finelybook
The Applied Data Science Workshop: Get started with the applications of data science and techniques to explore and assess data effectively,2nd Edition
by 作者: Alex Galea
pages 页数: 352 pages
Publisher Finelybook 出版社: Packt Publishing; 2nd edition edition (22 July 2020)
Language 语言: English
ISBN-10 书号: 1800202504
ISBN-13 书号: 9781800202504
Book Description
Designed with beginners in mind,this workshop helps you make the most of Python libraries and the Jupyter Notebook’s functionality to understand how data science can be applied to solve real-world data problems.
From banking and manufacturing through to education and entertainment,using data science for business has revolutionized almost every sector in the modern world. It has an important role to play in everything from app development to network security.
Taking an interactive approach to learning the fundamentals,this book is ideal for beginners. You’ll learn all the best practices and techniques for applying data science in the context of real-world scenarios and examples.
Starting with an introduction to data science and machine learning,you’ll start by 作者: getting to grips with Jupyter functionality and features. You’ll use Python libraries like sci-kit learn,pandas,Matplotlib,and Seaborn to perform data analysis and data preprocessing on real-world datasets from within your own Jupyter environment. Progressing through the chapters,you’ll train classification models using sci-kit learn,and assess model performance using advanced validation techniques. Towards the end,you’ll use Jupyter Notebooks to document your research,build stakeholder reports,and even analyze web performance data.
By the end of The Applied Data Science Workshop,you’ll be prepared to progress from being a beginner to taking your skills to the next level by 作者: confidently applying data science techniques and tools to real-world projects.
What you will learn
Understand the key opportunities and challenges in data science
Use Jupyter for data science tasks such as data analysis and modeling
Run exploratory data analysis within a Jupyter Notebook
Visualize data with pairwise scatter plots and segmented distribution
Assess model performance with advanced validation techniques
Parse HTML responses and analyze HTTP requests
