CompTIA data+ DAO-001 certification guide : complete coverage of the new comptia data + (DAO-001) exam to help you pass on the first

CompTIA data+ DAO-001 certification guide complete coverage of the new comptia data + (DAO-001) exam to help you pass on the first

Learn data analysis essentials and prepare for the Data+ exam with this CompTIA exam guide, complete with practice exams towards the end. Key Features Apply simple methods of data analysis and find out when and how to apply more complicated ones Take business requirements and produce a remote to the...

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Bibliographic Details
Other Authors: Dodd, Cameron, author (author)
Format: eBook
Language:Inglés
Published: London, England : Packt Publishing [2022]
Edition:1st ed
Subjects:
Data mining > Examinations > Study guides.
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009711797606719
Table of Contents:
  • Cover
  • Title Page
  • Copyright and Credit
  • Dedicated
  • Contributors
  • Table of Contents
  • Preface
  • Part 1: Preparing Data
  • Chapter 1: Introduction to CompTIA Data+
  • Understanding Data+
  • CompTIA Data+: DAO-001
  • Data science
  • Introducing the exam domains
  • Data Concepts and Environments
  • Exam format
  • Who should take the exam?
  • Summary
  • Chapter 2: Data Structures, Types, and Formats
  • Understanding structured and unstructured data
  • Structured databases
  • Unstructured databases
  • Relational and non-relational databases
  • Going through a data schema and its types
  • Star schema
  • Snowflake schema
  • Understanding the concept of warehouses and lakes
  • Data warehouses
  • Data marts
  • Data lakes
  • Updating stored data
  • Updating a record with an up-to-date value
  • Changing the number of variables being recorded
  • Going through data types and file types
  • Data types
  • Variable types
  • File types
  • Summary
  • Practice questions and their answers
  • Questions
  • Answers
  • Chapter 3: Collecting Data
  • Utilizing public sources of data
  • Public databases
  • Open sources
  • Application programming interfaces and web services
  • Collecting your own data
  • Web scraping
  • Surveying
  • Observing
  • Differentiating ETL and ELT
  • ETL
  • ELT
  • Delta load
  • Understanding OLTP and OLAP
  • OLTP
  • OLAP
  • Optimizing query structure
  • Filtering and subsets
  • Indexing and sorting
  • Parameterization
  • Temporary tables and subqueries
  • Execution plan
  • Summary
  • Practice questions and their answers
  • Questions
  • Answers
  • Chapter 4: Cleaning and Processing Data
  • Managing duplicate and redundant data
  • Duplicate data
  • Redundant data
  • Dealing with missing data
  • Types of missing data
  • Deletion
  • Imputation
  • Interpolation
  • Dealing with MNAR.
  • Understanding invalid data, specification mismatch, and data type validation
  • Invalid data
  • Specification mismatch
  • Data type validation
  • Understanding non-parametric data
  • Finding outliers
  • Summary
  • Practice questions
  • Questions
  • Answers
  • Chapter 5: Data Wrangling and Manipulation
  • Merging data
  • Key variables
  • Joining
  • Blending
  • Concatenation and appending
  • Calculating derived and reduced variables
  • Derived variables
  • Reduction variables
  • Parsing your data
  • Recoding variables
  • Recoding numbers into categories
  • Recoding categories into numbers
  • Shaping data with common functions
  • Working with dates
  • Conditional operators
  • Transposing data
  • System functions
  • Summary
  • Practice questions
  • Questions
  • Answers
  • Part 2: Analyzing Data
  • Chapter 6: Types of Analytics
  • Technical requirements
  • Exploring your data
  • Common types of EDA
  • EDA example
  • Checking on performance
  • KPIs
  • Project management
  • Process analytics
  • Discovering trends
  • Finding links
  • Choosing the correct analysis
  • Why is choosing an analysis difficult?
  • Assumptions
  • Making a list
  • Finally choosing the analysis type
  • Summary
  • Practice questions
  • Questions
  • Answers
  • Chapter 7: Measures of Central Tendency and Dispersion
  • Discovering distributions
  • Normal distribution
  • Uniform distribution
  • Poisson distribution
  • Exponential distribution
  • Bernoulli distribution
  • Binomial distribution
  • Skew and kurtosis
  • Understanding measures of central tendency
  • Mean
  • Median
  • Mode
  • When to use which
  • Calculating ranges and quartiles
  • Ranges
  • Quartiles
  • Interquartile range
  • Finding variance and standard deviation
  • Variance
  • Standard deviation
  • Summary
