Название: The Beginner's Guide to Data ScienceАвтор: Robert Ball, Brian Rague
Издательство: Springer
Год: 2022
Страниц: 251
Язык: английский
Формат: pdf (true), epub
Размер: 26.2 MB
This book discusses the principles and practical applications of Data Science, addressing key topics including data wrangling, statistics, Machine Learning (ML), data visualization, natural language processing (NLP) and time series analysis. Detailed investigations of techniques used in the implementation of recommendation engines and the proper selection of metrics for distance-based analysis are also covered.
Utilizing numerous comprehensive code examples, figures, and tables to help clarify and illuminate essential Data Science topics, the authors provide an extensive treatment and analysis of real-world questions, focusing especially on the task of determining and assessing answers to these questions as expeditiously and precisely as possible. This book addresses the challenges related to uncovering the actionable insights in “Big Data,” leveraging database and data collection tools such as web scraping and text identification.
Given a well-posed question, the topics in this book provide concise descriptions of the techniques and tools used in Data Science to generate viable answers. Data Science as a discipline leverages strategies and technologies derived from computer science, statistics, and various business domains. Data science is the progeny of these fields but does not belong to any particular one. The data scientist is an inter-disciplinarian, utilizing the core knowledge from several areas to make assessments of current data for the purposes of determining future directions in research and analysis. This book is intended for the beginning application-oriented data scientist who wishes to learn the essential methods necessary to extract meaning from numbers.
There are many Python example programs used throughout this book. We have done our best to make these examples readily available at a separate downloadable location. In addition, almost every image used in this book was created by Python code. For these plots and diagrams, the source code designed to create the image is cited and available. The current download location for the code included in this book is the URL: https:// github. com/ robertball/ Beginners-Guide-Data-Science.
There are three main reasons for posting the Python code at this separate location:
• There are many instances when having the code occupy unnecessary space in the book is neither meaningful nor instructive, so we have maintained the code separately to be perused, executed, and evaluated at your leisure.
• Python and its various associated libraries change. As a result, there may come a time when certain sample programs in the book no longer run efficiently or successfully on modern computers. Under these special circumstances we will respond by updating the relevant programs so they remain functional and informative. We cannot easily update the content of this book, but we can easily revise and refine downloadable code.
• Copying directly from this book to another resource such as a development environment often inadvertently transfers various formatting issues. Access to a download of the original code can avoid these formatting and program structure issues altogether.
This book is organized as 11 chapters, structured as independent treatments of the following crucial Data Science topics:
Data gathering and acquisition techniques including data creation
Managing, transforming, and organizing data to ultimately package the information into an accessible format ready for analysis
Fundamentals of descriptive statistics intended to summarize and aggregate data into a few concise but meaningful measurements
Inferential statistics that allow us to infer (or generalize) trends about the larger population based only on the sample portion collected and recorded
Metrics that measure some quantity such as distance, similarity, or error and which are especially useful when comparing one or more data observations
Recommendation engines representing a set of algorithms designed to predict (or recommend) a particular product, service, or other item of interest a user or customer wishes to buy or utilize in some manner
Machine Learning implementations and associated algorithms, comprising core data science technologies with many practical applications, especially predictive analytics
Natural Language Processing, which expedites the parsing and comprehension of written and spoken language in an effective and accurate manner
Time series analysis, techniques to examine and generate forecasts about the progress and evolution of data over time
Data Science provides the methodology and tools to accurately interpret an increasing volume of incoming information in order to discern patterns, evaluate trends, and make the right decisions. The results of data science analysis provide real world answers to real world questions. Professionals working on Data Science and business intelligence projects as well as advanced-level students and researchers focused on Data Science, Computer Science, business and mathematics programs will benefit from this book.
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