Book Image

The Data Science Workshop - Second Edition

By : Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare
5 (1)
Book Image

The Data Science Workshop - Second Edition

5 (1)
By: Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare

Overview of this book

Where there’s data, there’s insight. With so much data being generated, there is immense scope to extract meaningful information that’ll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you’ll open new career paths and opportunities. The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You’ll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you’ll get hands-on with approaches such as grid search and random search. Next, you’ll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the book demonstrates how to use the automated feature engineering tool. You’ll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch. By the end of this book, you’ll have the skills to start working on data science projects confidently. By the end of this book, you’ll have the skills to start working on data science projects confidently.
Table of Contents (16 chapters)
Preface
12
12. Feature Engineering

Visualizing Your Data

In the previous section, we saw how to explore a new dataset and calculate some simple descriptive statistics. These measures helped summarize the dataset into interpretable metrics, such as the average or maximum values. Now it is time to dive even deeper and get a more granular view of each column using data visualization.

In a data science project, data visualization can be used either for data analysis or communicating gained insights. Presenting results in a visual way that stakeholders can easily understand and interpret them in is definitely a must-have skill for any good data scientist.

However, in this chapter, we will be focusing on using data visualization for analyzing data. Most people tend to interpret information more easily on a graph than reading written information. For example, when looking at the following descriptive statistics and the scatter plot for the same variable, which one do you think is easier to interpret? Let's take...