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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
~ 14h
Level
Professional
Language
English
Edition
2025
Your progress0%
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Chapters & sections

15 chapters - 69 sections
Chapter 01

Chapter 1: Introduction: Moving Beyond the Basics

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11.1 Overview of Intermediate Data Analysis12m21.2 How this Book Builds on Foundations12m31.3 Tools: Pandas, NumPy, Scikit-learn in Action12m41.4 Practical Exercises for Chapter 1: Introduction: Moving Beyond the Basics12m51.5 What Could Go Wrong?12m61.6 Chapter 1 Summary: Moving Beyond the Basics12m
Chapter 02

Chapter 2: Optimizing Data Workflows

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12.1 Advanced Data Manipulation with Pandas12m22.2 Enhancing Performance with NumPy Arrays12m32.3 Combining Tools for Efficient Analysis12m42.4 Practical Exercises for Chapter 2: Optimizing Data Workflows12m52.5 What Could Go Wrong?12m62.6 Chapter 2 Summary: Optimizing Data Workflows12m
Chapter 03

Quiz Part 1: Setting the Stage for Advanced Analysis

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2Answers12m1Questions12m
Chapter 04

Project 1: House Price Prediction with Feature Engineering

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11. Feature Exploration and Cleaning12m22. Feature Engineering for House Price Prediction12m33. Building and Evaluating the Predictive Model12m44. Finalizing the House Price Prediction Project12m5Conclusion12m
Chapter 05

Chapter 3: The Role of Feature Engineering in Machine Learning

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13.1 Why Feature Engineering Matters12m23.2 Examples of Impactful Feature Engineering12m33.3 Practical Exercises for Chapter 312m43.4 What Could Go Wrong?12m53.5 Chapter 3 Summary12m
Chapter 06

Chapter 4: Techniques for Handling Missing Data

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14.1 Advanced Imputation Techniques12m24.2 Dealing with Missing Data in Large Datasets12m34.3 Practical Exercises for Chapter 412m44.4 What Could Go Wrong?12m54.5 Chapter 4 Summary12m
Chapter 07

Chapter 5: Transforming and Scaling Features

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15.1 Scaling and Normalization: Best Practices12m25.2 Log, Square Root, and Other Non-linear Transformations12m35.3 Practical Exercises for Chapter 512m45.4 What Could Go Wrong?12m55.5 Chapter 5 Summary12m
Chapter 08

Chapter 6: Encoding Categorical Variables

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16.1 One-Hot Encoding Revisited: Tips and Tricks12m26.2 Advanced Encoding Methods: Target, Frequency, and Ordinal Encoding12m36.3 Practical Exercises for Chapter 612m46.4 What Could Go Wrong?12m56.5 Chapter 6 Summary12m
Chapter 09

Chapter 7: Feature Creation & Interaction Terms

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17.1 Creating New Features from Existing Data12m27.2 Feature Interactions: Polynomial, Cross-features, and More12m37.3 Practical Exercises for Chapter 712m47.4 What Could Go Wrong?12m57.5 Chapter 7 Summary12m
Chapter 10

Quiz Part 2: Feature Engineering for Powerful Models

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2Answers12m1Questions12m
Chapter 11

Project 2: Time Series Forecasting with Feature Engineering

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11.1 Introduction to Time Series Forecasting with Feature Engineering12m21.2 Rolling Window Features for Capturing Trends and Seasonality12m31.3 Detrending and Dealing with Seasonality in Time Series12m41.4 Applying Machine Learning Models for Time Series Forecasting12m51.5 Hyperparameter Tuning for Time Series Models12m61.6 Wrapping Up the Time Series Forecasting Project12m
Chapter 12

Chapter 8: Advanced Data Cleaning Techniques

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18.1 Identifying Outliers and Handling Extreme Values12m28.2 Correcting Data Anomalies with Pandas12m38.3 Practical Exercises for Chapter 812m48.4 What Could Go Wrong?12m58.5 Chapter 8 Summary12m
Chapter 13

Chapter 9: Time Series Data: Special Considerations

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19.1 Working with Date/Time Features12m29.2 Creating Lagged and Rolling Features12m39.3 Practical Exercises for Chapter 912m49.4 What Could Go Wrong?12m59.5 Chapter 9 Summary12m
Chapter 14

Chapter 10: Dimensionality Reduction

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110.1 Principal Component Analysis (PCA)12m210.2 Feature Selection Techniques12m310.3 Practical Exercises for Chapter 1012m410.4 What Could Go Wrong?12m510.5 Chapter 10 Summary12m
Chapter 15

Quiz Part 3: Data Cleaning and Preprocessing

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2Answers12m1Questions12m