Fouda E. - Learn Data Science Using Python [2024, PDF, ENG] torrent

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Learn Data Science Using Python

Год издания: 2024
Автор: Fouda E.

Издательство: Apress
ISBN: 979-8-8688-0935-4
Язык: Английский

Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 186

Описание: 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.
You will learn, step-by-step, how to do visualizations. The book includes explanations of the code with screenshots of every step. The whole book follows the paradigm of data science in action to demonstrate how to perform complicated tasks and visualizations in Python in an easy to follow way and with real big data for interesting hands-on-labs.
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.
What You'll Learn:
- 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
[spoiler="Примеры страниц (скриншоты)"]

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[spoiler="Оглавление"]
About the Author ix
About the Technical Reviewer xi
Introduction xiii
Chapter 1: Data Science in Action 1
Chapter 2: Getting Started 23
Chapter 3: Data Visualizations 45
Chapter 4: Statistical Analysis and Linear Models 85
Chapter 5: Data Preprocessing and Feature Engineering 105
Chapter 6: Preparing Data for Analysis 123
Chapter 7: Regression 155
Index 177
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