Conditions of Use
I found the text to be complete and sufficient for an introduction to data science with python. This was remarkable as there are OER textbook for python, but few on data science "using" python. read more
I found the text to be complete and sufficient for an introduction to data science with python. This was remarkable as there are OER textbook for python, but few on data science "using" python.
I found no errors in my readings.
As I mentioned above, this was a fabulous find for me as I have embarked upon a start up data science cert (and class with python) for our college. Although there are a few publishers with this content (truly not many), this is actually the first OER text to specifically capture my content (data science & python).
The text was a clear and easy read, as it should be for an introduction to this area of study.
I found the text to be consistent and had an easy structure to follow.
The text had many sections, which will make it easy to utilize only the sections I find important to cover in my specific class. This is appreciated as modularity is so important for teachers who have unique courses that might not use all of the text book.
The topics flowed as I expected they should - building in a natural/logical way.
I found no issues with navigation or distortion in the etext. It was a good read.
I found no grammatical errors or technical errors.
I found no issues with insensitive or offensive content.
Although I am so grateful for this OER text, it was writing from the lens of a computer scientist, which most are. My course is from the lens of a non-computer scientist, more of "user" of python in the area of data science. But, again...I am thankful for this remarkable resource.
The book provides a great introduction to the world of data science, using Python as the main driver. Python is a good choice as it has become the de facto programming language used in the field with its many libraries that fit the bill. read more
The book provides a great introduction to the world of data science, using Python as the main driver. Python is a good choice as it has become the de facto programming language used in the field with its many libraries that fit the bill.
Book is accurate to current information in the field of data science.
The book is relevant since it seemingly uses Python 3. Pandas (and NumPy) dropped support for Python 2 at the start of 2020. Unless something more popular than pandas comes along, this book will be relevant for the foreseeable future. The text is organized in a way that appears to make it simple to update.
Concepts are introduced and explained as necessary. Readers with no experience should have no problem when starting at the beginning of the book.
Book uses consistent terms and doesn't appear to blindside the user with any new information or undefined terms.
This book would be useful for people who are versed in Python programming, but the chapter structure feels a bit off. Concepts related to the programming side (like functions) are found in later chapters. The author has their reasons which are explained in the book, but to an outsider it may be off putting. This book still offers a modular view where certain sections can be extracted as needed.
Book is organized well and topics are introduced as needed. Only complaint was about the ordering of the chapters, which I commented on in the "Modularity" section.
Table of Contents works as expected in the PDF version. Images appear fine and the book is free of any distracting display issues.
My only gripe is that I couldn't find a table of contents that included the subsections found within the chapters.
I saw no glaring grammatical errors while reviewing this text. I found the author's writing to be quite enjoyable at times.
I didn't see any issues with statements that could be seen as culturally insensitive.
I've enjoyed this book and plan to use it as the main or supplemental textbook in one of my future courses.
Table of Contents
- 1 Introduction
- 2 A trip to Jupyter
- 3 Three kinds of atomic data
- 4 Memory pictures
- 5 Calculations
- 6 Scales of measure
- 7 Three kinds of aggregate data
- 8 Arrays in Python (1 of 2)
- 9 Arrays in Python (2 of 2)
- 10 Interpreting Data
- 11 Assoc. arrays in Python (1 of 3)
- 12 Assoc. arrays in Python (2 of 3)
- 13 Assoc. arrays in Python (3 of 3)
- 14 Loops
- 15 EDA: univariate
- 16 Tables in Python (1 of 3)
- 17 Tables in Python (2 of 3)
- 18 Tables in Python (3 of 3)
- 19 EDA: bivariate (1 of 2)
- 20 EDA: bivariate (2 of 2)
- 21 Branching
- 22 Functions (1 of 2)
- 23 Functions (2 of 2)
- 24 Recoding and transforming
- 25 Machine Learning: concepts
- 26 Classification: concepts
- 27 Decision trees (1 of 2)
- 28 Decision trees (2 of 2)
- 29 Evaluating a classifier
About the Book
A perfect introduction to the exploding field of Data Science for the curious, first-time student. The author brings his trademark conversational tone to the important pillars of the discipline: exploratory data analysis, choices for structuring data, causality, machine learning principles, and introductory Python programming using open-source Jupyter Notebooks. This engaging read will allow any dedicated learner to build the skills necessary to contribute to the Data Science revolution, regardless of background.
About the Contributors
Stephen Davies, Associate Professor of Computer Science, earned a Ph.D. (2005) in Computer Science from the University of Colorado, Boulder, after having received an M.S. (1995) in Electrical Engineering from Colorado and a B.S. (1992) in Electrical Engineering from Rice University. He joined the UMW faculty in 2006, and has taught courses in database schema theory, Web application development, computational science, data mining, and object-oriented analysis & design, among other topics.