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May 25, Carsten rated it really liked it I needed a quick Python refresher as I want to use Python for machine learning projects. I had used Numpy and SciPy in the past, but havent used the language actively for a few years. Ive read the first 7 chapters of the book and did some of the exercises.
I might read chapters 8, 10 and 12 at some point. I can say that the book is well written and occasionally very funny. Absolute beginners should probably look elsewhere but if you have some programming experience, you cant go wrong with this I needed a quick Python refresher as I want to use Python for machine learning projects. My interest in Python stems primarily from my goal to work with data in order to engage on challenges, business and social, that are data focused.
I created a complitation of topics that I felt I needed to address the gap in knowledge, skill set, and work experience; around this I created a syllabus taking in advice from Im new to Python and to programming.
I created a complitation of topics that I felt I needed to address the gap in knowledge, skill set, and work experience; around this I created a syllabus taking in advice from current Data Scientists, professors in the program I am in, and on LinkedIn as well as from reading a lot of blogs and forums. Python was by far the oft repeated language to learn and Lubanovica book a must for self learning.
I came at Python with a singular objective: data science. Lubanovic writes with a clear purpose as well, one that is much broader and wider than my singular focus. To this end this book is amazing. I see myself using it as a reference book to fall back to in order to gain clarity. However, as in introduction its supposing that the one learning it can piece together lessons toward an end deliverable of some sort.
Here, i am incredibly lacking in both ingenuity and clarity. Give me a topic an I can research the crap out of it and present to a classroom, speak to policy makers, create a strategy. Tell me to value a company and I can research and put together financial models to tell you whether to buy, sell or hold and then walk you through the logic.
But if you ask me to provide a program in python, I wouldnt know where to begin and I wouldnt know what other books to read besides Lubanovics. I would be at a complete loss and thats after working through this book. I speculate thats its not for a lack of Lubanovics writing or presentation of Python, but rather my own incomplete understanding of what to do with this information.
I sense that this is my learning style- i need a project, an application for what this book goes over. Maybe a book thats a larger project broken down into parts that allows me to reference this books materials to come up with my own code then see it against the authors solutions. Maybe thats why students go to get BS Comp Sci degrees? I hope this helps. So maybe my opinion will change and become clearer as i go through that.
For example, it recommends IPython, even though that has turned into Jupyter. Also some of the function calls in the appendices were out-of-date. That said, it is a good introduction to Python, covering all the information a good textbook should. If you need to get up to speed on Python, this is the book for you. However, you will still need to read other books on NumPy, Pandas and other packages to do your data science This being , it is a little out of date with some of its suggestions.
However, you will still need to read other books on NumPy, Pandas and other packages to do your data science and machine learning. Some of the content is outdated targeting python 3. This probably serves as an approachable introduction after which reading the official Python documentation is useful. Nov 16, Matthew rated it really liked it I really have no idea why the topics are laid out in the order they are.
However when you find the section you need, the book is brilliant. But, in the end, it never let me down and I found the teaching style and explanations to be top notch. I am very glad I went with this book!
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