Unlock Python
A Python crash course to prepare you for working with data. Don't learn Python with a generic book. Learn it with the goal of approaching data.
Over 300 Pages:
Why Python? Understand the reasons behind Python's soaring popularity and why it's the preferred choice for many developers and data scientists.
Step-by-Step Python Installation: Never worked with Python before? No problem! Our guide takes you through the installation process, ensuring you have the right version and tools, such as Anaconda, to kick-start your Python journey.
Interactive Learning with Jupyter: Discover the world of Jupyter Notebooks and Labs, and leverage its powerful features for interactive coding, data analysis, and visualization.
Deep Dive into Python Basics: From variables, objects, numbers, strings, to data structures like lists, tuples, and dictionaries – master the fundamentals with clear explanations and real-world examples.
Data Science with NumPy and Pandas: Unveil the power of Python libraries like NumPy for numerical operations and Pandas for data manipulation. Work on practical exercises that simulate real-world problems.
Advanced Topics Explained: Delve into the world of classes, subclasses, data classes, exceptions, and more. Learn how Python is reshaping data science with advanced libraries like XGBoost for regression analysis.
Practical Tools & Techniques: Explore the utility of tools like dir, help, and pdb. Understand Python's interaction with Unicode, file handling capabilities, and the principles behind importing libraries and modules.
Python in Data Science: Integrate Python with data science tools like Scikit-learn, and explore how Python's OOP features can be used for advanced data analysis and modeling.
Hands-On Exercises: After each section, test your knowledge with carefully crafted exercises that challenge your understanding and help solidify your learning.
You have a unique way of explaining concepts, and you push readers forward to think with your samples and exercises. All these make this book even more valuable. I will recommend this book to all my beginner friends
You cover a tremendous amount of material in a relatively short number of pages.
I would definitely recommend it for someone who needs to get up to speed relatively quickly, and your Pandas book would be a natural second step.
My first impression is that after reading through the book a couple of times, I am convinced that the need for a book of this quality is long overdue. I am impressed by the details of the Python objects, the addresses, and the sequence of presentations.
This book is not just for fresh learners but for practitioners too. It contains useful tips, detailed explanations and cover from the basics to model development using python.
When someone asks me which book I would recommend to start learning Python, I would suggest @__mharrison__ book. I've made a lot of mistakes and read some not-so-good books, but I support all of Matt's books.
I've found this book incredibly valuable and I especially enjoy the very specific details on why python works in certain ways and mentions of the historical reasons/evolution. Also, I love that you go into the greatest detail for each topic. And your teaching is in this great step-by-step manner that somebody who doesn't like to "read the instruction manual" would not appreciate, but for the kind of technologist who revels in knowing every nook and cranny of a command or tool and why it works the way it does--well, it's a godsend. I find the tidbits of information you share are the kind of gems I generally only find after years of using a tool and then coming across it some course for beginners, as I often will take courses on topics I have already "so-called mastered" to gain such insights and to visualize and imagine how I would teach it and share topics with others. Sometimes, mastering a tool or technology does not equate to being able to teach others about it, but I believe true mastery is when you can both use the tool and teach the greatest novice how and why it works and build an enthusiastic interest in using it and learning more.
It's a great book, and I hope many more get the opportunity to enjoy it!
The Learning Python for Data by 🐍 Matt Harrison is a great book for starting with Python for data applications. The book focuses both on the foundation of Python and a variety of data-related topics.
As someone who started with R and uses both languages, I often use this book to get a clear understanding of Python core concepts such as OOP and methods.
Python is a versatile and widely used programming language, especially in the realm of data analysis and data science. With its extensive libraries and intuitive syntax, it makes data processing, analysis, and visualization accessible and efficient.
Technical folks who are interested in learning Python and considering using it for data science or data engineering. Graduates who took a programming class and want to refresh and understand Python.
Unlike many books that go through all of the features of Python (regardless of whether you will ever use them), this book focuses on the fundamental constructs you will need. It also introduces 3rd party libraries like NumPy and Pandas.
I've used Python for over 20 years. My first job was doing NLP before the term "Data Science" existed. I've taught thousands of students around the world and have seen what tricks them and also know what they will need to know to be successful in industry.
You might find that this book moves too fast if you are a complete beginner and have never programmed before. Having said that, I have had motivated students who were successful in the past.
I'm a very practical person. However, lots of folks are missing fundamental theory about Python. (I would say most coming out of college have pretty huge knowledge gaps.) I teach you the theory and then show you the practical bits.
Those are books that show how to use Python libraries. This book will prep you for those books. The book does contain examples of using both Pandas and XGBoost, but it doesn't go deep into the libraries. It shows them in the context of the language features you are learning.