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  • Python for Excel Users: A Beginner's Guide

    Python for Excel Users by Chou, Chi-Chun; Wang, David;

    A Beginner's Guide

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      • Publisher's listprice GBP 89.99
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        44 410 Ft (42 295 Ft + 5% VAT)
      • Discount 10% (cc. 4 441 Ft off)
      • Discounted price 39 969 Ft (38 066 Ft + 5% VAT)

    44 410 Ft

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    Product details:

    • Edition number 1
    • Publisher Chapman and Hall
    • Date of Publication 6 October 2025

    • ISBN 9781032936765
    • Binding Hardback
    • No. of pages360 pages
    • Size 234x156 mm
    • Weight 453 g
    • Language English
    • Illustrations 247 Illustrations, black & white; 89 Halftones, black & white; 158 Line drawings, black & white; 4 Tables, black & white
    • 700

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    Short description:

    In today’s data-driven world, the ability to efficiently analyze and interpret information is more crucial than ever, especially in the business sector. "Python for Excel Users: A Beginner's Guide" is tailored for business students and professionals proficient in Microsoft Excel but are ready to embark on their Python journey.

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    Long description:

    Introduction: Elevate Your Analytics with Python


    In today’s data-driven world, the ability to efficiently analyze and interpret information is more crucial than ever, especially in the business sector. Python for Excel Users: A Beginner’s Guide is tailored for business students and professionals who are proficient in Microsoft Excel but are ready to embark on their Python journey. As a powerful and versatile programming language, Python has become indispensable in data analysis. This book bridges the gap between Excel and Python by providing parallel exercises that demonstrate how Python can amplify business analytics tasks with unmatched efficiency and flexibility.


    Through its side-by-side comparisons, interactive Python exercises, and a "teachable moment" approach, this guide offers a unique and intuitive learning experience. By translating familiar Excel tasks into Python’s dynamic and versatile ecosystem, you’ll not only enhance your data analysis skills but also gain confidence in programming.


    Why Python?


    Did you know that Python powers cutting-edge technologies like ChatGPT? Indeed, Python forms the foundation of many machine learning algorithms, including large language models (LLMs). Python is more than a programming language; it’s a tool for understanding and shaping the digital world. Despite its advanced capabilities, Python’s simple, readable syntax makes it accessible to everyone – from professional software developers to citizen developers like you. Dubbed the "language of the people," Python is revolutionizing how we approach problem-solving and automation in the modern world.


    Becoming Tomorrow’s Tech- Savvy Leaders


    The leaders of tomorrow are not just visionaries – they are innovators who harness the power of technology to drive change and inspire others. This book guides you through different scenarios to help you understand the connections between business questions and analytics steps we are taking.


    As business students embracing Python, you’re positioning yourselves as future-ready leaders equipped to navigate and excel in the complexities of modern business.


    Welcome to a journey that will elevate your analytics, expand your technological fluency, and transform you into a tech-savvy leader of the future.

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    Table of Contents:

    Introduction
    Chapter 1: Data Exploration and Cleaning
    Chapter 2: Basic Computation
    Chapter 3: Aggregating Data Using Group-By and Pivot Tables
    Chapter 4: Data Visualization and Ranking
    Chapter 5: Conditional Functions and What-if Analysis
    Chapter 6: Record Lookup and Data Segmentation
    Chapter 7: Processing
    Chapter 8: Date Processing
    Chapter 9: Table Join and Merge
    Index

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