This book allows readers to take a slow and steady approach to understanding Python code, explaining concepts, connecting programming with real-life examples, writing Python programs, and completing case studies. For absolute beginners with no prior programming experience, and individuals with busy schedules or limited time for studying.
As an introduction to Python, this book allows readers to take a slow and steady approach to understanding Python code, explaining concepts, connecting programming with real-life examples, writing Python programs, and completing case studies.
While there are many books, websites, and online courses about the topic, we break down Python programming into easily digestible lessons of less than 5 minutes each, following our BiteSize approach. Each lesson begins with a clear and short introduction to the topic. This gives you a strong base to start from and gets you ready for deeper learning. Then, you will see coding demonstrations that show the ideas discussed. These examples are simple and useful, helping you really understand the concepts. You’ll then practice tasks at different difficulty levels, so you can test your knowledge and increase your confidence. You’ll also play with case studies to solve real-world problems. Tips are included to show how you can incorporate generative AI into your learning toolkit, using it for feedback, practice exercises, code reviews, and exploring advanced topics. Recommended AI prompts can help you identify areas for improvement, review key concepts, and track your progress.
This book is designed for absolute beginners with no prior programming experience. It is ideal for individuals with busy schedules or limited time for studying.
Part 1: Python Fundamentals
1. Introduction to Python
2. Input and
Output
3. Variables
4. Operations
5. String
6. Case Studies of Python
Fundamentals Part 2: Flow control and Functions
7. Branching
8. Repetition
9.
Functions
10. Advanced Functions Part 3: Data Structures
11. List
12. Tuple
13. Set 14.Dictionary
15. Case studies of data structures Part 4: Data
Collections
16. Named Tuple
17. Default Dictionary
18. Counters
Dr. Di Wu is an Assistant Professor of Finance, Information Systems, and Economics department of Business School, Lehman College. He obtained a Ph.D. in Computer Science from the Graduate Center, CUNY. Dr. Wus research interests are 1) Temporal extensions to RDF and semantic web, 2) Applied Data Science, and 3) Experiential Learning and Pedagogy in business education. Dr.Wu developed and taught courses including Strategic Management, Databases, Business Statistics, Management Decision Making, Programming Languages (C++, Java, and Python), Data Structures and Algorithms, Data Mining, Big Data, and Machine Learning.
