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Learn data science using Python = a quick-start guide /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learn data science using Python/ by Engy Fouda.
Reminder of title:	a quick-start guide /
Author:	Fouda, Engy.
Published:	Berkeley, CA :Apress : : 2024.,
Description:	xiv, 180 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Electronic data processing. -
Online resource:	https://doi.org/10.1007/979-8-8688-0935-4
ISBN:	9798868809354

Learn data science using Python = a quick-start guide /
Fouda, Engy.

Learn data science using Pythona quick-start guide /[electronic resource] :by Engy Fouda. - Berkeley, CA :Apress :2024. - xiv, 180 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9798868809354

Standard No.: 10.1007/979-8-8688-0935-4doiSubjects--Topical Terms:

520749
Electronic data processing.

LC Class. No.: QA76

Dewey Class. No.: 004

Learn data science using Python = a quick-start guide /
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520 $a 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.
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