Learn Data Science Using Python : A Quick-Start Guide / by Engy Fouda.

Format:

Book

Author/Creator:

Fouda, Engy.

Series:

Professional and Applied Computing Series

Language:

English

Subjects (All):

Artificial intelligence--Data processing.

Artificial intelligence.

Machine learning.

Python (Computer program language).

Data Science.

Artificial Intelligence.

Machine Learning.

Python.

Local Subjects:

Data Science.

Artificial Intelligence.

Machine Learning.

Python.

Physical Description:

1 online resource (190 pages)

Edition:

1st ed. 2024.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2024.

Summary:

Harness the capabilities of Python and gain the expertise need to master data science techniques. This step-by-step book guides you through using Python to achieve tasks related to data cleaning, statistics, and visualization. You’ll start by reviewing the foundational aspects of the data science process. This includes an extensive overview of research points and practical applications, such as the insightful analysis of presidential elections. The journey continues by navigating through installation procedures and providing valuable insights into Python, data types, typecasting, and essential libraries like Pandas and NumPy. You’ll then delve into the captivating world of data visualization. Concepts such as scatter plots, histograms, and bubble charts come alive through detailed discussions and practical code examples, unraveling the complexities of creating compelling visualizations for enhanced data understanding. Statistical analysis, linear models, and advanced data preprocessing techniques are also discussed before moving on to preparing data for analysis, including renaming variables, variable rearrangement, and conditional statements. Finally, you’ll be introduced to regression techniques, demystifying the intricacies of simple and multiple linear regression, as well as logistic regression. You will: Understand installation procedures and valuable insights into Python, data types, typecasting Examine the fundamental statistical analysis required in most data science and analytics reports Clean the most common data set problems Use linear progression for data prediction.

Contents:

Chapter 1: Data Science in Action

Chapter 2: Getting Started

Chapter 3: Data Visualization

Chapter 4: Statistical Analysis and Linear Models

Chapter 5: Advanced Data Pre-processing and Feature Engineering

Chapter 6: Preparing Data for Analysis

Chapter 7: Regression.

Notes:

Includes index.

Description based on publisher supplied metadata and other sources.

ISBN:

9798868809354

OCLC:

1470910852