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AI Engineering
Python

Data Analysis Foundations with Python

Master Python and Data Analysis using NumPy, Pandas, Matplotlib, and Seaborn

Unleash Python for data analysis! This book equips you with the essential tools to clean, manipulate, and visualize data using Python libraries like Pandas and NumPy.

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What You'll Get from This Book

18 chapters spanning over 470 pages

More than 450 explanatories blocks of code

More than 48 practical exercises

7 Quizzes to test your knowledge

3 Practical "Real World" Projects

About thIS book

Master Data Analysis Basics with Python

Transform your raw data into valuable insights with "Data Analysis Foundations with Python." This comprehensive guide equips you with the essential skills and knowledge to navigate the world of data analysis, using Python as your powerful tool.

Through engaging explanations, practical exercises, and real-world examples, you'll gain a thorough understanding of fundamental concepts like:

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  • Data Cleaning and Manipulation: Learn how to prepare your data for analysis by cleaning inconsistencies, handling missing values, and transforming it into a usable format.
  • Exploratory Data Analysis (EDA): Master the art of exploring your data, uncovering hidden patterns, and gaining valuable insights through techniques like visualization and summary statistics.
  • Data Wrangling with Python Libraries: Discover powerful libraries like pandas and NumPy that streamline data manipulation tasks, enabling you to work with large datasets efficiently.

Become a Data Analyst with Python Fundamentals

Through engaging exercises and real-world projects, you'll delve into the essential building blocks of data analysis:

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  • Data Import and Exploration: Learn how to import data from various sources, explore its structure and content, and identify any potential issues.
  • Data Visualization: Master the art of creating informative visualizations using libraries like Matplotlib and Seaborn, effectively communicating insights to both technical and non-technical audiences.
  • Statistical Analysis with Python: Understand core statistical concepts and apply them to your data using Python libraries, enabling you to draw meaningful conclusions and make informed decisions.
  • ‍

By the end of this journey, you'll be equipped with the foundational knowledge and practical skills to confidently approach data analysis challenges. Whether you're pursuing a career as a data analyst or simply seeking to extract valuable insights from your own data, this book empowers you to unlock the power of Python and become a data-driven individual.

Unlock the potential of your data and embark on a rewarding career path with "Data Analysis Foundations with Python." This practical guide provides you with the fundamental skills and knowledge necessary to become a data analyst using Python, one of the most popular languages in the data science field.

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By mastering these crucial foundations, you'll be well-equipped to confidently approach any data analysis project. This book empowers you to not only understand the concepts but also apply them to real-world scenarios, setting you on the path to becoming a proficient data analyst.

Data analysis is fundamental in today’s data-driven world, influencing industries like finance, healthcare, marketing, and artificial intelligence. Due to its versatility and extensive libraries, Python has become the go-to language for data analysis. Data Analysis Foundations with Python provides a structured and hands-on approach to working with data. It is an essential resource for anyone looking to develop data-driven decision-making skills, whether for business intelligence, research, or AI applications.
This book teaches you how to efficiently clean, manipulate, visualize, and analyze data using Python’s most powerful libraries, including Pandas, NumPy, and Matplotlib. You will learn how to structure datasets, perform exploratory data analysis, and extract meaningful insights. With practical exercises and real-world case studies, you will develop the ability to handle real datasets confidently, equipping you with the skills to solve data-related problems in professional environments.
Unlike general Python programming books, Data Analysis Foundations with Python focuses on data manipulation and analytical techniques. It combines practical exercises, real-world datasets, and clear explanations to bridge the programming and analytical thinking gap. Whether you’re working with spreadsheets, databases, or large datasets, this book teaches you how to turn raw data into valuable insights using Python.
A basic understanding of Python is recommended, but no prior data analysis experience is required. The book starts with foundational concepts and gradually builds up to more advanced techniques, making it accessible for beginners and a valuable resource for those looking to solidify their data analysis skills.
Access to the Cuantum Technologies VIP customer service, with a dedicated team of developers ready to answer all your questions. A code repository with fully working examples and pre-tested, production-ready code. The Success University e-learning platform, where you can access additional resources and free video content to reinforce your learning. Regular updates and additional materials to stay updated with new advancements in data analysis and Python programming.

