The textbook is written by those who are well equipped with the IT industry and have practical exposure to building software and web applications. Thus, The book is intuitive and innovative in its approach and will push students to solve real-life problems using technology.
Also, Every chapter in the book consists of brainstorming exercises and exciting coding challenges. It allows students to apply their learning in a practical environment and understand how the application is built. It is recommended to practice coding on a machine as much as possible, rather than mugging up concepts. Also, It is advised to research the topics in-depth rather than getting a high-level understanding.
Unit 5 — Communication Technologies Networking, Internet services etc. A comprehensive understanding of data structures, algorithms, and their implementation in languages like Python is a prerequisite for developing software used in real life. And being aware of databases and SQL concepts will help you efficiently manage extensive information in limited storage. If you seek to pursue a career in software engineering, this textbook lays out the foundation for you. The book explains complex concepts in layman language so that they are easier to comprehend and apply.
This textbook contains practical lab exercises after each topic to implement on a machine and gain a good coding experience. The Python language is explained with well-defined logical programs, with proper documentation and code quality.
Python is a popular language today and used in Artificial Intelligence, Sentimental analysis, Self-driving cars, robotics, healthcare, and much more. It opens up a vast scope of career options in the future like Data science, Web development, Software engineer, etc.
The CBSE class 12 computer science textbook download pdf covers computer systems, operating systems, and mobile systems. Also, It introduces a high-level programming language like Python covering basic syntax, conditional statements, iterative loops, and different data structures like Dictionary, Lists, and Tuples. The code examples put together a lot of Watson services in a really nifty example. I enjoyed the OOP chapter—doctest unit testing is nice because you can have the test in the actual docstring so things are traveling together.
The line-by-line explanations of the static and dynamic visualizations of the die rolling are just great. Excellent section on problem decomposition. Thanks for pointing out seeding the random number generator for reproducibility.
I like the use of dictionary and set comprehensions for succinct programming. Good defensive programming. The section on data wrangling is excellent. Natural Language Processing is an excellent chapter! I learned a tremendous amount going through it. Great exercises. I really liked the Data Mining Twitter chapter; it focused on a real data source, and brought in a lot of techniques for analysis e.
I like that the Python modules helped hide some of the complexity. Word clouds look cool. The right level for IT students. The examples are definitely a high point to this text. I love the quantity and quality of exercises. Avoiding heavy mathematics fits an IT program well.
Irene Bruno, George Mason University. I like its focus on data science and a general purpose language for writing useful data science programs. The data science portion distinguishes this book from most other introductory Python books.
Harvey Siy, University of Nebraska at Omaha. I liked the Deep Learning chapter, which left me amazed with the things that have already been achieved in this field. Many of the projects are really interesting.
A meaningful overview of deep learning concepts, using Keras. I like the streaming example. I never made word clouds with shapes before, but I can see this being a motivating example for people getting started with NLP. They are really practical. I really enjoyed working through all the Big Data examples, especially the IoT ones. The Twitter examples covering trending topics, creating word clouds, and mapping the location of users are instructive and engaging.
I like the real-world examples of data munging. Reviewing this book was enjoyable and even though I was fairly familiar with Python, I ended up learning a lot. Architecture of the Book. Buy the Book Amazon. Library focused : Use Python and data science libraries to accomplish significant tasks with minimal code. Rich coverage of fundamentals : Problem solving, algorithm development, control statements, functions.
Procedural, functional-style and object-oriented programming. Intro to Data Science sections : Basic stats, simulation, animation, random variables, data wrangling, regression. Privacy, security, ethics, reproducibility, transparency.
Frequently Asked Questions. Before that, you can buy the paperback book also. This book covers the Board suggested syllabus. The course of study is completely covered in this book. Once you use this textbook, you will not need to go through any tuition. Following the guidebook alone will help you score high in your entrance.
The book follows the syllabus thoroughly, covering-. The book is highly useful as it makes the subject very easy for the student to understand. The features of these books are-.
Hundreds of exercises, examples, projects EEPs , and implementation case studies give students a challenging, engaging, and entertaining introduction to Python programming and practical data science.
Designed to aid students not only learn programming fundamentals but also influence a large number of existing libraries to start accomplishing jobs with minimal code.
Concepts are complemented by rich Python examples that students can adapt to apply their own solutions to data science problems. I like that cloud services are used.
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