Introduction to Data Science Using Python
Afrand Agah, West Chester University
Copyright Year:
Publisher: The Pennsylvania Alliance for Design of Open Textbooks (PA-ADOPT)
Language: English
Formats Available
Conditions of Use
Attribution-NonCommercial
CC BY-NC
Reviews
Reviewed by Kim Mandery, Computer Scientist in Residence, St. Olaf College on 4/2/26
This book is written more akin to personal notes rather than a complete textbook. I would recommend this to someone wanting additional practice problems, or short tutorials on how to use some building blocks of the Python programming language.... read more
Reviewed by Kim Mandery, Computer Scientist in Residence, St. Olaf College on 4/2/26
Comprehensiveness
This book is written more akin to personal notes rather than a complete textbook. I would recommend this to someone wanting additional practice problems, or short tutorials on how to use some building blocks of the Python programming language. Dictionaries are not mentioned, but used later on in the graph theory section. No glossary of terms are included, and the depth of the content is very shallow (more showing rather than explaining how it works).
Content Accuracy
No glaring issues in the code presented.
Relevance/Longevity
While able to add in new content easily, this is moreso because the text is quite short and shallow.
Clarity
While currently written in accessible prose, additional narrative is needed to explain the "why" behind the concepts and code given.
Consistency
No issues with terminology used.
Modularity
There are a couple of sections that rely on earlier material (i.e. the section on lists has a few examples using content from files section).
Organization/Structure/Flow
The ordering of concepts makes sense. Personally I would prefer lists after strings (both are sequences) but before files. Content provided (while narrow) does flow well.
Interface
No navigation issues. I appreciate that the practice questions and solutions for each section are linked together as this makes navigating through the pdf easier for learners.
Grammatical Errors
No issues with grammar.
Cultural Relevance
Not insensitive, but include no examples of real-world application.
Table of Contents
- Preface
- Installing Python
- Introduction to Programming
- Decision Structures
- Repetitions
- Functions
- Recursion
- File Access
- Lists
- Arrays
- Plotting Graphs
- Object Oriented Programming
- Using Python Packages
- Python and Graph Theory
- Python and Machine Learning
- Python and Statistics
- References
- Appendix
About the Book
This book contains two parts, the first is designed to be used in an introductory programming course for students looking to learn Python, without having any prior experience with programming. Basic programming concepts are discussed, explained, and illustrated with a Python program. Ample programming questions are provided for practice. The second part of the book utilizes machine-learning concepts and statistics to accomplish data-driven resolutions. Python programs are provided to apply scientific computing to conclude statistically driven results.
About the Contributors
Author
Afrand Agah, West Chester University