Table of contents

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Chapter 1: Introduction to Data Analysis and Python

1.1 Importance of Data Analysis

1.2 Role of Python in Data Analysis

1.3 Overview of the Data Analysis Process

1.4 Practical Exercises for Chapter 1: Introduction to Data Analysis and Python

1.5 Chapter 1 Conclusion of Introduction to Data Analysis and Python

Chapter 2: Getting Started with Python

2.1 Installing Python

2.2 Your First Python Program

2.3 Variables and Data Types

2.4 Practical Exercises for Chapter 2: Getting Started with Python

2.5 Chapter 2 Conclusion of Getting Started with Python

Quiz for Part I: Setting the Stage

Multiple Choice Questions

True or False Questions

Answer Key for Quiz for Part I: Introduction to Data Analysis and Python

Chapter 3: Basic Python Programming

3.1 Control Structures

3.2 Functions and Modules

3.3 Python Scripting

3.4 Practical Exercises Chapter 3: Basic Python Programming

3.5 Chapter 3 Conclusion of Basic Python Programming

Chapter 4: Setting Up Your Data Analysis Environment

4.1 Installing Anaconda

4.2 Jupyter Notebook Basics

4.3 Git for Version Control

4.4 Practical Exercises Chapter 4: Setting Up Your Data Analysis Environment

4.5 Chapter 4 Conclusion of Setting Up Your Data Analysis Environment

Quiz for Part II: Python Basics for Data Analysis

Multiple-Choice Questions

True/False Questions

Answer Key for Quiz for Part II: Python Basics for Data Analysis

Chapter 5: NumPy Fundamentals

5.1 Arrays and Matrices

5.2 Basic Operations

5.3 Advanced NumPy Functions

5.4 Practical Exercises for Chapter 5: NumPy Fundamentals

5.5 Chapter 5 Conclusion of NumPy Fundamentals

Chapter 6: Data Manipulation with Pandas

6.1 DataFrames and Series

6.2 Data Wrangling

6.3 Handling Missing Data

6.4 Real-World Examples: Challenges and Pitfalls in Handling Missing Data

6.5 Practical Exercises Chapter 6: Data Manipulation with Pandas

Chapter 7: Data Visualization with Matplotlib and Seaborn

7.1 Basic Plotting with Matplotlib

7.2 Advanced Visualizations

7.3 Introduction to Seaborn

7.4 Practical Exercises - Chapter 7: Data Visualization with Matplotlib and Seaborn

7.5 Chapter 7 Conclusion of Data Visualization with Matplotlib and Seaborn

Chapter 8: Understanding EDA

8.1 Importance of EDA

8.2 Types of Data

8.3 Descriptive Statistics

8.4 Practical Exercises for Chapter 8: Understanding EDA

8.5 Chapter 8 Conclusion of Understanding EDA

Quiz for Part III: Core Libraries for Data Analysis

Chapter 5: NumPy Fundamentals

Chapter 6: Data Manipulation with Pandas

Chapter 7: Data Visualization with Matplotlib and Seaborn

Answer Key for Quiz for Part III: Core Libraries for Data Analysis

Chapter 9: Data Preprocessing

9.1 Data Cleaning

9.2 Feature Engineering

9.3 Data Transformation

9.4 Practical Exercises: Chapter 9: Data Preprocessing

9.5 Chapter 9 Conclusion

Chapter 10: Visual Exploratory Data Analysis

10.1 Univariate Analysis

10.2 Bivariate Analysis

10.3 Multivariate Analysis

10.4 Practical Exercises Chapter 10

10.5 Chapter 10 Conclusion of Data Preprocessing

Quiz for Part IV: Exploratory Data Analysis (EDA)

