CompTIA Data+ Study Guide

Exam DA0-001

Mike Chapple, Sharif Nijim

• Editorial: Sybex
• Año: 2022
• ISBN:
  9781119845263
• Edición: 1
• Categoría: Computer Science
• Categoría: Certification Guides in Computer Science

Build a solid foundation in data analysis skills and pursue a coveted Data+ certification with this intuitive study guide

CompTIA Data+ Study Guide: Exam DA0-001 delivers easily accessible and actionable instruction for achieving data analysis competencies required for the job and on the CompTIA Data+ certification exam. You'll learn to collect, analyze, and report on various types of commonly used data, transforming raw data into usable information for stakeholders and decision makers.

With comprehensive coverage of data concepts and environments, data mining, data analysis, visualization, and data governance, quality, and controls, this Study Guide offers:

• All the information necessary to succeed on the exam for a widely accepted, entry-level credential that unlocks lucrative new data analytics and data science career opportunities
• 100% coverage of objectives for the NEW CompTIA Data+ exam
• Access to the Sybex online learning resources, with review questions, full-length practice exam, hundreds of electronic flashcards, and a glossary of key terms

Ideal for anyone seeking a new career in data analysis, to improve their current data science skills, or hoping to achieve the coveted CompTIA Data+ certification credential, CompTIA Data+ Study Guide: Exam DA0-001 provides an invaluable head start tobeginningor accelerating a career as an in-demand data analyst.

Chapter 1
Today's Data Analyst

Analytics is at the heart of modern business. Virtually every organization collects large quantities of data about its customers, products, employees, and service offerings. Managers naturally seek to analyze that data and harness the information it contains to improve the efficiency, effectiveness, and profitability of their work.

Data analysts are the professionals who possess the skills and knowledge required to perform this vital work. They understand how the organization can acquire, clean, and transform data to meet the organization's needs. They are able to take that collected information and analyze it using the techniques of statistics and machine learning. They may then create powerful visualizations that display this data to business leaders, managers, and other stakeholders.

Welcome to the World of Analytics

We are fortunate to live in the Golden Age of Analytics. Businesses around the world recognize the vital nature of data to their work and are investing heavily in analytics programs designed to give them a competitive advantage. Organizations have been collecting this data for years, and many of the statistical tools and techniques used in analytics work date back decades. But if that's the case, why are we just now in the early years of this Golden Age? Figure 1.1 shows the three major pillars that have come together at this moment to allow analytics programs to thrive: data, storage, and computing power.

Data

The amount of data the modern world generates on a daily basis is staggering. From the organized tables of spreadsheets to the storage of photos, video, and audio recordings, modern businesses create an almost overwhelming avalanche of data that is ripe for use in analytics programs.

Let's try to quantify the amount of data that exists in the world. We'll begin with an estimate made by Google's then-CEO Eric Schmidt in 2010. At a technology conference, Schmidt estimated that the sum total of all of the stored knowledge created by the world at that point in time was approximately 5 exabytes. To give that a little perspective, the file containing the text of this chapter is around 100 kilobytes. So, Schmidt's estimate is that the world in 2010 had total knowledge that is about the size of 50,000,000,000,000 (that's 50 trillion!) copies of this book chapter. That's a staggering number, but it's only the beginning of our journey.

FIGURE 1.1 Analytics is made possible by modern data, storage, and computing capabilities.

Now fast-forward just two years to 2012. In that year, researchers estimated that the total amount of stored data in the world had grown to 1,000 exabytes (or one zettabyte). Remember, Schmidt's estimate of 5 exabytes was made only two years earlier. In just two years, the total amount of stored data in the world grew by a factor of 200! But we're still not finished!

In the year 2020, IDC estimates that the world created 59 zettabytes (or 59,000 exabytes) of new information. Compare that to Schmidt's estimate of the world having a total of 5 exabytes of stored information in 2010. If you do the math, you'll discover that this means that on any given day in the modern era, the world generates an amount of brand-new data that is approximately 32 times the sum total of all information created from the dawn of civilization until 2010! Now, that is a staggering amount of data!

From an analytics perspective, this trove of data is a gold mine of untapped potential.

Storage

The second key trend driving the growth of analytics programs is the increased availability of storage at rapidly decreasing costs. Table 1.1 shows the cost of storing a gigabyte of data in different years using magnetic hard drives.

TABLE 1.1 Gigabyte storage costs over time

Year	Cost per GB
1985	$169,900
1990	$53,940
1995	$799
2000	$17.50
2005	$0.62
2010	$0.19
2015	$0.03
2020	$0.01

Figure 1.2 shows the same data plotted as a line graph on a logarithmic scale. This visualization clearly demonstrates the fact that storage costs have plummeted to the point where storage is almost free and businesses can afford to retain data for analysis in ways that they never have before.

Computing Power

In 1975, Gordon Moore, one of the co-founders of Intel Corporation, made a prediction that computing technology would continue to advance so quickly that manufacturers would be able to double the number of components placed on an integrated circuit every two years. Remarkably, that prediction has stood the test of time and remains accurate today.

Commonly referred to as Moore's Law, this prediction is often loosely interpreted to mean that we will double the amount of computing power on a single devi...