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Full Description
Gain useful insights from your data using popular data science tools
Key Features
A one-stop guide to Python libraries such as pandas and NumPy
Comprehensive coverage of data science operations such as data cleaning and data manipulation
Choose scalable learning algorithms for your data science tasks
Book DescriptionFully 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
What you will learn
Set up your data science toolbox on Windows, Mac, and Linux
Use the core machine learning methods offered by the scikit-learn library
Manipulate, fix, and explore data to solve data science problems
Learn advanced explorative and manipulative techniques to solve data operations
Optimize your machine learning models for optimized performance
Explore and cluster graphs, taking advantage of interconnections and links in your data
Who this book is forIf you're a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.
Contents
Table of Contents
First Steps
Data Munging
The Data Pipeline
Machine Learning
Visualization, Insights, and Results
Social Network Analysis
Deep Learning Beyond the Basics
Spark for Big Data
Appendix A: Strengthen Your Python Foundations
