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Data Engineering Foundations

15 chapters and 69 canonical sections synced from the Cuantum content database.

Author
Cuantum Tech.
Chapters
15
Reading time
~ 16h
Level
Professional
Language
English
Edition
2025
Your progress0%
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Chapters & sections

15 chapters - 69 sections

Chapter 1: Introduction: Moving Beyond the Basics

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1.1 Overview of Intermediate Data Analysis37m1.2 How this Book Builds on Foundations16m1.3 Tools: Pandas, NumPy, Scikit-learn in Action28m1.4 Practical Exercises for Chapter 1: Introduction: Moving Beyond the Basics6m1.5 What Could Go Wrong?6m1.6 Chapter 1 Summary: Moving Beyond the Basics3m

Chapter 2: Optimizing Data Workflows

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2.1 Advanced Data Manipulation with Pandas48m2.2 Enhancing Performance with NumPy Arrays26m2.3 Combining Tools for Efficient Analysis27m2.4 Practical Exercises for Chapter 2: Optimizing Data Workflows7m2.5 What Could Go Wrong?6m2.6 Chapter 2 Summary: Optimizing Data Workflows3m

Quiz Part 1: Setting the Stage for Advanced Analysis

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Questions5mAnswers2m

Project 1: House Price Prediction with Feature Engineering

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1. Feature Exploration and Cleaning11m2. Feature Engineering for House Price Prediction21m3. Building and Evaluating the Predictive Model16m4. Finalizing the House Price Prediction Project5mConclusion3m

Chapter 3: The Role of Feature Engineering in Machine Learning

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3.1 Why Feature Engineering Matters19m3.2 Examples of Impactful Feature Engineering30m3.3 Practical Exercises for Chapter 35m3.4 What Could Go Wrong?5m3.5 Chapter 3 Summary3m

Chapter 4: Techniques for Handling Missing Data

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4.1 Advanced Imputation Techniques29m4.2 Dealing with Missing Data in Large Datasets35m4.3 Practical Exercises for Chapter 45m4.4 What Could Go Wrong?7m4.5 Chapter 4 Summary3m

Chapter 5: Transforming and Scaling Features

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5.1 Scaling and Normalization: Best Practices28m5.2 Log, Square Root, and Other Non-linear Transformations33m5.3 Practical Exercises for Chapter 55m5.4 What Could Go Wrong?6m5.5 Chapter 5 Summary3m

Chapter 6: Encoding Categorical Variables

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6.1 One-Hot Encoding Revisited: Tips and Tricks32m6.2 Advanced Encoding Methods: Target, Frequency, and Ordinal Encoding27m6.3 Practical Exercises for Chapter 64m6.4 What Could Go Wrong?7m6.5 Chapter 6 Summary3m

Chapter 7: Feature Creation & Interaction Terms

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7.1 Creating New Features from Existing Data36m7.2 Feature Interactions: Polynomial, Cross-features, and More30m7.3 Practical Exercises for Chapter 75m7.4 What Could Go Wrong?7m7.5 Chapter 7 Summary4m

Quiz Part 2: Feature Engineering for Powerful Models

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Questions4mAnswers1m

Project 2: Time Series Forecasting with Feature Engineering

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1.1 Introduction to Time Series Forecasting with Feature Engineering12m1.2 Rolling Window Features for Capturing Trends and Seasonality17m1.3 Detrending and Dealing with Seasonality in Time Series20m1.4 Applying Machine Learning Models for Time Series Forecasting21m1.5 Hyperparameter Tuning for Time Series Models16m1.6 Wrapping Up the Time Series Forecasting Project8m

Chapter 8: Advanced Data Cleaning Techniques

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8.1 Identifying Outliers and Handling Extreme Values47m8.2 Correcting Data Anomalies with Pandas32m8.3 Practical Exercises for Chapter 84m8.4 What Could Go Wrong?5m8.5 Chapter 8 Summary3m

Chapter 9: Time Series Data: Special Considerations

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9.1 Working with Date/Time Features29m9.2 Creating Lagged and Rolling Features31m9.3 Practical Exercises for Chapter 94m9.4 What Could Go Wrong?5m9.5 Chapter 9 Summary3m

Chapter 10: Dimensionality Reduction

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10.1 Principal Component Analysis (PCA)25m10.2 Feature Selection Techniques26m10.3 Practical Exercises for Chapter 105m10.4 What Could Go Wrong?6m10.5 Chapter 10 Summary3m

Quiz Part 3: Data Cleaning and Preprocessing

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Questions3mAnswers1m