Muutke küpsiste eelistusi

E-raamat: CompTIA Data+ Study Guide: Exam DA0-001

4.12/5 (20 hinnangut Goodreads-ist)
(University of Notre Dame), (University of Notre Dame)
  • Formaat: PDF+DRM
  • Sari: Sybex Study Guide
  • Ilmumisaeg: 18-Mar-2022
  • Kirjastus: Sybex Inc.,U.S.
  • Keel: eng
  • ISBN-13: 9781119845270
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 58,66 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
CompTIA Data+ Study Guide: Exam DA0-001Suurem pilt
  • Formaat: PDF+DRM
  • Sari: Sybex Study Guide
  • Ilmumisaeg: 18-Mar-2022
  • Kirjastus: Sybex Inc.,U.S.
  • Keel: eng
  • ISBN-13: 9781119845270
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

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 to beginning or accelerating a career as an in-demand data analyst.

Introduction xv
Assessment Test xxii
Chapter 1 Today's Data Analyst 1(16)
Welcome to the World of Analytics
2(3)
Data
2(1)
Storage
3(1)
Computing Power
4(1)
Careers in Analytics
5(1)
The Analytics Process
6(4)
Data Acquisition
7(1)
Cleaning and Manipulation
7(1)
Analysis
8(1)
Visualization
8(1)
Reporting and Communication
8(2)
Analytics Techniques
10(3)
Descriptive Analytics
10(1)
Predictive Analytics
11(1)
Prescriptive Analytics
11(1)
Machine Learning, Artificial Intelligence, and Deep Learning
11(2)
Data Governance
13(1)
Analytics Tools
13(2)
Summary
15(2)
Chapter 2 Understanding Data 17(40)
Exploring Data Types
18(21)
Structured Data Types
20(11)
Unstructured Data Types
31(5)
Categories of Data
36(3)
Common Data Structures
39(3)
Structured Data
39(2)
Unstructured Data
41(1)
Semi-structured Data
42(1)
Common File Formats
42(6)
Text Files
42(2)
JavaScript Object Notation
44(1)
Extensible Markup Language (XML)
45(2)
HyperText Markup Language (HTML)
47(1)
Summary
48(1)
Exam Essentials
49(2)
Review Questions
51(6)
Chapter 3 Databases and Data Acquisition 57(48)
Exploring Databases
58(13)
The Relational Model
59(3)
Relational Databases
62(6)
Nonrelational Databases
68(3)
Database Use Cases
71(10)
Online Transactional Processing
71(3)
Online Analytical Processing
74(1)
Schema Concepts
75(6)
Data Acquisition Concepts
81(7)
Integration
81(2)
Data Collection Methods
83(5)
Working with Data
88(11)
Data Manipulation
89(7)
Query Optimization
96(3)
Summary
99(1)
Exam Essentials
100(1)
Review Questions
101(4)
Chapter 4 Data Quality 105(46)
Data Quality Challenges
106(10)
Duplicate Data
106(1)
Redundant Data
107(3)
Missing Values
110(1)
Invalid Data
111(1)
Nonparametric data
112(1)
Data Outliers
113(1)
Specification Mismatch
114(1)
Data Type Validation
114(2)
Data Manipulation Techniques
116(16)
Recoding Data
116(1)
Derived Variables
117(1)
Data Merge
118(1)
Data Blending
119(2)
Concatenation
121(1)
Data Append
121(1)
Imputation
122(2)
Reduction
124(2)
Aggregation
126(1)
Transposition
127(1)
Normalization
128(2)
Parsing/String Manipulation
130(2)
Managing Data Quality
132(12)
Circumstances to Check for Quality
132(4)
Automated Validation
136(1)
Data Quality Dimensions
136(4)
Data Quality Rules and Metrics
140(2)
Methods to Validate Quality
142(2)
Summary
144(1)
Exam Essentials
145(1)
Review Questions
146(5)
Chapter 5 Data Analysis and Statistics 151(50)
Fundamentals of Statistics
152(3)
Descriptive Statistics
155(20)
Measures of Frequency
