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E-grāmata: CompTIA Data+ DA0-001 Exam Cram

  • Formāts: 528 pages
  • Izdošanas datums: 03-Jan-2023
  • Izdevniecība: Pearson IT Certification
  • Valoda: eng
  • ISBN-13: 9780137637416
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  • Formāts: 528 pages
  • Izdošanas datums: 03-Jan-2023
  • Izdevniecība: Pearson IT Certification
  • Valoda: eng
  • ISBN-13: 9780137637416
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Businesses now more than ever are relying on data analysis to maximize sales and understand consumer trends. Regardless of your role or job function, just about everyone in today's climate is expected to have some skills in this area. As the field of data analysis becomes more integral to business strategy it is imperative to gain the knowledge that will set yourself apart from the competition and benefit your career prospects. CompTIA Data+ DA0-001 Exam Cram is designed to prepare candidates for success on the exam, using the proven Exam Cram method of study. This latest addition to the Exam Cram series is geared toward early-career business and data analysts, and as with all Exam Cram books, includes:

  • Chapters that map directly to the exam objectives Comprehensive foundational learning on all topics covered on the exam
  • An extensive collection of practice questions
  • Access to the Pearson Test Prep practice test software that provides real-time practice and feedback, online or offline
  • Flash cards to drill on key concepts
  • The Cram Sheet tear-out card including tips, acronyms, and memory joggers not available anywhere else;mdash;perfect for last-minute study

All the topics in the exam are covered here, including: mining data, manipulating data, applying basic statistical methods, and analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle. This book is in complete alignment with the CompTIA Data+ certification exam blueprint. It will take you systematically through the five major domains, and the subtopics within those domains, which constitute the ComptTIA Data+ exam. It is an invaluable guide to preparing to pass the CompTIA Data+ exam.

