Practical data science with Python : learn tools and techniques from hands-on examples to extract insights from data / Nathan George
Material type:
TextPublication details: Birmingham, Packt Publishing, c2021Description: xxiii, 595 pages ; 24 cmISBN: 9781801071970Subject(s): BIG DATA | DATABASE MANAGEMENT | PYTHON (COMPUTER PROGRAM LANGUAGE)LOC classification: QA 76.73.P98 .G46 2021
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LRC - Main | National University - Manila | Machine Learning | General Circulation | GC QA 76.73.P98 .G46 2021 (Browse shelf (Opens below)) | c.1 | Available | NULIB000019744 |
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| GC QA 76.73.B3 .W55 2010 Beginning Microsoft Visual Basic 2010 / | GC QA 76.73.J38 .S35 2018 Java : the complete reference / | GC QA 76.73.P98 .B55 2020 Mastering reinforcement learning with python : build next-generation, self-learning models using reinforcement learning techniques and best practices / | GC QA 76.73.P98 .G46 2021 Practical data science with Python : learn tools and techniques from hands-on examples to extract insights from data / | GC QA 76.73.P98 .G75 2022 Practical deep reinforcement learning with python / | GC QA 76.73.P98 .G78 2019 Data science from scratch : first principles with python / | GC QA 76.73.P98 .R37 2022 Machine Learning with PyTorch and Scikit-Learn : develop machine learning and deep learning models with Python / |
Includes index.
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.
The book provides a one-stop solution for getting into data science with Python and teaches how to extract insights from data.
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