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  • Statistical Learning with Python: The Essential Guide for Data-Driven Decisions

    • Item No : 395945229598
    • Condition : Brand New
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    • Seller : grandeagleretail
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    • * Item Description

    • Statistical Learning with Python

      by Maxwell Osbert

      Estimated delivery 3-12 business days

      Format Paperback

      Condition Brand New

      Description This book utilizes Python, a versatile and beginner-friendly programming language widely used in data analysis and machine learning. No prior coding experience is necessary - we'll guide you step-by-step. Written by a data science expert with a passion for clear communication, this book equips you with the skills and knowledge to confidently analyze data and translate insights into actionable strategies. Tired of data overwhelm? This book unveils the secrets to transforming numbers into impactful narratives. Master data analysis techniques, explore data visualization tools, and learn how to craft compelling data stories that drive informed decision-making. What's Inside?Practical hands-on projects to solidify your learning.In-depth explanations of essential data analysis concepts.Clear and concise code examples using Python libraries like Pandas and Matplotlib.Ethical considerations for responsible data use.Proven storytelling techniques to captivate your audience with data insights.This book is designed for anyone curious about the power of data analysis, from business professionals to marketing specialists, entrepreneurs, and students. No matter your background, you can unlock the potential of data-driven decision making! Invest a few hours learning these valuable skills, and reap the rewards of data-driven success for years to come. Stop feeling lost in a sea of data. Unleash the power of data storytelling! Get your copy today and embark on your journey to becoming a data analysis rockstar.

      Details

      • ISBN-13 9798326180834
      • Title Statistical Learning with Python
      • Author Maxwell Osbert
      • Format Paperback
      • Year 2024
      • Pages 248
      • Publisher Independently Published
      GE_Item_ID:160630727;

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