Tarek Ziadé's "Expert Python Programming" (http://www.packtpub.com/expert-python-programming/book) from Packt Publishing and my mind was blown. There was so much of the professional way of doing things with Python that I didn't even start to think about, or thought about it but was scared that it's not the time yet, I would not even be able to comprehend it. I was wrong. I still use it as a reference in my everyday job, and kindly, fullheartedly recommend it to anyone.
Having my mind set out to explore big data and the more statistical part of the web, I then searched for another book that could teach me something regarding the new direction I wanted to head to. Being satisfied by the customer support and general website UI/UX, I naturally began my search on Packt's web application. I found Igor Milovanović's Python Data Visualization Cookbook, (http://www.packtpub.com/python-data-visualization-cookbook/book), and skeptically downloaded it. I say 'skeptically', because cookbooks are not usually my go-to option when it comes to expanding my programming knowledge in general -- I find them too short and technical, with few to null of those much needed explanations that I require in order to understand the subject I'm interested in.
I was, again, surprised. Not only the book has thorough explanations of the recipes, but it also comes with great tips and tricks for Python programming in general. The author made sure you first and foremost grasp the concept of data visualization, the need for it, and application in real life scenarios, before starting the teaching process.
I discovered a whole array of tools that I wasn't aware of, that can be used not only for data visualization, but for my every day Python programming. After setting up my environment, by installing the somewhat dependence heavy libraries needed for going through the cookbook, I started to get frightened by my lack of knowledge in the field of big data manipulation in general. With math not being my forte, and no formal training in statistics, I surprisingly was not caught off-guard by the recipes. There are not advanced math requirements or statistical foundation required in order to understand the cookbook, and the author makes the recipes relatively easy to understand. When we encounter strange or unfamiliar terms, we get explanations or links towards wikipedia or web-standard pages that explain the terminology.
With a broad spectrum of real-life applications, and sharing crucial data manipulation techniques, like noise reduction or handling various data formats, the author makes sure you're prepared for most of the scenarios you might bump into, be you a researcher, software developer (CRM's, ERP's, products that usually require data visualization) or active in academia. But it doesn't stop here. By expanding the already thick information layer, the book takes us, with baby steps, through key concepts in 3D data visualization, OpenGL, working with the Google Maps API for plotting statistical information based on geographical location, but also addresses more discrete subjects as choosing the right colour for your presentation depending on your target demographic, or understanding what data visualization technique can best supply the audience with valuable info, regarding the nature of the information you want to emphasize.
As with all cookbooks, it won't make you a field expert by reading it, but it will surely carve an image of what Python can do regarding data visualization, and that's the most important thing, based on my personal experience. Learning what a tool can do means that whenever you may need it, the information is only a click or a page away. I'd recommend it for both professionals that already work in the subject, and beginner-intermediate curiosity-driven enthusiasts as myself.
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