Big Data for Beginners: Book 1 - An Introduction to the Data Collection, Storage, Data Cleaning and Preprocessing

By Brian Murray

"Big Data for Beginners" is a comprehensive introduction to the world of big data and its various components. In this book, you will learn about the processes involved in collecting, storing, cleaning, and preprocessing large amounts of data.

 

With the rise of the digital age, companies and organizations have access to more data than ever before. However, this data is often unstructured and messy, making it difficult to analyze and draw meaningful insights from it. This is where the process of data cleaning and preprocessing comes in.

 

This book will guide you through the different tools and techniques used to clean and preprocess data, making it easier to analyze and draw insights from. You will also learn about the different types of data storage and the various technologies used to manage large datasets.

 

Whether you are a complete beginner or have some experience working with data, "Big Data for Beginners" is an essential guide to understanding the world of big data and its applications. With clear explanations and practical examples, this book will help you develop the skills and knowledge necessary to navigate the exciting and ever-changing world of big data.

• Language: English
• Publisher: May Reads
• Release date: Apr 29, 2024
• ISBN: 9798224184866

Book preview

Brian Murray

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Please note the information contained within this document is for educational and entertainment purposes only. All effort has been executed to present accurate, up to date, reliable, complete information. No warranties of any kind are declared or implied. Readers acknowledge that the author is not engaging in the rendering of legal, financial, medical, or professional advice. The content within this book has been derived from various sources. Please consult a licensed professional before attempting any techniques outlined in this book.

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Table of Contents

Chapter 1: Introduction to Data

Definition of data

Types of data (structured, unstructured, semi-structured)

The importance of data

Chapter 2: Data Collection and Storage

Techniques for collecting data

Choosing the right storage system

Overview of databases (relational, NoSQL, NewSQL)

Cloud storage and its advantages

Chapter 3: Data Cleaning and Preprocessing

Techniques for cleaning and preparing data

Handling missing values

Dealing with duplicates

Feature scaling and normalization

Data discretization and binning

Chapter 1: Introduction to Data

Definition of data

Data refers to any set of information that can be processed or analyzed to reveal patterns, trends, and insights. It can take various forms, such as numbers, text, images, and sounds. Data is typically collected from various sources, such as sensors, surveys, and transactions, and is used in various applications, such as business intelligence, scientific research, and machine learning. The quality and accuracy of data are crucial for its usefulness in decision-making and other applications.

Types of data (structured, unstructured, semi-structured)

There are three main types of data: structured, unstructured, and semi-structured data.

Structured data: This is data that is organized in a well-defined manner, typically in a tabular format with rows and columns. Structured data is highly organized and can be easily processed, analyzed, and queried using traditional relational database management systems. Examples of structured data include data in a spreadsheet or a database.

Structured data refers to data that has a well-defined schema, with a fixed set of attributes, data types, and relationships between entities. This type of data is typically stored in tables or spreadsheets, and each row represents a unique instance of an entity, while each column represents a specific attribute or feature of that entity.

One of the main advantages of structured data is that it is highly organized, making it easy to process, analyze, and query using traditional relational database management systems such as SQL. Structured data is also easy to visualize using tools like Tableau or Power BI, which can help users quickly gain insights into the data.

Examples of structured data include customer information such as name, address, phone number, and email address stored in a CRM system, financial data such as revenue and expenses stored in an accounting system, and sales data such as product name, quantity sold, and price stored in a sales database.

Structured data is commonly used in a wide range of industries, including finance, healthcare, retail, and manufacturing, where it is used to manage and analyze large amounts of data efficiently. With the increasing popularity of data analytics and machine learning, structured data is becoming even more important as it provides a foundation for many of these advanced data analysis techniques.

Unstructured data: This is data that is not organized in a predefined way, making it difficult to analyze using traditional methods. Unstructured data can come in many different forms, including text, audio, video, and images. Examples of unstructured data include emails, social media posts, customer feedback, and video files.

Unstructured data refers to any type of data that lacks a specific format or structure. Unlike structured data, it cannot be easily organized into tables, columns, or rows. Unstructured data is typically more complex and difficult to process than structured data, making it more challenging to analyze using traditional data management techniques.

Examples of unstructured data include:

- Text: This can include anything from email messages and social media posts to legal documents and research reports.

- Audio: This includes speech recordings, phone calls, and voicemail messages.

- Video: This includes movies, TV shows, and YouTube videos.

Images: This includes photographs, drawings, and diagrams.

- Social media: This includes posts, comments, and messages on social media platforms like Twitter, Facebook, and Instagram.

- Customer feedback: This includes comments