Название: Learn Data Science Using Python: A Quick-Start Guide
Автор: Engy Fouda
Издательство: Apress
Год: 2024
Страниц: 190
Язык: английский
Формат: pdf (true), epub
Размер: 14.0 MB

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

Who This Book Is For:
The primary audience of this book is students who are newbies to Data Science and might not have deep programming experience. Consequently, university professors can use it as a handout for their courses. Also, technical instructors, like myself, who teach professionals from various industry sectors at certified training centers, can teach from it and use it as the courses’ textbook.

Also, system analysts and scientists who are experienced but new to Python will find faster and more efficient tools to achieve their daily tasks. Moreover, data journalists and investigative reporters will find the book easy to follow and can generate essential visualizations fast.