Practical data science with Python : learn tools and techniques from hands-on examples to extract insights from data /

The book provides a one-stop solution for getting into data science with Python and teaches how to extract insights from data.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: George, Nathan (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, 2021.
Temas:
Database management.
Big data.
Python (Computer program language)
Bases de données > Gestion.
Données volumineuses.
Python (Langage de programmation)
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Cover
  • CopyRight
  • Contributors
  • Table of Contents
  • Preface
  • An Introduction and the Basics
  • Chapter 1: Introduction to Data Science
  • The data science origin story
  • The top data science tools and skills
  • Python
  • Other programming languages
  • GUIs and platforms
  • Cloud tools
  • Statistical methods and math
  • Collecting, organizing, and preparing data
  • Software development
  • Business understanding and communication
  • Specializations in and around data science
  • Machine learning
  • Business intelligence
  • Deep learning
  • Data engineering
  • Big data
  • Statistical methods
  • Natural Language Processing (NLP)
  • Artificial Intelligence (AI)
  • Choosing how to specialize
  • Data science project methodologies
  • Using data science in other fields
  • CRISP-DM
  • TDSP
  • Further reading on data science project management strategies
  • Other tools
  • Test your knowledge
  • Summary
  • Chapter 2: Getting Started with Python
  • Installing Python with Anaconda and getting started
  • Installing Anaconda
  • Running Python code
  • The Python shell
  • The IPython Shell
  • Jupyter
  • Why the command line?
  • Command line basics
  • Installing and using a code text editor
  • VS Code
  • Editing Python code with VS Code
  • Running a Python file
  • Installing Python packages and creating virtual environments
  • Python basics
  • Numbers
  • Strings
  • Variables
  • Lists, tuples, sets, and dictionaries
  • Lists
  • Tuples
  • Sets
  • Dictionaries
  • Loops and comprehensions
  • Booleans and conditionals
  • Packages and modules
  • Functions
  • Classes
  • Multithreading and multiprocessing
  • Software engineering best practices
  • Debugging errors and utilizing documentation
  • Debugging
  • Documentation
  • Version control with Git
  • Code style
  • Productivity tips
  • Test your knowledge
  • Summary
  • Dealing with Data
  • Chapter 3: SQL and Built-in File Handling Modules in Python
  • Introduction
  • Loading, reading, and writing files with base Python
  • Opening a file and reading its contents
  • Using the built-in JSON module
  • Saving credentials or data in a Python file
  • Saving Python objects with pickle
  • Using SQLite and SQL
  • Creating a SQLite database and storing data
  • Using the SQLAlchemy package in Python
  • Test your knowledge
  • Summary
  • Chapter 4: Loading and Wrangling Data with Pandas and NumPy
  • Data wrangling and analyzing iTunes data
  • Loading and saving data with Pandas
  • Understanding the DataFrame structure and combining/concatenating multiple DataFrames
  • Exploratory Data Analysis (EDA) and basic data cleaning with Pandas
  • Examining the top and bottom of the data
  • Examining the data's dimensions, datatypes, and missing values
  • Investigating statistical properties of the data
  • Plotting with DataFrames
  • Cleaning data
  • Filtering DataFrames
  • Removing irrelevant data
  • Dealing with missing values
  • Dealing with outliers
  • Dealing with duplicate values

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