Data Engineering Foundations
This book provides an essential guide to the building blocks of data engineering and analysis. This book introduces readers to the fundamental tools and techniques necessary to manipulate, process, and analyze large datasets effectively using Python’s most powerful libraries. It’s designed to give practitioners a solid foundation, bridging the gap between theoretical knowledge and practical application in real-world settings.
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Mastering Pandas for Data Manipulation
Pandas is an indispensable tool for data manipulation and analysis, and mastering it is essential for any aspiring data professional. "Data Engineering Foundations" offers an in-depth exploration of Pandas, starting from basic data structures like Series and DataFrames to more complex data operations essential for real-time analysis.
This section covers crucial techniques such as data indexing, handling missing data, merging and concatenating datasets, and pivoting tables for better data aggregation. It also delves into time-series analysis, showcasing how Pandas can be utilized to deal with chronological data effectively—essential for sectors like finance and logistics.
Beyond functionality, the book provides insights into optimizing performance when working with large datasets, ensuring readers know how to handle data efficiently in Pandas. Practical exercises and real-world examples throughout the chapter reinforce learning and demonstrate the application of each technique in a variety of business contexts.
Numerical Computing with NumPy
NumPy is at the core of numerical computing in Python, and this book ensures you understand how to harness its full potential. "Data Engineering Foundations" walks you through the fundamental aspects of NumPy, including array creation, mathematical operations, and handling multidimensional data for complex computations.
Learn about vectorization for performance optimization, broadcasting for efficient arithmetic operations, and the use of universal functions for array processing. This section also introduces techniques for statistical analysis and linear algebra, which are pivotal for machine learning and scientific computing.
With detailed case studies and step-by-step guides, you will learn not only to perform numerical tasks but also to optimize your workflows for better performance and accuracy. This knowledge is vital for any professional dealing with large quantities and varieties of numerical data.
"Data Engineering Foundations" goes beyond the realms of Pandas and NumPy, offering an in-depth exploration of Scikit-Learn for machine learning applications. This comprehensive section of the book delves into the intricacies of data pre-processing techniques, guiding readers through the nuanced process of feature selection and transformation. It provides a thorough examination of Scikit-Learn's diverse array of algorithms, equipping readers with the tools to construct robust predictive models.
The book meticulously bridges the gap between data manipulation, numerical computing, and machine learning, presenting a seamless integration of these crucial components. By doing so, it offers readers a panoramic perspective of the data science and engineering landscape, illuminating the interconnections between various facets of the field.
This holistic approach enables readers to develop a nuanced understanding of how different elements of data engineering and analysis come together to form a cohesive whole, thereby enhancing their ability to tackle complex, real-world data challenges with confidence and expertise.
Table of contents
Chapter 1: Introduction: Moving Beyond the Basics
1.1 Overview of Intermediate Data Analysis
1.2 How this Book Builds on Foundations
1.3 Tools: Pandas, NumPy, Scikit-learn in Action
1.4 Practical Exercises for Chapter 1: Introduction: Moving Beyond the Basics
1.5 What Could Go Wrong?
Chapter 2: Optimizing Data Workflows
2.1 Advanced Data Manipulation with Pandas
2.2 Enhancing Performance with NumPy Arrays
2.3 Combining Tools for Efficient Analysis
2.4 Practical Exercises for Chapter 2: Optimizing Data Workflows
2.5 What Could Go Wrong?
Quiz Part 1: Setting the Stage for Advanced Analysis
Questions
Answers
Project 1: House Price Prediction with Feature Engineering
1. Feature Exploration and Cleaning
2. Feature Engineering for House Price Prediction
3. Building and Evaluating the Predictive Model
4. Finalizing the House Price Prediction Project
Conclusion
Chapter 3: The Role of Feature Engineering in Machine Learning
3.1 Why Feature Engineering Matters
3.2 Examples of Impactful Feature Engineering
3.3 Practical Exercises for Chapter 3
3.4 What Could Go Wrong?
3.5 Chapter 3 Summary
Chapter 4: Techniques for Handling Missing Data
4.1 Advanced Imputation Techniques
4.2 Dealing with Missing Data in Large Datasets
4.3 Practical Exercises for Chapter 4
4.4 What Could Go Wrong?
4.5 Chapter 4 Summary
Chapter 5: Transforming and Scaling Features
5.1 Scaling and Normalization: Best Practices
5.2 Log, Square Root, and Other Non-linear Transformations
5.3 Practical Exercises for Chapter 5
5.4 What Could Go Wrong?
5.5 Chapter 5 Summary
Chapter 6: Encoding Categorical Variables
6.1 One-Hot Encoding Revisited: Tips and Tricks
6.2 Advanced Encoding Methods: Target, Frequency, and Ordinal Encoding
6.3 Practical Exercises for Chapter 6
6.4 What Could Go Wrong?
6.5 Chapter 6 Summary
Chapter 7: Feature Creation & Interaction Terms
7.1 Creating New Features from Existing Data
7.2 Feature Interactions: Polynomial, Cross-features, and More
7.3 Practical Exercises for Chapter 7
7.4 What Could Go Wrong?
7.5 Chapter 7 Summary
Quiz Part 2: Feature Engineering for Powerful Models
Questions
Answers
Project 2: Time Series Forecasting with Feature Engineering
1.1 Introduction to Time Series Forecasting with Feature Engineering
1.2 Rolling Window Features for Capturing Trends and Seasonality
1.3 Detrending and Dealing with Seasonality in Time Series
1.4 Applying Machine Learning Models for Time Series Forecasting
1.5 Hyperparameter Tuning for Time Series Models
Chapter 8: Advanced Data Cleaning Techniques
8.1 Identifying Outliers and Handling Extreme Values
8.2 Correcting Data Anomalies with Pandas
8.3 Practical Exercises for Chapter 8
8.4 What Could Go Wrong?
8.5 Chapter 8 Summary
Chapter 9: Time Series Data: Special Considerations
9.1 Working with Date/Time Features
9.2 Creating Lagged and Rolling Features
9.3 Practical Exercises for Chapter 9
9.4 What Could Go Wrong?
9.5 Chapter 9 Summary
Chapter 10: Dimensionality Reduction
10.1 Principal Component Analysis (PCA)
10.2 Feature Selection Techniques
10.3 Practical Exercises for Chapter 10
10.4 What Could Go Wrong?
10.5 Chapter 10 Summary
Quiz Part 3: Data Cleaning and Preprocessing
Questions
Answers
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.
The book breaks down complex data manipulation and analysis techniques into digestible, easy-to-understand segments. The chapters on Pandas and NumPy are particularly illuminating, offering a treasure trove of insights into data indexing, handling missing data, and performance optimization that are rarely covered with such depth in other texts. The real-world examples provided are directly applicable to the challenges I face daily, making this an invaluable resource.
What sets this book apart is its practical approach—each chapter is laden with examples and exercises that bridge the gap between theory and practice. From manipulating data frames in Pandas to performing complex numerical computations with NumPy, and finally to building predictive models with Scikit-Learn, this book has it all. It's written in a way that both beginners and experienced professionals can benefit from it, making complex concepts accessible to all. This book has not only boosted my confidence in data analysis but also enriched my day-to-day work by improving the quality and efficiency of my outputs.
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