CompTIA Data+: DAO-001 Certification Guide

CompTIA Data+: DAO-001 Certification Guide

by Cameron Dodd
Released December 2022
Publisher(s): Packt Publishing
ISBN: 9781804616086

Read it now on the O’Reilly learning platform with a 10-day free trial.

O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Start your free trial

Book description

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 correct audience using appropriate visualizations
  • Learn about data governance rules, including quality and control

Book Description

The CompTIA Data+ certification exam not only helps validate a skill set required to enter one of the fastest-growing fields in the world, but also is starting to standardize the language and concepts within the field. However, there’s a lot of conflicting information and a lack of existing resources about the topics covered in this exam, and even professionals working in data analytics may need a study guide to help them pass on their first attempt.

The CompTIA Data + (DAO-001) Certification Guide will give you a solid understanding of how to prepare, analyze, and report data for better insights.

You’ll get an introduction to Data+ certification exam format to begin with, and then quickly dive into preparing data. You'll learn about collecting, cleaning, and processing data along with data wrangling and manipulation. As you progress, you’ll cover data analysis topics such as types of analysis, common techniques, hypothesis techniques, and statistical analysis, before tackling data reporting, common visualizations, and data governance. All the knowledge you've gained throughout the book will be tested with the mock tests that appear in the final chapters.

By the end of this book, you’ll be ready to pass the Data+ exam with confidence and take the next step in your career.

What you will learn

  • Become well versed in the five domains covered in the DAO-001 exam
  • Gain an understanding of all the major concepts covered in the exam and when to apply them
  • Understand the fundamental concepts behind ETL and ELT
  • Explore various imputation and deletion methods to deal with missing data
  • Identify and deal with outliers
  • Learn about performing hypothesis testing
  • Create insightful reports to showcase your findings

Who this book is for

If you are a data analyst looking to get certified with DAO-001 exam this is the book for you. This CompTIA book is also ideal for who needs help in entering the quickly growing field of Data Analytics and are seeking professional certifications.

Table of contents

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

Product information

  • Title: CompTIA Data+: DAO-001 Certification Guide
  • Author(s): Cameron Dodd
  • Release date: December 2022
  • Publisher(s): Packt Publishing
  • ISBN: 9781804616086