The Beginner S Guide To Data Science
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The Beginner’s Guide to Data Science
Author: Jason Brownlee
language: en
Publisher: Machine Learning Mastery
Release Date: 2024-03-27
In today’s data-driven world, businesses and industries constantly seek insights to drive innovation, enhance decision-making, and stay ahead of the curve. Data science is not just a skill but a superpower that empowers you to extract meaningful patterns and knowledge from raw data, unlocking limitless opportunities. The theme of data science is to tell a story from data. There are many tools to help you build a narrative, but you should be focused on something other than the tool since the end is more important than the means. If you are a beginner, how should you embark on data science? You can learn many models, read many examples, and eventually gain the right mindset to handle a data science project. You can also learn the data science mindset first and then learn models that fit the picture when needed. The Beginner’s Guide to Data Science is your gateway to learn the data science mindset from examples. This ebook is written in the engaging and approachable style you are familiar with from Machine Learning Mastery. Discover exactly how to start and what the thought process is in dealing with a data science project.
The Beginner's Guide to Data Science
This book discusses the principles and practical applications of data science, addressing key topics including data wrangling, statistics, machine learning, data visualization, natural language processing 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. 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.
A Beginner's Guide To DATA SCIENCE
This book is designed for aspiring data scientists who want to start their careers in data science, even if they don't have coding skills. It provides a comprehensive introduction to the foundations of data science and its applications, using simple language that is easy for beginners to understand. No technical expertise is required to master the material in this book. It is an ideal resource for anyone looking to learn about data science in an accessible and straightforward way. Key features include: Introduction to data science History of data science Data science life-cycle Data science tools and technologies Data science methodology Data science models Developing data science business strategy Managing data science projects Becoming a data scientist, data engineer etc. Big data Data Mining Artificial intelligence Machine learning Deep learning Neural networks Mathematical analysis Statistical modelling Understanding the fundamentals of data science programming languages Database structures and principles Robotic Process Automation Data science acronyms You need to know And a lot more.