Cargando…
Python for data science for dummies /
The fast and easy way to learn Python programming and statisticsPython is a general-purpose programming language created in the late 1980s & mdash;and named after Monty Python & mdash;that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley and Sons, Inc.,
[2019]
|
| Edición: | 2nd edition. |
| Colección: | --For dummies.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Intro; Title Page; Copyright Page; Table of Contents; Introduction; About This Book; Foolish Assumptions; Icons Used in This Book; Beyond the Book; Where to Go from Here; Part 1 Getting Started with Data Science and Python; Chapter 1 Discovering the Match between Data Science and Python; Defining the Sexiest Job of the 21st Century; Considering the emergence of data science; Outlining the core competencies of a data scientist; Linking data science, big data, and AI; Understanding the role of programming; Creating the Data Science Pipeline; Preparing the data.
- Performing exploratory data analysisLearning from data; Visualizing; Obtaining insights and data products; Understanding Python's Role in Data Science; Considering the shifting profile of data scientists; Working with a multipurpose, simple, and efficient language; Learning to Use Python Fast; Loading data; Training a model; Viewing a result; Chapter 2 Introducing Python's Capabilities and Wonders; Why Python?; Grasping Python's Core Philosophy; Contributing to data science; Discovering present and future development goals; Working with Python; Getting a taste of the language.
- Understanding the need for indentationWorking at the command line or in the IDE; Performing Rapid Prototyping and Experimentation; Considering Speed of Execution; Visualizing Power; Using the Python Ecosystem for Data Science; Accessing scientific tools using SciPy; Performing fundamental scientific computing using NumPy; Performing data analysis using pandas; Implementing machine learning using Scikit-learn; Going for deep learning with Keras and TensorFlow; Plotting the data using matplotlib; Creating graphs with NetworkX; Parsing HTML documents using Beautiful Soup.
- Chapter 3 Setting Up Python for Data ScienceConsidering the Off-the-Shelf Cross- Platform Scientific Distributions; Getting Continuum Analytics Anaconda; Getting Enthought Canopy Express; Getting WinPython; Installing Anaconda on Windows; Installing Anaconda on Linux; Installing Anaconda on Mac OS X; Downloading the Datasets and Example Code; Using Jupyter Notebook; Defining the code repository; Understanding the datasets used in this book; Chapter 4 Working with Google Colab; Defining Google Colab; Understanding what Google Colab does; Considering the online coding difference.
- Using local runtime supportGetting a Google Account; Creating the account; Signing in; Working with Notebooks; Creating a new notebook; Opening existing notebooks; Saving notebooks; Downloading notebooks; Performing Common Tasks; Creating code cells; Creating text cells; Creating special cells; Editing cells; Moving cells; Using Hardware Acceleration; Executing the Code; Viewing Your Notebook; Displaying the table of contents; Getting notebook information; Checking code execution; Sharing Your Notebook; Getting Help; Part 2 Getting Your Hands Dirty with Data; Chapter 5 Understanding the Tools.