Alex Galea - The Applied Data Science Workshop - Second Edition: Get started with the applications of data science and techniques to explore and assess data effectively

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 getting to grips with Jupyter functionality and features. You'll use Python libraries like scikit-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 scikit-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, Second Edition, you'll 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.

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.

Chapter 1, Introduction to Jupyter Notebooks, will get you started by explaining how to use the Jupyter Notebook and JupyterLab platforms. After going over the basics, we will discuss some fantastic features of Jupyter, which include tab completion, magic functions, and new additions to the JupyterLab interface. Finally, we will look at the Python libraries we'll be using in this book, such as pandas, seaborn, and scikit-learn.

Chapter 2, Data Exploration with Jupyter, is focused on exploratory analysis in a live Jupyter Notebook environment. Here, you will use visualizations such as scatter plots, histograms, and violin plots to deepen your understanding of the data. We will also walk through some simple modeling problems with scikit-learn.

Chapter 3, Preparing Data for Predictive Modeling, will enable you to plan a machine learning strategy and assess whether or not data is suitable for modeling. In addition to this, you'll learn about the process involved in preparing data for machine learning algorithms, and apply this process to sample datasets using pandas.

Chapter 4,Training Classification Models, will introduce classification algorithms such as SVMs, KNNs, and Random Forests. Using a real-world Human Resources analytics dataset, we'll train and compare models that predict whether an employee will leave their company. You'll learn about training models with scikit-learn and use decision boundary plots to see what overfitting looks like.

Chapter 5, Model Validation and Optimization, will give you hands-on experience with model testing and model selection concepts, including k-fold cross-validation and validation curves. Using these techniques, you'll learn how to optimize model parameters and compare model performance reliably. You will also learn how to implement dimensionality reduction techniques such as Principal Component Analysis (PCA).