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Python for Data Science: 2 Books in 1. A Practical Beginners Guide to learn Python Programming, introducing into Data Analytics, Machine learning, Web Development, with Hands-on Projects
Python is the most popular programming language of the 21st century and data scientist is the most requested job of the 21st century. So anyone with skills in both data science and Python will be in great demand in industry.
Python for Data Science: 2 Books in 1. A Practical Beginners Guide to learn Python Programming, introducing into Data Analytics, Machine learning, Web Development, with Hands-on Projects
Item #: 39764022

Python for Data Science: 2 Books in 1. A Practical Beginners Guide to learn Python Programming, introducing into Data Analytics, Machine learning,

Item #: 39764022

MKD 2503

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Python is the most popular programming language of the 21st century and data scientist is the most requested job of the 21st century. So anyone with skills in both data science and Python will be in great demand in industry.
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What Stands Out

Comprehensive Learning
This dual-book offers an extensive introduction to Python programming, data analytics, machine learning, and web development, making it ideal for beginners eager to learn multiple in-demand skills.
Hands-On Projects
Engage with practical, hands-on projects that reinforce learning by applying concepts in real-world scenarios, ensuring a deeper understanding of Python and its applications in data science.
Beginner Friendly
Designed for novice programmers, the clear explanations and step-by-step guides make complex topics accessible, encouraging users to build confidence as they progress through their data science journey.

Product Details

A practical beginner’s guide to Python programming for Data Analytics, Machine Learning, and Web Development. 2 books in 1. Get started with hands-on projects. Shop now on Ubuy North Macedonia.
  • A comprehensive guide containing 2 books in 1: Python crash course and Python for data analysis
  • Covers a wide range of topics including data science technologies, machine learning, data analysis with Python, and data visualization with Python
  • Designed to simplify the visualization and analysis of data and creation of machine learning models and artificial intelligence using Python
  • Ideal for beginners and does not require expensive textbooks
  • Includes hands-on projects for practical learning
  • Suitable for those interested in pursuing a career in data science, machine learning, and web development using Python
Publisher Independently published
Publication date October 27, 2020
Language English
Print length 265 pages
ISBN-13 979-8554509728
Item Weight 12.6 ounces (357.21 grams)
Dimensions 6 x 0.6 x 9 inches (15.2 x 1.5 x 22.9 cm)

Who Should Buy?

Suitable For
  • Beginner Programmers

    Ideal for those starting programming or transitioning from other languages to learn Python fundamentals.

  • Data Enthusiasts

    Perfect for individuals wanting to delve into data analytics and machine learning without prior knowledge.

  • Hands-on Learners

    Great for those who prefer practical experience through projects that reinforce learning concepts in real-world scenarios.

Not Suitable For
  • Advanced Users

    Not suitable for experienced programmers looking for advanced Python techniques or in-depth data science methodologies.

Product Description

Python for Data Science: 2 Books in 1. A Practical Beginners Guide to learn Python Programming, introducing into Data Analytics, Machine learning, Web Development, with Hands-on Projects

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Data Modeling & Design Editorial Review

Python for Data Science: 2 Books in 1 offers an extensive overview of Python programming tailored for beginners looking to delve into data analytics, machine learning, and web development. With 265 pages packed with 22 chapters, it aims to equip readers with hands-on project experience. However, there have been mentions of spelling errors and formatting issues, indicating that its practicality may vary depending on individual preferences. Readers appreciate the detailed approach and straightforward explanations presented, although some found the content to lack depth and quality in presentation. Nonetheless, this guide serves as a basic entry point into the world of data science using Python.

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Pros

  • Extensive content covering various Python applications
  • Hands-on projects enhance learning experience
  • Detailed approach suitable for beginners
  • Covers data analytics and machine learning
  • Good introductory resource for new learners

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