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Data Engineering Foundations: Core Techniques for Data Analysis with Pandas, NumPy, and Scikit-Learn (Advanced Data Analysis Series Book 1)
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Unlock the Power of Data Engineering with practical applications that equip you with the knowledge and confidence to tackle real-world data challenges.
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Produkt detaljer
- This Book grants Free Access to our e-learning Platform, which includes:✅ Free Repository Code with all code blocks used in this book✅ Access to Free Chapters of all our library of programming published books✅ Free premium customer support✅ Much more...Unlock the Power of Data EngineeringData is everywhere, but only with the right skills can you transform raw data into insights that drive impactful decisions. Data Engineering Foundations: Core Techniques for Data Analysis with Pandas, NumPy, and Scikit-Learn is your comprehensive guide to mastering the essential skills needed to clean, transform, and prepare data for machine learning and analytics. With a focus on practical applications, this book equips you with the knowledge and confidence to take on real-world data challenges.What You’ll LearnData Engineering Foundations is divided into three comprehensive parts, each building on the last to provide a complete understanding of data engineering fundamentals:1. Essential Data Preparation and Manipulation TechniquesData Cleaning: Learn how to identify, handle, and transform missing and inconsistent data, ensuring that your datasets are accurate and reliable.Data Wrangling with Pandas and NumPy: Master core data manipulation techniques, including merging, filtering, aggregating, and reshaping data. With hands-on exercises, you’ll understand how to streamline and simplify complex data tasks using Pandas and NumPy.Efficiency and Performance Optimization: Understand how to handle large datasets efficiently by optimizing performance with NumPy and applying best practices in data manipulation.2. Feature Engineering for Enhanced Model PerformanceFeature Transformation: Explore scaling, normalization, and encoding techniques, each tailored to make data more suitable for machine learning models.Handling Categorical Variables: Discover strategies to manage and encode categorical data, including one-hot encoding, target encoding, and frequency encoding.Advanced Feature Creation: Learn to create meaningful features that capture complex relationships, including polynomial features and interaction terms that boost your model’s predictive power.3. Data Cleaning and Preprocessing for Real-World ProjectsOutlier Detection and Anomaly Handling: Identify and manage outliers to improve data quality and model stability.Dimensionality Reduction: Understand the value of Principal Component Analysis (PCA) and other techniques that streamline high-dimensional data, making it more manageable without sacrificing critical information.Building Reproducible Workflows with Scikit-Learn Pipelines: Automate and structure your data transformation steps using Scikit-Learn’s powerful pipeline functionality, ensuring consistency and reproducibility in data workflows.Hands-On Learning with Real-World ApplicationsEach chapter is packed with practical examples, exercises, and case studies to reinforce your understanding. You’ll work through examples from a variety of industries—such as healthcare, retail, and customer analytics—providing insight into how data engineering techniques apply across fields. With case studies that show the entire data preparation workflow, you’ll see firsthand how the techniques you’ve learned are applied in industry settings.Who This Book is ForIt’s perfect for those who want to:Build robust data preparation skills in Python using Pandas, NumPy, and Scikit-Learn.Understand how to clean, transform, and engineer data for machine learning.Develop reproducible, scalable data workflows for professional data science applications.
| Publisher | Cuantum Technologies |
| Publication date | November 6, 2024 |
| Language | English |
| File size | 3.5 MB |
| Screen Reader | Supported |
| Enhanced typesetting | Enabled |
| X-Ray | Not Enabled |
| Word Wise | Not Enabled |
| Print length | 962 pages |
| ISBN-13 | 979-8895873557 |
| Page Flip | Enabled |
| Book 1 of 2 | Advanced Data Analysis Series |
| Item Weight | 1 lbs (450 grams) |
Hvem passer produktet for?
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Data Analysts
Ideal for analysts wanting to enhance their skills in data manipulation and analysis using popular Python libraries.
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Aspiring Data Engineers
Great resource for those seeking foundational knowledge in data engineering and proficiency in data analysis techniques.
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Students in Data Science
Perfect for students studying data science who need practical knowledge of data tools and analytical techniques.
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Beginners in Programming
Not suitable for those with no prior programming experience as it may overwhelm beginners with technical details.
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AI & Machine Learning Editorial Review
**** "Data Engineering Foundations: Core Techniques for Data Analysis with Pandas, NumPy, and Scikit-Learn" stands out as a vital resource for both beginners and those looking to deepen their understanding of data engineering practices. Many readers have expressed their appreciation for the book's comprehensive and well-structured approach, highlighting how effectively it simplifies complex topics. The use of clear explanations combined with practical examples and hands-on exercises allows readers to grasp difficult concepts without becoming overwhelmed. Feedback suggests that the book does a commendable job of making theoretical knowledge applicable, especially through its focus on real-world scenarios and problem-solving techniques. This practical orientation is invaluable for those working in data-intensive environments, where engineering principles continuously evolve. Moreover, the inclusion of additional resources greatly enhances the learning experience, making it an excellent guide for strengthening data analysis and machine learning skills. Overall, "Data Engineering Foundations" emerges as a highly recommended read, especially for aspiring data analysts and engineers who seek to build a robust foundation in this ever-evolving field. Those in various sectors, from Construction to finance, have noted its relevance, indicating that its benefits extend beyond traditional tech boundaries. **
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Fordeler
- Comprehensive and well-structured content.
- Clear explanations of complex data engineering concepts.
- Practical examples and hands-on exercises.
- Additional resources enhance the learning experience.
- Valuable for both beginners and seasoned professionals.
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Funksjoner og fordeler
- Comprehensive guide to mastering data engineering skills.
- Focuses on practical applications for real-world data challenges.
- Learn essential techniques for data cleaning, transformation, and preparation.
- Gain access to e-learning platform and premium customer support.
- Emphasis on efficiency and performance optimization with large datasets.
- Hands-on exercises for mastering data manipulation using Pandas and NumPy.
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