Yuli Vasiliev
Python For Data Science: A Hands-On Introduction
Python For Data Science: A Hands-On Introduction
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Python is an ideal choice for data analysis due to its versatility and practicality. Python for Data Science provides a learn-by-doing approach to data analysis with practical examples and hands-on activities.
Format: Paperback / softback
Length: 180 pages
Publication date: 02 August 2022
Publisher: No Starch Press,US
Python is an ideal language for accessing, manipulating, and gaining insights from a wide range of data types. Python for Data Science provides a hands-on learning experience with a focus on practical examples and real-world applications. Through this course, you will learn how to write Python code to retrieve, transform, and analyze data, employing state-of-the-art data processing techniques for business management, marketing, and decision support.
The Python language is highly versatile and efficient, making it suitable for a wide range of data-related tasks. Whether you are working with structured data, such as databases or CSV files, or unstructured data, such as images or text, Python has the tools and libraries to handle it effectively.
One of the key strengths of Python for Data Science is its ability to handle large datasets efficiently. Python's powerful data structures, such as lists, dictionaries, and arrays, allow you to store and manipulate large amounts of data with ease. This makes it ideal for tasks such as data mining, machine learning, and statistical analysis, which require processing large amounts of data.
Another advantage of Python for Data Science is its simplicity and readability. The language's clean and concise syntax makes it easy to understand and write code, even for beginners. This makes it an ideal language for teaching and learning data analysis, as it allows students to focus on the concepts and techniques rather than getting bogged down in syntax and technical details.
Python for Data Science also offers a wide range of libraries and frameworks that can be used for data analysis and manipulation. These libraries include popular tools such as NumPy, Pandas, SciPy, Matplotlib, and Seaborn, which provide powerful tools for data visualization, statistical analysis, machine learning, and more.
In addition to its versatility and efficiency, Python for Data Science is also highly portable. The language is supported on a wide range of operating systems, including Windows, macOS, and Linux, making it easy to deploy and run your data analysis projects on different platforms.
Overall, Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. Whether you are a beginner or an experienced data analyst, Python for Data Science provides the skills and knowledge you need to succeed in the field of data science. With its learn-by-doing approach, practical examples, and hands-on activities, this course will help you build a strong foundation in data analysis and prepare you for real-world data science projects.
Weight: 434g
Dimension: 177 x 234 x 21 (mm)
ISBN-13: 9781718502208
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