This book is designed for intermediate learners with a basic understanding of Python. Python programming is broken down here into easily digestible lessons of less than 5 minutes each, following a tried and tested BiteSize approach.
This book is an introduction to Python for intermediate learners. It allows readers to take a slow and steady approach to building upon their understanding of Python code. While there are many books, websites, and online courses about the topic, Python programming is broken down here into easily digestible lessons of less than 5 minutes each, following a tried and tested BiteSize approach.
Each lesson begins with a clear and concise introduction to the topic, giving the reader a strong base to start from and gets them ready for deeper learning. This is followed by coding demonstrations that further explore the ideas discussed. The book offers practice tasks in different difficulty levels, so readers can test their knowledge and grow their confidence. The reader will also play with case studies to solve real-world problems. Tips on how to incorporate Generative AI into a learning toolkit are provided, for purposes like feedback, practice exercises, code reviews, and exploring advanced topics. Recommended AI prompts can help readers identify areas for improvement, review key concepts, and track progress.
This book is designed for intermediate learners with a basic understanding of Python. It is ideal for individuals with busy schedules or limited time for studying.
Section I: Object-Oriented Programming
1. Introduction to OOP
2.
Inheritance
3. Polymorphism
4. Encapsulation
5. Abstraction
6. Documentation
7. Case Studies Section II. Data Manipulation
8. N-dimensional Arrays
9.
NumPy
10. Series
11. DataFrame
12. Pandas Section III. Data Visualization
13.
Matplotlib (Basic)
14. Matplotlib (Advanced)
15. Seaborn
16. Plotly. What is
Next? Index
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.
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