The Data Science Workshop
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The Data Science Workshop
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Overview of this book
You already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on building up your practical skills so that you can understand how to develop simple machine learning models in Python or even build an advanced model for detecting potential bank frauds with effective modern data science. You'll learn from real examples that lead to real results.
Throughout The Data Science Workshop, you'll take an engaging step-by-step approach to understanding data science. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend training a model using sci-kit learn. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding.
Every physical print copy of The Data Science Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your data science book.
Fast-paced and direct, The Data Science Workshop is the ideal companion for data science beginners. You'll learn about machine learning algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.
Table of Contents (18 chapters)
Preface
1. Introduction to Data Science in Python
Free Chapter
2. Regression
3. Binary Classification
4. Multiclass Classification with RandomForest
5. Performing Your First Cluster Analysis
6. How to Assess Performance
7. The Generalization of Machine Learning Models
8. Hyperparameter Tuning
9. Interpreting a Machine Learning Model
10. Analyzing a Dataset
11. Data Preparation
12. Feature Engineering
13. Imbalanced Datasets
14. Dimensionality Reduction
15. Ensemble Learning
16. Machine Learning Pipelines
17. Automated Feature Engineering
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