Designed with beginners in mind, this workshop helps you make the most of Python libraries and the Jupyter Notebooks functionality to understand how data science can be applied to solve real-world data problems.

Key Features

• Gain useful insights into data science and machine learning
• Explore the different functionalities and features of a Jupyter Notebook
• Discover how Python libraries are used with Jupyter for data analysis

Book Description

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. Youll 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, youll start by getting to grips with Jupyter functionality and features. Youll 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, youll train classification models using sci-kit learn, and assess model performance using advanced validation techniques. Towards the end, youll 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, youll be prepared to progress from being a beginner to taking your skills to the next level by 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

Who this book is for

If you are an aspiring data scientist who wants to build a career in data science or a developer who wants to explore the applications of data science from scratch and analyze data in Jupyter using Python libraries, then this book is for you. Although a brief understanding of Python programming and machine learning is recommended to help you grasp the topics covered in the book more quickly, it is not mandatory.