  • Practice questions
  • Questions
  • Answers
  • Chapter 8: Common Techniques in Descriptive Statistics.
  • Understanding frequencies and percentages
  • Frequencies
  • Percentages
  • Calculating percent change and percent difference
  • Percent change
  • Percent difference
  • Discovering confidence intervals
  • Understanding z-scores
  • Summary
  • Practice questions
  • Questions
  • Answers
  • Chapter 9: Hypothesis Testing
  • Understanding hypothesis testing
  • Why use hypothesis testing
  • Hypothesis testing process
  • Differentiating null hypothesis and alternative hypothesis
  • Null hypothesis ( )
  • Alternative hypothesis ( )
  • Null hypothesis versus alternative hypothesis
  • Learning about p-value and alpha
  • p-value
  • Alpha
  • Alpha and tails
  • Understanding type I and type II errors
  • Type I error
  • Type II error
  • How type I and type II errors interact with alpha
  • Writing the right questions
  • The parts of a good question
  • Qualities of a good question
  • What to do about bad questions
  • Summary
  • Practice questions
  • Questions
  • Answers
  • Chapter 10: Introduction to Inferential Statistics
  • Technical requirements
  • Understanding t-tests
  • What you need to know about t-tests
  • T-test practice
  • Knowing chi-square
  • What you need to know about chi-square
  • Chi-square practice
  • Calculating correlations
  • Correlation
  • Correlation practice
  • Understanding simple linear regression
  • What you need to know about simple linear regression
  • Simple linear regression practice
  • Summary
  • Practice questions
  • Questions
  • Answers
  • Part 3: Reporting Data
  • Chapter 11: Types of Reports
  • Distinguishing between static and dynamic reports
  • Point-in-time reports
  • Real-time reports
  • Static versus dynamic reports
  • Understanding ad hoc and research reports
  • Ad hoc reports
  • Research reports
  • Knowing about self-service reports
  • Understanding recurring reports
  • Compliance reports
  • Risk and regulatory reports.
  • Operational reports (KPI reports)
  • Knowing important analytical tools
  • Query tools
  • Spreadsheet tools
  • Programming language tools
  • Visualization tools
  • Business services
  • All-purpose tools
  • Which tools you should learn to use
  • Summary
  • Practice questions
  • Questions
  • Answers
  • Chapter 12: Reporting Process
  • Understanding the report development process
  • Creating a plan
  • Getting the plan approved
  • Creating the report
  • Delivering the report
  • Knowing what to consider when making a report
  • Business requirements
  • Dashboard-specific requirements
  • Understanding report elements
  • Understanding report delivery
  • Designing reports
  • Branding
  • Fonts, layouts, and chart elements
  • Color theory
  • Summary
  • Practice questions
  • Questions
  • Answers
  • Chapter 13: Common Visualizations
  • Understanding infographics and word clouds
  • Infographics
  • Word clouds
  • Comprehending bar charts
  • Bar charts
  • Stacked charts
  • Histograms
  • Waterfall charts
  • Charting lines, circles, and dots
  • Line charts
  • Pareto charts
  • Pie charts
  • Scatter plots
  • Bubble charts
  • Understanding heat maps, tree maps, and geographic maps
  • Heat maps
  • Tree maps
  • Geographic maps
  • Summary
  • Practice questions
  • Questions
  • Answers
  • Chapter 14: Data Governance
  • Understanding data security
  • Access requirements
  • Security requirements
  • Knowing use requirements
  • Acceptable use policy
  • Data processing
  • Data deletion
  • Data retention
  • Understanding data classifications
  • Personally identifiable information
  • Personal health information
  • Payment Card Industry
  • Handling entity relationship requirements
  • Summary
  • Practice questions
  • Questions
  • Answers
  • Chapter 15: Data Quality and Management
  • Understanding quality control
  • When to check for quality
  • Data quality dimensions.
  • Data quality rules and metrics
  • Validating quality
  • Cross-validation
  • Sample/spot check
  • Reasonable expectations
  • Data profiling
  • Data audits
  • Automated checks
  • Understanding master data management
  • When to use MDM
  • Processes of MDM
  • Summary
  • Practice questions
  • Questions
  • Answers
  • Part 4: Mock Exams
  • Chapter 16: Practice Exam One
  • Practice exam one
  • Congratulations!
  • Practice exam one answers
  • Chapter 17: Practice Exam Two
  • Practice exam two
  • Congratulations!
  • Practice exam two answers
  • Index
  • Other Books You May Enjoy.

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