 Название: Data Analysis Foundations with Python: Master Python and Data Analysis using NumPy, Pandas, Matplotlib, and Seaborn: A Hands-On Guide with Projects and Case Studies
Автор: Cuantum Technologies
Издательство: Cuantum Technologies
Год: 2024
Страниц: 551
Язык: английский
Формат: pdf, epub (true)
Размер: 10.1 MB
"Artificial Intelligence, Deep Learning, Machine Learning — whatever you're doing if you don't understand it — learn it. Because otherwise, you're going to be a dinosaur within 3 years." - Mark Cuban, entrepreneur, and investor
Dive into data analysis with Python, starting from the basics to advanced techniques. This course covers Python programming, data manipulation with Pandas, data visualization, exploratory data analysis, and Machine Learning.
Key Features:
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.
Book 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.
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.
Who this book is 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. This book also serves as the first step in a larger learning path aimed at becoming a fully-fledged Data Scientist or AI Engineer. Understanding the nuances of data analysis is foundational to fields like Machine Learning, natural language processing, and Deep Learning. By mastering the concepts laid out in this book, you're setting a strong foundation for more advanced studies in AI.
Contents:
Introduction
Who is This Book For?
How to Use This Book
Chapter 1: Introduction to Data Analysis and Python
Quiz for Part I: Introduction to Data Analysis and Python
Chapter 2: Getting Started with Python
Chapter 3: Basic Python Programming
Chapter 4: Setting Up Your Data Analysis Environment
Quiz for Part II: Python Basics for Data Analysis
Chapter 5: NumPy Fundamentals
Chapter 6: Data Manipulation with Pandas
Chapter 7: Data Visualization with Matplotlib and Seaborn
Quiz for Part III: Core Libraries for Data Analysis
Chapter 8: Understanding EDA
Chapter 9: Data Preprocessing
Chapter 10: Visual Exploratory Data Analysis
Quiz for Part IV: Exploratory Data Analysis (EDA)
Project 1: Analyzing Customer Reviews
Chapter 11: Probability Theory
Chapter 12: Hypothesis Testing
Quiz for Part V: Statistical Foundations
Chapter 13: Introduction to Machine Learning
Chapter 14: Supervised Learning
Chapter 15: Unsupervised Learning
Quiz Part VI: Machine Learning Basics
Project 2: Predicting House Prices
Chapter 16: Case Study 1: Sales Data Analysis
Chapter 17: Case Study 2: Social Media Sentiment Analysis
Quiz Part VII: Case Studies
Project 3: Capstone Project: Building a Recommender System
Chapter 18: Best Practices and Tips
Conclusion
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Data Analysis Foundations with Python: Master Python and Data Analysis using NumPy, Pandas, Matplotlib 