Questions of Quiz for Part IV: Exploratory Data Analysis (EDA)

Answers of Quiz for Part IV: Exploratory Data Analysis (EDA)

Project 1: Analyzing Customer Reviews

1.1 Data Collection

1.2: Data Cleaning

1.3: Data Visualization

1.4: Basic Sentiment Analysis

Chapter 11: Probability Theory

11.1 Basic Concepts

11.2: Probability Distributions

11.3: Specialized Probability Distributions

11.4 Bayesian Theory

11.5 Practical Exercises for Chapter 11: Probability Theory

Chapter 12: Hypothesis Testing

12.1 Null and Alternative Hypotheses

12.2 t-test and p-values

12.3 ANOVA (Analysis of Variance)

12.4 Practical Exercises Chapter 12: Hypothesis Testing

12.5 Chapter 12 Conclusion of Hypothesis Testing

Quiz for Part V: Statistical Foundations

Chapter 11: Probability Theory

Chapter 12: Hypothesis Testing

Answers of Quiz for Part V: Statistical Foundations

Chapter 13: Introduction to Machine Learning

13.1 Types of Machine Learning

13.2 Basic Algorithms

13.3 Model Evaluation

13.4 Practical Exercises Chapter 13: Introduction to Machine Learning

13.5 Chapter 13 Conclusion of Introduction to Machine Learning

Chapter 14: Supervised Learning

14.1 Linear Regression

14.2 Types of Classification Algorithms

14.3 Decision Trees

14.4 Practical Exercises Chapter 14: Supervised Learning

14.5 Chapter 14 Conclusion of Supervised Learning

Chapter 15: Unsupervised Learning

15.1 Clustering

15.2 Principal Component Analysis (PCA)

15.3 Anomaly Detection

15.4 Practical Exercises Chapter 15: Unsupervised Learning

15.5 Chapter 15 Conclusion of Unsupervised Learning

Quiz Part VI: Machine Learning Basics

Chapter 13: Introduction to Machine Learning

Chapter 14: Supervised Learning

Chapter 15: Unsupervised Learning

Answer Key of Quiz Part VI: Machine Learning Basics

Project 2: Predicting House Prices

Problem Statement

Data Collection and Preprocessing

Feature Engineering

Model Building and Evaluation

Chapter 16: Case Study 1: Sales Data Analysis

16.1 Problem Definition

16.2 EDA and Visualization

16.3 Predictive Modeling

16.4 Practical Exercises: Sales Data Analysis

16.5 Chapter 16 Conclusion of Sales Data Analysis

Chapter 17: Case Study 2: Social Media Sentiment Analysis

17.1 Data Collection

17.2 Text Preprocessing

17.3 Sentiment Analysis

17.4 Practical Exercises of Chapter 17: Case Study 2: Social Media Sentiment Analysis

17.5 Chapter 17 Conclusion of Social Media Sentiment Analysis

Quiz Part VII: Case Studies

Chapter 16: Case Study 1 - Sales Data Analysis

Chapter 17: Case Study 2 - Social Media Sentiment Analysis

Answers of Quiz Part VII: Case Studies

Project 3: Capstone Project: Building a Recommender System

Problem Statement

Data Collection and Preprocessing

Model Building

Evaluation and Deployment

Chapter 18: Best Practices and Tips

18.1 Code Organization

18.2 Documentation

Reviews

What our readers are saying about this book

Explore the reviews to understand why this book is a great choice! Discover how others have gained from the knowledge and insights it provides. Get a taste of the exciting content that awaits you and see if this book is the perfect fit for your journey.

Recommended by dozens of people
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Verica Mihajlovic

This book is not one-time read! This is a study material, and it is very well written and understandable for beginners. I highly recomend this book to anyone who wants to start learning about Python data analysis.

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Ryan Collins

This series of books is on the cutting edge and is a must for anyone who wants to delve into the world of AI. The price is a steep, but the information gained was worth it for me.

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