<p><span>Learn data analysis essentials and prepare for the Data+ exam with this CompTIA exam guide, complete with practice exams towards the end.</span></p><h4><span>Key Features</span></h4><ul><li><span><span>Apply simple methods of data analysis and find out when and how to apply more complicated
CompTIA Data+: DAO-001 Certification Guide: Complete coverage of the new CompTIA Data + (DAO-001) exam to help you pass on the first attempt
β Scribed by Cameron Dodd
- Publisher
- Packt Publishing
- Year
- 2022
- Tongue
- English
- Leaves
- 370
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
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 and perform 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
- Introduction to Data +
- Data Structures, Types, and Formats
- Collecting Data
- Cleaning and Processing Data
- Data Wrangling and Manipulation
- Types of Analysis
- Measures of Central Tendency and Dispersion
- Common Techniques in Descriptive Statistics
- Hypothesis Testing
- Introduction to Statistical Analyses
- Types of Reports
- Turning a Question into a Report
- Common Visualizations
- Data Governance
- Data Quality and Management
- Practice Exam One
- Practice Exam Two
β¦ 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 (οΏΌ)
Alternative hypothesis (οΏΌ)
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
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