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Python Data Science Handbook
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CC BY-NC-ND
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For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.

Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.

With this handbook, you’ll learn how to use:

IPython and Jupyter: provide computational environments for data scientists using Python
NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python
Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python
Matplotlib: includes capabilities for a flexible range of data visualizations in Python
Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Subject:
Applied Science
Computer Science
Material Type:
Textbook
Author:
Jake Vanderplas
Date Added:
02/09/2024
Python for Everybody: Exploring Data In Python 3
Unrestricted Use
CC BY
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New Edition! The goal of this book is to provide an Informatics-oriented introduction to programming. The primary difference between a computer science approach and the Informatics approach taken in this book is a greater focus on using Python to solve data analysis problems common in the world of Informatics.

Subject:
Applied Science
Computer Science
Material Type:
Textbook
Author:
Charles Severance
Date Added:
12/05/2019
Think Complexity
Unrestricted Use
CC BY
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This book is about complexity science, data structures and algorithms, intermediate programming in Python, and the philosophy of science. This book focuses on discrete models, which include graphs, cellular automata, and agent-based models. They are often characterized by structure, rules and transitions rather than by equations. They tend to be more abstract than continuous models; in some cases there is no direct correspondence between the model and a physical system.

Subject:
Applied Science
Computer Science
Information Science
Material Type:
Textbook
Provider:
Green Tea Press
Author:
Allen B. Downey
Date Added:
01/01/2012
Think DSP: Digital Signal Processing in Python
Conditional Remix & Share Permitted
CC BY-NC
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The examples and supporting code for this book are in Python. You should know core Python and you should be familiar with object-oriented features, at least using objects if not defining your own. If you are not already familiar with Python, you might want to start with my other book, Think Python, which is an introduction to Python for people who have never programmed, or Mark Lutz’s Learning Python, which might be better for people with programming experience.

Subject:
Applied Science
Computer Science
Material Type:
Textbook
Provider:
Green Tea Press
Author:
Allen B. Downey
Date Added:
01/01/2012
Think Python 2nd Edition
Conditional Remix & Share Permitted
CC BY-NC-SA
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The goal of this book is to teach you to think like a computer scientist. This way of thinking combines some of the best features of mathematics, engineering, and natural science. Like mathematicians, computer scientists use formal languages to denote ideas (specifically computations). Like engineers, they design things, assembling components into systems and evaluating tradeoffs among alternatives. Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions.

Subject:
Applied Science
Computer Science
Material Type:
Primary Source
Textbook
Provider:
Green Tea Press
Author:
Allen B. Downey
Date Added:
12/05/2019
Think Stats: Probability and Statistics for Programmers
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CC BY-NC
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Think Stats is an introduction to Probability and Statistics for Python programmers.

*Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets.
*If you have basic skills in Python, you can use them to learn concepts in probability and statistics. Think Stats is based on a Python library for probability distributions (PMFs and CDFs). Many of the exercises use short programs to run experiments and help readers develop understanding.

Subject:
Applied Science
Computer Science
Mathematics
Statistics and Probability
Material Type:
Textbook
Provider:
Green Tea Press
Author:
Allen Downey
Date Added:
01/01/2014