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FRCCOM-12CompTIA

CompTIA Data+ (DA0-002)

This five-day instructor-led course prepares professionals to collect, analyze, visualize, and communicate data-driven insights for business decision-making.

Duration · 5 daysVirtual + In-PersonInstructor-Led

$2,450

Course Description

This five-day instructor-led course prepares professionals to collect, analyze, visualize, and communicate data-driven insights for business decision-making. Students learn foundational data analytics concepts, data mining techniques, statistical methods, data governance practices, data visualization, and reporting methodologies aligned with the CompTIA Data+ (DA0-002) exam objectives. Through hands-on exercises and practical scenarios, participants develop the skills necessary to work effectively with data across a variety of business environments.

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Audience Profile

This course is intended for:

  • Data Analysts
  • Business Analysts
  • Reporting Analysts
  • Operations Analysts
  • Marketing Analysts
  • Project Managers
  • IT Professionals working with data
  • Individuals seeking to enter the data analytics field

Prerequisites

Before enrolling, you should have:

  • Basic computer literacy
  • Familiarity with spreadsheets and databases
  • Fundamental understanding of business operations
  • No prior data analytics experience required

— What You'll Learn —

Learning Objectives

In this course, you will learn to:

  • 1Understand core data analytics concepts and methodologies.
  • 2Collect, clean, and prepare data for analysis.
  • 3Apply statistical techniques to interpret data.
  • 4Identify trends, patterns, and business insights.
  • 5Develop effective visualizations and dashboards.
  • 6Communicate analytical findings to stakeholders.
  • 7Support data governance and quality management initiatives.
  • 8Prepare for and confidently attempt the CompTIA Data+ (DA0-002) certification exam.

— Day-by-Day —

Course Outline

Day 1: Data Concepts and Environments

Module 1

Introduction to Data Analytics

  • The role of data analytics in organizations
  • Data analytics lifecycle
  • Types of analytics
  • Descriptive
  • Diagnostic
  • Predictive
  • Prescriptive
  • Data-driven decision-making

Module 2

Data Types and Structures

  • Structured, semi-structured, and unstructured data
  • Quantitative and qualitative data
  • Data formats and file types
  • Relational and non-relational databases
  • Data models and schemas

Module 3

Data Environments

  • Data warehouses
  • Data marts
  • Data lakes
  • Cloud-based data platforms
  • Big data concepts

Module 4

Data Governance Fundamentals

  • Data quality dimensions
  • Data ownership and stewardship
  • Data privacy and security
  • Compliance considerations
  • Data governance frameworks

Day 2: Data Collection and Data Preparation

Module 5

Data Collection Methods

  • Data acquisition techniques
  • Internal and external data sources
  • Surveys and observational data
  • APIs and automated data collection
  • Data integration concepts

Module 6

Data Cleaning and Transformation

  • Data profiling
  • Data cleansing techniques
  • Handling missing values
  • Data normalization and standardization
  • Data transformation processes

Module 7

Data Preparation Techniques

  • Data aggregation
  • Data filtering and sorting
  • Data enrichment
  • Data validation procedures
  • Preparing datasets for analysis

Module 8

Data Quality Management

  • Data quality assessment
  • Identifying inconsistencies and anomalies
  • Data integrity controls
  • Quality monitoring processes

Day 3: Data Analysis and Statistical Methods

Module 9

Fundamental Statistics

  • Measures of central tendency
  • Measures of dispersion
  • Percentiles and quartiles
  • Data distributions
  • Outlier identification

Module 10

Statistical Analysis Techniques

  • Correlation analysis
  • Trend analysis
  • Sampling methods
  • Hypothesis testing concepts
  • Probability fundamentals

Module 11

Analytical Methods

  • Exploratory data analysis
  • Comparative analysis
  • Root cause analysis
  • Pattern recognition
  • Forecasting concepts

Module 12

Querying and Data Exploration

  • Database query concepts
  • Data retrieval techniques
  • Filtering and grouping data
  • Basic reporting queries
  • Data exploration methodologies

Day 4: Data Visualization and Reporting

Module 13

Data Visualization Fundamentals

  • Principles of effective visualization
  • Selecting appropriate chart types
  • Visual storytelling techniques
  • Dashboard design concepts
  • Accessibility considerations

Module 14

Dashboard Development

  • KPI identification
  • Executive dashboards
  • Operational dashboards
  • Interactive reporting concepts
  • Dashboard best practices

Module 15

Business Reporting

  • Report development methodologies
  • Audience-focused reporting
  • Communicating analytical findings
  • Data presentation techniques
  • Report validation and review

Module 16

Data-Driven Decision Support

  • Translating analysis into recommendations
  • Business impact assessment
  • Risk identification
  • Decision-making frameworks
  • Performance measurement

Day 5: Data Governance, Analytics Operations, and Exam Preparation

Module 17

Advanced Data Governance

  • Data lifecycle management
  • Metadata management
  • Master data management concepts
  • Data retention policies
  • Ethical use of data

Module 18

Analytics Operations

  • Data management workflows
  • Analytics project planning
  • Collaboration among stakeholders
  • Documentation standards
  • Continuous improvement processes

Module 19

Business Analytics Case Studies

  • Sales performance analysis
  • Financial reporting scenarios
  • Operational efficiency assessments
  • Customer behavior analysis
  • Risk management analytics

Module 20

CompTIA Data+ (DA0-002) Exam Preparation

  • Review of all exam domains
  • Data analysis methodologies review
  • Scenario-based exercises
  • Practice assessments
  • Test-taking strategies

— Additional Details —

What else is included

Hands-On Activities Included

  • Exploring data sources and formats
  • Identifying data quality issues
  • Working with relational datasets
  • Data environment assessment exercises
  • Cleaning and transforming datasets
  • Data profiling exercises
  • Quality assessment activities
  • Preparing datasets for analysis
  • Statistical analysis exercises
  • Trend and correlation analysis
  • Dataset exploration activities
  • Analytical problem-solving scenarios
  • Creating visualizations and charts
  • Designing dashboards
  • Developing business reports
  • Presenting analytical findings

Note: Course outlines are provided as a general guide. Content, pacing, labs, and instructional emphasis may vary based on instructor expertise, student experience levels, and customer-specific learning objectives.

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