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دانلود کتاب CompTIA Data+: DAO-001 Certification Guide: Complete coverage of the new CompTIA Data + (DAO-001) exam to help you pass on the first attempt
دانلود کتاب CompTIA Data+: DAO-001 Guide Certification: پوشش کامل آزمون جدید CompTIA Data + (DAO-001) برای کمک به شما در قبولی در اولین تلاش
مشخصات کتاب
CompTIA Data+: DAO-001 Certification Guide: Complete coverage of the new CompTIA Data + (DAO-001) exam to help you pass on the first attempt
ویرایش:
نویسندگان: Cameron Dodd
سری:
ISBN (شابک) : 1804616087, 9781804616086
ناشر: Packt Publishing
سال نشر: 2022
تعداد صفحات: 370
زبان: English
فرمت فایل : RAR (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 36 Mb
قیمت کتاب (تومان) : 40,000
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توضیحاتی در مورد کتاب CompTIA Data+: DAO-001 Guide Certification: پوشش کامل آزمون جدید CompTIA Data + (DAO-001) برای کمک به شما در قبولی در اولین تلاش
توضیحاتی درمورد کتاب به خارجی
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
فهرست مطالب
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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