Introduction xx
Chapter 1 Understanding Databases and Data Warehouses
1(30)
Databases and Database Management Systems
2(13)
Database Management System (DBMS)
4(3)
Relational Database
7(2)
Non-relational Database
9(6)
Data Warehouses and Data Lakes
15(9)
Data Warehouses
16(4)
Data Lakes
20(4)
OLTP and OLAP
24(6)
Online Transactional Processing (OLTP)
24(2)
Online Analytical Processing (OLAP)
26(4)
What Next?
30(1)
Chapter 2 Understanding Database Schemas and Dimensions
31(22)
Schema Concepts
32(5)
Star and Snowflake Schemas
37(8)
Star Schema
38(3)
Snowflake Schema
41(4)
Slowly Changing Dimensions, Keeping Historical Information, and Keeping Current Information
45(6)
Keeping Current and Historical Information
46(1)
Slowly Changing Dimensions
46(5)
What Next?
51(2)
Chapter 3 Data Types and Types of Data
53(34)
Introduction to Data Types
54(6)
Storage Sizes of Various Data Types
56(1)
Character
56(1)
Integer
57(1)
Float and Double
57(1)
Array
58(1)
String
58(2)
Comparing and Contrasting Different Data Types
60(7)
Date
61(2)
Alphanumeric
63(1)
Numeric
63(1)
Text
64(1)
Currency
64(3)
Categorical vs. Dimension and Discrete vs. Continuous Data Types
67(5)
Categorical/Dimension Data Types
68(1)
Discrete vs. Continuous Data Types
69(3)
Types of Data: Audio, Video, and Images
72(14)
Audio
73(6)
Video
79(2)
Images
81(5)
What Next?
86(1)
Chapter 4 Understanding Common Data Structures and File Formats
87(24)
Structured vs. Unstructured Data
88(10)
Structured vs. Unstructured Data
90(1)
Structured Data
91(1)
Rows and Columns
92(2)
Unstructured Data
94(2)
Semi-structured Data
96(1)
Metadata
96(2)
Data File Formats
98(12)
Text/Flat File
99(1)
Tab Delimited File
100(1)
Comma-Delimited File
101(1)
JavaScript Object Notation (JSON)
102(2)
Extensible Markup Language (XML)
104(2)
Hypertext Markup Language (HTML)
106(4)
What Next?
110(1)
Chapter 5 Understanding Data Acquisition and Monetization
111(26)
Integration
112(14)
Data Integration
114(1)
Extract, Transform, and Load (ETL)
115(2)
Extract, Load, and Transform (ELT)
117(4)
Delta Load
121(1)
Application Programming Interfaces (APIs)/Web Services
121(5)
Data Collection Methods
126(9)
Web Scraping
128(1)
Public Databases
129(1)
Surveys
129(3)
Sampling
132(1)
Observation
133(2)
What Next?
135(2)
Chapter 6 Cleansing and Profiling Data
137(16)
Profiling and Cleansing Basics
138(13)
Duplicate Data
140(1)
Redundant Data
141(2)
Missing Values
143(2)
Invalid Data
145(1)
Non-parametric Data
146(1)
Data Outliers
146(2)
Specification Mismatches
148(1)
Data Type Validation
148(3)
What Next?
151(2)
Chapter 7 Understanding and Executing Data Manipulation
153(30)
Data Manipulation Techniques
154(28)
Recoding Data
156(3)
Derived Variables
159(1)
Data Merges
160(1)
Data Blending
161(1)
Concatenation
162(2)
Data Appending
164(2)
Imputation
166(1)
Data Reduction
167(1)
Data Transposition
168(2)
Normalizing Data
170(1)
Parsing/String Manipulation
171(1)
Filtering
171(1)
Sorting
172(2)
Date Functions
174(1)
Logical Functions
174(3)
Aggregate Functions
177(2)
System Functions
179(3)
What Next?
182(1)
Chapter 8 Understanding Common Techniques for Data Query Optimization and Testing
183(24)
Query Optimization
184(22)
Execution Plans
187(3)
Parameterization
190(3)
Indexing
193(4)
Temporary Table in a Query Set
197(3)
Subsets of Records
200(6)
What Next?
206(1)
Chapter 9 The (Un)Common Data Analytics Tools
207(18)
Data Analytics Tools
208(16)
Structured Query Language (SQL)
210(1)
Python
211(1)
Microsoft Excel
211(2)
R
213(1)
Rapid Miner
214(1)
IBM Cognos
214(1)
IBM SPSS Modeler
214(1)
SAS
215(1)
Tableau
216(1)
Power BI
217(1)
Qlik
218(1)
Micro Strategy
219(1)
Business Objects
219(1)
Apex
220(1)
Datorama
220(1)
Domo
220(1)
AWS Quick Sight
221(1)
Stata
221(1)
Minitab
221(3)
What Next?
224(1)
Chapter 10 Understanding Descriptive and Inferential Statistical Methods
225(30)
Introduction to Descriptive and Inferential Analysis
226(12)
Measures of Central Tendency
228(3)
Measures of Dispersion
231(1)
Range
232(2)
Frequencies
234(1)
Percent Change and Percent Difference
235(3)
Inferential Statistical Methods
238(15)
Confidence Intervals
240(1)
Z-score
241(1)
R-tests
242(1)
P-values
243(1)
Chi-Square Test
244(2)
Hypothesis Testing
246(2)
Simple Linear Regression
248(2)
Correlation
250(3)
What Next?
253(2)
Chapter 11 Exploring Data Analysis and Key Analysis Techniques
255(24)
Process to Determine Type of Analysis
256(9)
Determining Data Needs
257(2)
Review/Refine Business Questions
259(1)
Data Collection Sources
260(1)
Gap Analysis
261(4)
Types of Analysis
265(13)
Trend Analysis
267(4)
Performance Analysis
271(1)
Exploratory Data Analysis
272(2)
Link Analysis
274(4)
What Next?
278(1)
Chapter 12 Approaching Data Visualization
279(20)
Business Reports
280(17)
Report Content
282(3)
Filters
285(2)
Views
287(1)
Date Range
288(3)
Frequency
291(1)
Audience for Reports
292(5)
What Next?
297(2)
Chapter 13 Exploring the Different Types of Reports and Dashboards
299(40)
Report Cover Page and Design Elements
300(16)
Report Cover Page
301(5)
Design Elements
306(10)
Documentation Elements
316(5)
Version Number
317(1)
Reference Data Sources and Dates
318(1)
FAQs and Appendix
319(2)
Dashboard Considerations, Development, and Delivery Process
321(16)
Dashboard Considerations
324(4)
Development Process
328(4)
Delivery Considerations
332(5)
What Next?
337(2)
Chapter 14 Data-Driven Decision Making: Leveraging Charts, Graphs, and Reports
339(28)
Types of Data Visualizations
340(18)
Line Charts
342(1)
Pie Charts
343(1)
Bubble Charts
344(1)
Scatter Plots
345(1)
Bar Charts
346(1)
Histograms
347(1)
Waterfall Charts
348(1)
Heat Maps
348(2)
Geographic Maps
350(1)
Tree Maps
351(1)
Stacked Charts
352(1)
Infographics
353(1)
Word Clouds
354(4)
Reports
358(8)
Static Reporting
359(2)
Dynamic Reports
361(1)
Ad Hoc/One-Time Reports
362(1)
Self-Service/On-Demand Reports
363(1)
Recurring Reports
363(1)
Tactical/Research Reporting
364(2)
What Next?
366(1)
Chapter 15 Data Governance Concepts: Ensuring a Baseline
367(44)
Access and Security Requirements
370(13)
Access Requirements
372(2)
Security Requirements
374(9)
Storage Environment Requirements
383(5)
Shared Drives
383(1)
Local Storage
384(1)
Cloud-Based Storage
385(3)
Use and Entity Relationship Requirements
388(11)
Use Requirements
389(4)
Entity Relationship Requirements
393(6)
Data Classification, Jurisdiction Requirements, and Data Breach Reporting
399(11)
Data Classification
401(5)
Jurisdiction Requirements
406(1)
Data Breach Reporting
407(3)
What Next?
410(1)
Chapter 16 Applying Data Quality Control
411(30)
Data Quality Dimensions and Circumstances to Check for Quality
412(12)
Data Quality
413(5)
Circumstances to Check for Quality
418(3)
Final Product, Reports, and Dashboards
421(3)
Data Quality Rules and Metrics, Methods to Validate Quality, and Automated Validation
424(15)
Data Quality Rules and Metrics
426(4)
Methods to Validate Data Quality and Automated Validation
430(5)
Automated Validation
435(4)
What Next?
439(2)
Chapter 17 Understanding Master Data Management (MDM) Concepts
441(18)
Processes
442(12)
Consolidation of Multiple Data Fields
446(2)
Standardization of Data Field Names
448(3)
Data Dictionary
451(3)
Circumstances for MDM
454(4)
Mergers and Acquisitions
455(1)
Compliance with Policies and Regulations
456(1)
Streamline Data Access
456(2)
What Next?
458(1)
Chapter 18 Getting Ready for the CompTIA Data+ Exam
459(8)
Getting Ready for the CompTIA Data+ Exam
459(2)
Tips for Taking the Real Exam
461(4)
Beyond the CompTIA Data+ Certification
465(2)
Index 467
Akhil Behl is a passionate technologist and business development practitioner. He has more than 20 years of experience in the IT industry, working across several leadership, advisory, consultancy, and business development profiles across OEMs, telcos, and SI organizations. Akhil believes in cultivating an entrepreneurial culture, working across high-performance teams, identifying emerging technology trends, and ongoing innovation. For the last 7+ years, he has been working extensively with hyperscalers across industry verticals. He is employed at Red Hat, leading the Global System Integrator (GSI) partner alliances for the ANZ region.