155(5)
Measures of Central Tendency
160(4)
Measures of Dispersion
164(9)
Measures of Position
173(2)
Inferential Statistics
175(15)
Confidence Intervals
175(4)
Hypothesis Testing
179(7)
Simple Linear Regression
186(4)
Analysis Techniques
190(2)
Determine Type of Analysis
190(1)
Types of Analysis
191(1)
Exploratory Data Analysis
192(1)
Summary
192(2)
Exam Essentials
194(2)
Review Questions
196(5)
Chapter 6 Data Analytics Tools 201(30)
Spreadsheets
202(3)
Microsoft Excel
203(2)
Programming Languages
205(4)
R
205(1)
Python
206(2)
Structured Query Language (SQL)
208(1)
Statistics Packages
209(3)
IBM SPSS
210(1)
SAS
211(1)
Stata
211(1)
Minitab
212(1)
Machine Learning
212(5)
IBM SPSS Modeler
213(1)
RapidMiner
214(3)
Analytics Suites
217(8)
IBM Cognos
217(1)
Power BI
218(1)
MicroStrategy
219(1)
Domo
220(1)
Datorama
221(1)
AWS QuickSight
222(1)
Tableau
222(2)
Qlik
224(1)
BusinessObjects
225(1)
Summary
225(1)
Exam Essentials
225(2)
Review Questions
227(4)
Chapter 7 Data Visualization with Reports and Dashboards 231(48)
Understanding Business Requirements
232(3)
Understanding Report Design Elements
235(12)
Report Cover Page
236(1)
Executive Summary
237(2)
Design Elements
239(5)
Documentation Elements
244(3)
Understanding Dashboard Development Methods
247(5)
Consumer Types
247(1)
Data Source Considerations
248(1)
Data Type Considerations
249(1)
Development Process
250(1)
Delivery Considerations
250(2)
Operational Considerations
252(1)
Exploring Visualization Types
252(16)
Charts
252(6)
Maps
258(6)
Waterfall
264(2)
Infographic
266(1)
Word Cloud
267(1)
Comparing Report Types
268(3)
Static and Dynamic
268(1)
Ad Hoc
269(1)
Self-Service (On-Demand)
269(1)
Recurring Reports
269(1)
Tactical and Research
270(1)
Summary
271(1)
Exam Essentials
272(2)
Review Questions
274(5)
Chapter 8 Data Governance 279(32)
Data Governance Concepts
280(19)
Data Governance Roles
281(1)
Access Requirements
281(5)
Security Requirements
286(3)
Storage Environment Requirements
289(2)
Use Requirements
291(1)
Entity Relationship Requirements
292(1)
Data Classification Requirements
292(5)
Jurisdiction Requirements
297(1)
Breach Reporting Requirements
298(1)
Understanding Master Data Management
299(4)
Processes
300(1)
Circumstances
301(2)
Summary
303(1)
Exam Essentials
304(2)
Review Questions
306(5)
Appendix Answers to the Review Questions 311(16)
Chapter 2: Understanding Data
312(2)
Chapter 3: Databases and Data Acquisition
314(1)
Chapter 4: Data Quality
315(2)
Chapter 5: Data Analysis and Statistics
317(2)
Chapter 6: Data Analytics Tools
319(3)
Chapter 7: Data Visualization with Reports and Dashboards
322(1)
Chapter 8: Data Governance
323(4)
Index 327
ABOUT THE AUTHORS

Mike Chapple, PhD, is Teaching Professor of IT, Analytics, and Operations at the University of Notre Dame. Hes a technology professional and educator with over 20 years of experience. Mike provides certification resources at his website, CertMike.com.

Sharif Nijim is Assistant Teaching Professor of IT, Analytics, and Operations in the Mendoza College of Business at the University of Notre Dame. He teaches undergraduate and graduate courses on cloud computing, business analytics, and information technology.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97811198452702e.html
Märksõnad: