Data Analysis Foundations with Python : Master Data Analysis with Python / Cuantum Technologies LLC.

Format:

Book

Author/Creator:

Cuantum Technologies LLC, author.

Language:

English

Subjects (All):

Python (Computer program language).

Physical Description:

1 online resource (551 pages)

Edition:

First edition.

Place of Publication:

Plano, TX : Cuantum Technologies LLC, [2023]

System Details:

Mode of access: World Wide Web.

Summary:

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.

Contents:

Intro

Code Blocks Resource

Premium Customer Support

Who we are

Our Philosophy

Our Expertise

Introduction

Who is This Book For?

Beginners and Students

Career Changers

Professionals in Data-Adjacent Roles

Aspiring Data Scientists and AI Engineers

Educators and Trainers

How to Use This Book

Start at the Beginning

Work Through the Exercises

Take the Quizzes

Participate in Projects

Utilize Additional Resources

Collaborate and Share

Experiment and Explore

Acknowledgments

Chapter 1: Introduction to Data Analysis and Python

1.1 Importance of Data Analysis

1.1.1 Informed Decision-Making

1.1.2 Identifying Trends

1.1.3 Enhancing Efficiency

1.1.4 Resource Allocation

1.1.5 Customer Satisfaction

1.1.6 Social Impact

1.1.7 Innovation and Competitiveness

1.2 Role of Python in Data Analysis

1.2.1 User-Friendly Syntax

1.2.2 Rich Ecosystem of Libraries

1.2.3 Community Support

1.2.4 Integration and Interoperability

1.2.5 Scalability

1.2.6 Real-world Applications

1.2.7 Versatility Across Domains

1.2.8 Strong Support for Data Science Operations

1.2.9 Open Source Advantage

1.2.10 Easy to Learn, Hard to Master

1.2.11 Cross-platform Compatibility

1.2.12 Future-Proofing Your Skillset

1.2.13 The Ethical Aspect

1.3 Overview of the Data Analysis Process

1.3.1 Define the Problem or Question

1.3.2 Data Collection

1.3.3 Data Cleaning and Preprocessing

1.3.4 Exploratory Data Analysis (EDA)

1.3.5 Data Modeling

1.3.6 Evaluate and Interpret Results

1.3.7 Communicate Findings

1.3.8 Common Challenges and Pitfalls

1.3.9 The Complexity of Real-world Data

1.3.10 Selection Bias

1.3.11 Overfitting and Underfitting

Practical Exercises for Chapter 1

Exercise 1: Define a Data Analysis Problem.

Exercise 2: Data Collection with Python

Exercise 3: Basic Data Cleaning with Pandas

Exercise 4: Create a Basic Plot

Exercise 5: Evaluate a Simple Model

Conclusion for Chapter 1

Quiz for Part I: Introduction to Data Analysis and Python

Chapter 2: Getting Started with Python

2.1 Installing Python

2.1.1 For Windows Users:

2.1.2 For Mac Users:

2.1.3 For Linux Users:

2.1.4 Test Your Installation

2.2 Your First Python Program

2.2.1 A Simple Print Function

2.2.2 Variables and Basic Arithmetic

2.2.3 Using Python's Interactive Mode

2.3 Variables and Data Types

2.3.1 What is a Variable?

2.3.2 Data Types in Python

2.3.3 Declaring and Using Variables

2.3.4 Type Conversion

2.3.5 Variable Naming Conventions and Best Practices

Practical Exercises for Chapter 2

Exercise 1: Install Python

Exercise 2: Your First Python Script

Exercise 3: Working with Variables

Exercise 4: Type Conversion

Exercise 5: Explore Data Types

Exercise 6: Variable Naming

Chapter 2 Conclusion

Chapter 3: Basic Python Programming

3.1 Control Structures

3.1.1 If, Elif, and Else Statements

3.1.2 For Loops

3.1.3 While Loops

3.1.4 Nested Control Structures

3.2 Functions and Modules

3.2.1 Functions

3.2.2 Parameters and Arguments

3.2.3 Return Statement

3.2.4 Modules

3.2.5 Creating Your Own Module

3.2.6 Lambda Functions

3.2.7 Function Decorators

3.2.8 Working with Third-Party Modules

3.3 Python Scripting

3.3.1 Writing Your First Python Script

3.3.2 Script Execution and Command-Line Arguments

3.3.3 Automating Tasks

3.3.4 Debugging Scripts

3.3.5 Scheduling Python Scripts

3.3.6 Script Logging

3.3.7 Packaging Your Scripts

Practical Exercises Chapter 3

Exercise 1: Your First Script

Exercise 2: Command-Line Arguments

Exercise 3: CSV File Reader.

