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Data Analysis Foundations with Python
Data Analysis Foundations with Python

Data Analysis Foundations with Python: Master Data Analysis with Python: From Basics to Advanced Techniques

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Profile Icon Cuantum Technologies LLC
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S$46.99
eBook Jun 2024 551 pages 1st Edition
eBook
S$46.99
Arrow left icon
Profile Icon Cuantum Technologies LLC
Arrow right icon
S$46.99
eBook Jun 2024 551 pages 1st Edition
eBook
S$46.99
eBook
S$46.99

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Key benefits

  • From Python basics to advanced data analysis techniques.
  • Apply your skills to practical scenarios through real-world case studies.
  • Detailed projects and quizzes to help gain the necessary skills.

Description

Embark on a comprehensive journey through data analysis with Python. Begin with an introduction to data analysis and Python, setting a strong foundation before delving into Python programming basics. Learn to set up your data analysis environment, ensuring you have the necessary tools and libraries at your fingertips. As you progress, gain proficiency in NumPy for numerical operations and Pandas for data manipulation, mastering the skills to handle and transform data efficiently. Proceed to data visualization with Matplotlib and Seaborn, where you'll create insightful visualizations to uncover patterns and trends. Understand the core principles of exploratory data analysis (EDA) and data preprocessing, preparing your data for robust analysis. Explore probability theory and hypothesis testing to make data-driven conclusions and get introduced to the fundamentals of machine learning. Delve into supervised and unsupervised learning techniques, laying the groundwork for predictive modeling. To solidify your knowledge, engage with two practical case studies: sales data analysis and social media sentiment analysis. These real-world applications will demonstrate best practices and provide valuable tips for your data analysis projects.

Who is this book for?

This course adopts a hands-on approach, seamlessly blending theoretical lessons with practical exercises and real-world case studies. Practical exercises are designed to apply theoretical knowledge, providing learners with the opportunity to experiment and learn through doing. Real-world applications and examples are integrated throughout the course to contextualize concepts, making the learning process engaging, relevant, and effective. By the end of the course, students will have a thorough understanding of the subject matter and the ability to apply their knowledge in practical scenarios.

What you will learn

  • Develop a strong foundation in Python for data analysis.
  • Manipulate and analyze data using NumPy and Pandas.
  • Create insightful data visualizations with Matplotlib and Seaborn.
  • Understand and apply probability theory and hypothesis testing.
  • Implement supervised and unsupervised machine learning algorithms.
  • Execute real-world data analysis projects with confidence.

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jun 12, 2024
Length: 551 pages
Edition : 1st
Language : English
ISBN-13 : 9781836209065
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Languages :
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What do you get with eBook?

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Product feature icon Download this book in EPUB and PDF formats
Product feature icon DRM FREE - Read whenever, wherever and however you want
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OR
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Product Details

Publication date : Jun 12, 2024
Length: 551 pages
Edition : 1st
Language : English
ISBN-13 : 9781836209065
Category :
Languages :
Tools :

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Table of Contents

37 Chapters
Code Blocks Resource Chevron down icon Chevron up icon
Premium Customer Support Chevron down icon Chevron up icon
Who we are Chevron down icon Chevron up icon
Introduction Chevron down icon Chevron up icon
Who is This Book For? Chevron down icon Chevron up icon
How to Use This Book Chevron down icon Chevron up icon
Acknowledgments Chevron down icon Chevron up icon
Chapter 1: Introduction to Data Analysis and Python Chevron down icon Chevron up icon
Quiz for Part I: Introduction to Data Analysis and Python Chevron down icon Chevron up icon
Chapter 2: Getting Started with Python Chevron down icon Chevron up icon
Chapter 3: Basic Python Programming Chevron down icon Chevron up icon
Chapter 4: Setting Up Your Data Analysis Environment Chevron down icon Chevron up icon
Quiz for Part II: Python Basics for Data Analysis Chevron down icon Chevron up icon
Chapter 5: NumPy Fundamentals Chevron down icon Chevron up icon
Chapter 6: Data Manipulation with Pandas Chevron down icon Chevron up icon
Chapter 7: Data Visualization with Matplotlib and Seaborn Chevron down icon Chevron up icon
Quiz for Part III: Core Libraries for Data Analysis Chevron down icon Chevron up icon
Chapter 8: Understanding EDA Chevron down icon Chevron up icon
Chapter 9: Data Preprocessing Chevron down icon Chevron up icon
Chapter 10: Visual Exploratory Data Analysis Chevron down icon Chevron up icon
Quiz for Part IV: Exploratory Data Analysis (EDA) Chevron down icon Chevron up icon
Project 1: Analyzing Customer Reviews Chevron down icon Chevron up icon
Chapter 11: Probability Theory Chevron down icon Chevron up icon
Chapter 12: Hypothesis Testing Chevron down icon Chevron up icon
Quiz for Part V: Statistical Foundations Chevron down icon Chevron up icon
Chapter 13: Introduction to Machine Learning Chevron down icon Chevron up icon
Chapter 14: Supervised Learning Chevron down icon Chevron up icon
Chapter 15: Unsupervised Learning Chevron down icon Chevron up icon
Quiz Part VI: Machine Learning Basics Chevron down icon Chevron up icon
Project 2: Predicting House Prices Chevron down icon Chevron up icon
Chapter 16: Case Study 1: Sales Data Analysis Chevron down icon Chevron up icon
Chapter 17: Case Study 2: Social Media Sentiment Analysis Chevron down icon Chevron up icon
Quiz Part VII: Case Studies Chevron down icon Chevron up icon
Project 3: Capstone Project: Building a Recommender System Chevron down icon Chevron up icon
Chapter 18: Best Practices and Tips Chevron down icon Chevron up icon
Conclusion Chevron down icon Chevron up icon
Know more about us Chevron down icon Chevron up icon
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