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CompTIA Data+ (DA0-001) Online Training

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This entry-level CompTIA Data+ (DA0-001) training prepares learners to collect, analyze, and visualize data that helps their company achieve goals and clarify complex business decisions.

Companies turn to data analysts to help clarify complex situations or muddy circumstances. That's a big responsibility for someone to carry, and if you're not well trained, you could be the reason your organization makes a poor decision.


1. Understanding Data and Information
  • 1. Understanding Data and Information
  • 2. Data and Information
  • 3. Structured and Unstructured Data
  • 4. Tabular Data
  • 5. KeyValue Pairs
  • 6. Bonus Materials

  • 2. Explain Data Types
  • 1. Data Concepts and Environments
  • 2. Numeric Data
  • 3. Text Data
  • 4. Discrete and Continuous Data
  • 5. Dates and Times
  • 6. Multimedia

  • 3. Identify File Formats
  • 1. Introduction
  • 2. Text Data
  • 3. JSON Files
  • 4. HTML
  • 5. XML
  • 6. Challenge

  • 4. Explain Data Storage
  • 1. Introduction
  • 2. Relational Databases
  • 3. OLTP and OLAP
  • 4. Non-Relational Databases
  • 5. Schemas and Dimensions
  • 6. Extra Resources

  • 5. Collect and Integrate Data
  • 1. Introduction
  • 2. ETL and ELT
  • 3. Public Datasets
  • 4. APIs
  • 5. Web Scraping
  • 6. Surveys and Observations
  • 7. Sampling

  • 6. Clean and Profile Datasets
  • 1. Introduction
  • 2. Handling Duplicate Data
  • 3. Null or Missing Values
  • 4. Errors and Outliers
  • 5. Working with Outliers

  • 7. Manipulate Data
  • 1. Introduction
  • 2. Working with Strings
  • 3. Working with Dates
  • 4. Base and Derived Data
  • 5. Joining Datasets
  • 6. Bonus Resources

  • 8. Recode, Sort, Filter, and Aggregate Data
  • 1. Introduction
  • 2. Recoding Data
  • 3. Sorting Data
  • 4. Filtering Data
  • 5. Aggregating Data
  • 6. Challenge

  • 9. Optimize Data
  • 1. Introduction
  • 2. Indexing
  • 3. Subsets
  • 4. Query Execution Plans
  • 5. Parameterization

  • 10. Describe Measures of Central Tendencies
  • 1. Introduction
  • 2. Measures of Central Tendency
  • 3. Mode
  • 4. Median
  • 5. Mean
  • 6. Centrality and Distributions
  • 7. Centrality and Outliers
  • 8. Challenge

  • 11. Describe Measures of Dispersion
  • 1. Introduction
  • 2. Range
  • 3. Variance Part 1
  • 4. Variance Part 2
  • 5. Standard Deviation
  • 6. Z-score
  • 7. Percentages

  • 12. Explain Inferential Statistics
  • 1. Introduction
  • 2. Confidence Intervals Part 1
  • 3. Confidence Intervals Part 2
  • 4. Hypothesis Testing Part 1
  • 5. Hypothesis Testing Part 2
  • 6. T-test Part 1
  • 7. T-test Part 2

  • 13. Apply Inferential Statistics
  • 1. Introduction -3
  • 2. AA Testing
  • 3. AB Testing Part 1
  • 4. AB Testing Part 2
  • 5. Correlation Testing
  • 6. Simple Linear Regression

  • 14. Explore Analysis Techniques
  • 1. Introduction -5
  • 2. Business Questions
  • 3. EDA (Exploratory Data Analysis)
  • 4. Excel EDA
  • 5. Python EDA
  • 6.Analysis Types

  • 15. Identify Data Analytics Tools
  • 1. Introduction
  • 2. Spreadsheets
  • 3. Programming Languages Part 1
  • 4. Programming Languages Part 2
  • 5. SQL (Structures Query Language)
  • 6. Machine Learning
  • 7. Other Analytics Tools
  • 8. Bonus Resources

  • 16. Explain Business Requirements and Reports
  • 1. Introduction
  • 2. Meeting Requirements Part 1
  • 3. Meeting Requirements Part 2
  • 4. Building Cover Pages
  • 5. Applying Design Basics
  • 6. Including Documentation

  • 17. Explain Dashboarding
  • 1. Introduction
  • 2. Dashboard Basics
  • 3. Dashboard Design
  • 4. Building Dashboards
  • 5. Presenting Dashboards
  • 6. Bonus Resource

  • 18. Compare Visualizations
  • 1. Introduction
  • 2. Line Charts
  • 3. Scatterplots and Bubble Charts
  • 4. Pie Charts and Heat Maps
  • 5. Bar Charts and Histograms
  • 6. Tree Maps, Word Clouds, and Infographics

  • 19. Describe Data Governance Concepts
  • 1. Introduction
  • 2. Data Governance
  • 3. Data Governance Roles
  • 4. Data Owner
  • 5. Data Steward
  • 6. Data Custodian
  • 7. Challenge

  • 20. Describe Data Quality Control Concepts
  • 1. Introduction
  • 2. Master Data Management
  • 3. Data Dictionary
  • 4. Maintaining Data Quality
  • 5. Data Quality Validation

  • 21. Identify Data Security Concepts
  • 1. Introduction
  • 2. What is Data Security
  • 3. Data Confidentiality
  • 4. Access Management
  • 5. Access Control Models

  • 22. Identify Data Privacy Concepts
  • 1. Introduction -3
  • 2. What is Data Privacy
  • 3. Data Privacy Principles
  • 4. Anonymized Data
  • 5. Data Obfuscation

  • 23. Explain Regulations and Compliance
  • 1. Introduction -5
  • 2. Regulatory Compliance Concepts
  • 3. Data Breaches
  • 4. The Gramm-Leach-Biley Act (GLBA)
  • 5. Health Insurance Portability and Accountability Act (HIPAA)
  • 6. Family Educational Rights and Privacy Act (FERPA)
  • 7. International Data Transfers
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    تاریخ انتشار: 13 اسفند 1401
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