Python Data Science Essentials

Description

Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users. 1 First Steps 2 Data Munging 3 The Data Pipeline 4 Machine Learning 5 Visualization, Insights, and Results 6 Social Network Analysis 7 Deep Learning Beyond the Basics 8 Spark for Big Data A Appendix A: Strengthen Your Python Foundations A Appendix B: Other Books You May Enjoy A Appendix C: Index