Exercise 4: Simple Task Automation

Exercise 5: Debugging Practice

Exercise 6: Script Logging

Chapter 3 Conclusion

Chapter 4: Setting Up Your Data Analysis Environment

4.1 Installing Anaconda

4.1.1 For Windows Users:

4.1.2 For macOS Users:

4.1.3 For Linux Users:

4.1.4 Troubleshooting and Tips

4.2 Jupyter Notebook Basics

4.2.1 Launching Jupyter Notebook

4.2.2 The Notebook Interface

4.2.3 Writing and Running Code

4.2.4 Markdown and Annotations

4.2.5 Saving and Exporting

4.2.6 Advanced Features of Jupyter Notebook

4.3 Git for Version Control

4.3.1 Why Use Git?

4.3.2 Installing Git

4.3.3 Basic Git Commands

4.3.4 Git Best Practices for Data Analysis

Practical Exercises Chapter 4

Exercise 4.1: Installing Anaconda

Exercise 4.2: Jupyter Notebook Basics

Exercise 4.3: Git for Version Control

Chapter 4 Conclusion

Quiz for Part II: Python Basics for Data Analysis

Chapter 5: NumPy Fundamentals

5.1 Arrays and Matrices

5.1.1 Additional Operations on Arrays

5.2 Basic Operations

5.2.1 Arithmetic Operations

5.2.2 Aggregation Functions

5.2.3 Boolean Operations

5.2.4 Vectorization

5.3 Advanced NumPy Functions

5.3.1 Aggregation Functions

5.3.2 Indexing and Slicing

5.3.3 Broadcasting with Advanced Operations

5.3.4 Logical Operations

5.3.5 Handling Missing Data

5.3.6 Reshaping Arrays

Practical Exercises for Chapter 5

Exercise 1: Create an Array

Exercise 2: Array Arithmetic

Exercise 3: Handling Missing Data

Exercise 4: Advanced NumPy Functions

Chapter 5 Conclusion

Chapter 6: Data Manipulation with Pandas

6.1 DataFrames and Series

6.1.1 DataFrame

6.1.2 Series

6.1.3 DataFrame vs Series

6.1.4 DataFrame Methods and Attributes

6.1.5 Series Methods and Attributes

6.1.6 Changing Data Types

6.2 Data Wrangling.

6.2.1 Reading Data from Various Sources

6.2.2 Handling Missing Values

6.2.3 Data Transformation

6.2.4 Data Aggregation

6.2.5 Merging and Joining DataFrames

6.2.6 Applying Functions

6.2.7 Pivot Tables and Cross-Tabulation

6.2.8 String Manipulation

6.2.9 Time Series Operations

6.3 Handling Missing Data

6.3.1 Detecting Missing Data

6.3.2 Handling Missing Values

6.3.3 Advanced Strategies

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

6.4.1 Case Study 1: Healthcare Data

6.4.2 Case Study 2: Financial Data

6.4.3 Challenges and Pitfalls:

Practical Exercises Chapter 6

Exercise 1: Creating DataFrames

Exercise 2: Missing Data Handling

Exercise 3: Data Wrangling

Chapter 6 Conclusion

Chapter 7: Data Visualization with Matplotlib and Seaborn

7.1 Basic Plotting with Matplotlib

7.1.1 Installing Matplotlib

7.1.2 Your First Plot

7.1.3 Customizing Your Plot

7.1.4 Subplots

7.1.5 Legends and Annotations

7.1.6 Error Bars

7.2 Advanced Visualizations

7.2.1 Customizing Plot Styles

7.2.2 3D Plots

7.2.3 Seaborn's Beauty

7.2.4 Heatmaps

7.2.5 Creating Interactive Visualizations

7.2.6 Exporting Your Visualizations

7.2.7 Performance Tips for Large Datasets

7.3 Introduction to Seaborn

7.3.1 Installation

7.3.2 Basic Plotting with Seaborn

7.3.3 Categorical Plots

7.3.4 Styling and Themes

7.3.5 Seaborn for Exploratory Data Analysis

7.3.6 Facet Grids

7.3.7 Joint Plots

7.3.8 Customizing Styles

Practical Exercises - Chapter 7

Exercise 1: Basic Line Plot

Exercise 2: Bar Chart with Seaborn

Exercise 3: Scatter Plot Matrix

Exercise 4: Advanced Plot - Heatmap

Exercise 5: Customize Your Plot

Chapter 7 Conclusion

Quiz for Part III: Core Libraries for Data Analysis

Chapter 8: Understanding EDA.

8.1 Importance of EDA

8.1.1 Why is EDA Crucial?

8.1.2 Code Example: Simple EDA using Pandas

8.1.3 Importance in Big Data

8.1.4 Human Element

8.1.5 Risk Mitigation

8.1.6 Examples from Different Domains

8.1.7 Comparing Datasets

8.1.8 Code Snippets for Visual EDA

8.2 Types of Data

8.2.1 Numerical Data

8.2.2 Categorical Data

8.2.3 Textual Data

8.2.4 Time-Series Data

8.2.5 Multivariate Data

8.2.6 Geospatial Data

8.3 Descriptive Statistics

8.3.1 What Are Descriptive Statistics?

8.3.2 Measures of Central Tendency

8.3.3 Measures of Variability

8.3.4 Why Is It Useful?

8.3.6 Example: Analyzing Customer Reviews

8.3.7 Skewness and Kurtosis

Practical Exercises for Chapter 8

Exercise 1: Understanding the Importance of EDA

Exercise 2: Identifying Types of Data

Exercise 3: Calculating Descriptive Statistics

Exercise 4: Understanding Skewness and Kurtosis

Chapter 8 Conclusion

Chapter 9: Data Preprocessing

9.1 Data Cleaning

9.1.1 Types of 'Unclean' Data

9.1.2 Handling Missing Data

9.1.3 Dealing with Duplicate Data

9.1.4 Data Standardization

9.1.5 Outliers Detection

9.1.6 Dealing with Imbalanced Data

9.1.7 Column Renaming

9.1.8 Encoding Categorical Variables

9.1.9 Logging the Changes

9.2 Feature Engineering

9.2.1 What is Feature Engineering?

9.2.2 Types of Feature Engineering

9.2.3 Key Considerations

9.2.4 Feature Importance

9.3 Data Transformation

9.3.1 Why Data Transformation?

9.3.2 Types of Data Transformation

9.3.3 Inverse Transformation

Practical Exercises: Chapter 9

Exercise 9.1: Data Cleaning

Exercise 9.2: Feature Engineering

Exercise 9.3: Data Transformation

Chapter 9 Conclusion

Chapter 10: Visual Exploratory Data Analysis

10.1 Univariate Analysis

10.1.1 Histograms

10.1.2 Box Plots.

10.1.3 Count Plots for Categorical Data.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

1-83620-906-1