 

Akhil is a published author. Over the past decade, he has authored multiple titles on security and business communication technologies. This is his fifth book with Pearson Education. He has contributed as technical editor for over a dozen books on security, networking, and information technology. He has published several research papers in national and international journals, including IEEE Xplore, and presented at various IEEE conferences, as well as other prominent ICT, security, and telecom events. Writing and mentoring are his passions.

 

Akhil holds CCIE 19564 Emeritus (Collaboration and Security), CompTIA Data+, Azure Solutions Architect Expert, Google Professional Cloud Architect, Azure AI Certified Associate, Azure Data Fundamentals, CCSK, CHFI, ITIL, VCP, TOGAF, CEH, ISM, CCDP, and multiple other industry certifications. He has a bachelor's degree in technology and a master's in business administration.

 

Akhil lives in Melbourne, Australia, with his better half, Kanika, and two sons, Shivansh (11 years) and Shaurya (9 years). Both of them are passionate gamers and are excellent musicians, sporting guitar and keyboard, respectively.

 

In his spare time, Akhil likes to play cricket, chess, and console games with his sons, watch movies with his family, and write articles or blogs. The family enjoys building LEGO! The family are big Star Wars fans and have keen interest in Star Wars as well as Technic and Creator LEGOs.

 

Dr. Siva Ganapathy Subramanian Manoharan is a senior professional with more than 18+ years of expertise in the data, analytics, artificial intelligence, and machine learning arenas, spanning a wide range of data portfolios. He heads the Data & Analytics Business Unit for Searce Inc in his current role as global chief data officer. He is a cloud data and platform architect with a background in data engineering, management, and analytics. He has considerable experience in a variety of enterprises across sectors. He is an ambitious leader with a startup-to-scale growth mindset who has built/launched new practices and strategic business units for several corporations and scaled them to huge growth.

 

Siva specializes in the sales, strategic solutions, P&L consulting, pre-sales, delivery of information management advisory, data architecture, and implementation services in the various industry verticals. He has extensive experience serving more than 200 customers globally, with a travel history of more than 25 countries. Over the past 8+ years, he has been living in the United Kingdom.

 

Siva leads a technology-focused group of individuals and motivates them for professional certifications and knowledge sharing. He has himself attained more than 81 IT certifications. Siva mentors and guides IT professionals and youth across the globe in their journey for a successful future in information technology focused on data analytics and artificial intelligence.

 

Siva is a technology-integrated author. He has several IT blog posts and book publications to his credit on data and analytics, artificial intelligence, and machine learning technologies. He has contributed as a technical editor for multiple blogs and whitepapers and hosted many events on data and analytics and information technology.

 

Siva was awarded the International Achievers award in 2022 by IAF India, the Leader of Excellence award in 2022 by BIZEMAG, and the Most Admired Global Indians 2022 with Passion Vista. He completed a bachelor's degree in electronics communication engineering from the University of Madras, an international MBA from Russian Ulyanovsk State University, a Ph.D. from the University of Swahili, and a D.Sc. from Azteca University.

 

Siva lives in London with Gaurave SGS (10 years) and Thejashvini SGS (5 years). Both of them are innovative artists, passionate gamers, and excellent creators. In his leisure time, Siva likes to watch movies, travel to new locations, play with Gaurave and Thejashvini, and write whitepapers, articles, and blogs.