CompTIA Data Plus (DAO-001) Certification Crash Course

 About the Course

In this course we focus on preparing for you’re sitting for the CompTIA Data Plus DA0-001 exam.

Data is considered the most important asset a lot of organizations have and as a data professional you will be able to provide immediate value to data driven organizations.

CompTIA Data+ is a very challenging exam but successful passing will validate that certified professionals have the skills required to facilitate data-driven business decisions to provide immediate value to the enterprise.

An important aspect that data professional must know is focused on data mining, data manipulation as well as visualizing and reporting data to stakeholders.

So, whether applying basic statistical methods or analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle a data professional is an important role to enterprises.

All of the exam objectives are covered as specified by CompTIA for the exam in these domains. 

• Data Concepts and Environments
• Data Mining
• Data Analysis
• Data Visualization
• Data Governance, Quality, and Controls

ABOUT THE COMPTIA DATA+ CERTIFICATION

CompTIA Data+ is an early-career data analytics certification for professionals tasked with developing and promoting data-driven business decision-making. The certification validates the data analytics skills and competencies that are needed to organize, understand, and act on relevant data.

This course covers 100% of the DA0-001 objective domains and also provides exam test tips, topic focused demonstrations and over 100 practice questions along with a free downloadable study guide.

 What will you learn in the course?

• Understand the importance of the CompTIA Data Plus exam and its objectives.
• You'll learn to how to collect, analyze, and report on various types of commonly used data.
• You’ll learn about how to transform raw data into usable information for your stakeholders.
• Learn about database types, data structures, data schemes and other important aspects of data management.
• Understand how JSON data, HTML data, and XML data is used with data professionals.
• Understand how ETL and ELT processes work, how APIs connect us to the cloud and learn about profiling datasets.
• Learn about data manipulation techniques and important concepts around data transposition and normalization.
• Learn about descriptive statistics, inferential statistics, and various analytic techniques.
• Identify common analytics tools used in data analysis.
• Learn about the importance of Structured Query Language (SQL) and its main components.
• Learn about reporting, reporting dashboards and the various visualization types.
• Describe the importance of data governance, data stewardship and quality controls to ensure compliance and data consistency.
• Identify the compliance requirements, security controls and privacy.

Course Content Covered

• Course Welcome
• Course Overview
• Instructor Introduction
• What is the CompTIA Data Plus Exam
• Exam Objectives
• Exam Acronym List
• Data Roles to Know
• The Importance of Data
• Download Course Resources
• Data Concepts and Environments
• Data Schemes
• Data Dimensions
• Databases
• Demonstration - Google Cloud SQL
• Data Warehouses and Data Lakes
• Online transactional processing (OLTP)
• Demonstration - AWS Redshift
• Online Analytical Processing (OLAP)
• What is a Schema?
• Importance of Dimensions
• Demonstration - Google Cloud Big Query (OLAP)
• Data Types
• Demonstration - File Types
• Demonstration - Deploy SQL Demo Bench
• Data Structures
• What is a Data Structure
• Structured
• Unstructured
• Semi Structured
• Data File Formats
• Big Data File Formats
• What is Columnar Format
• Data Compression
• Module Summary Review
• Module Review Questions
• Data Mining
• Understanding Data Acquisition
• Integration Concepts
• What is An API?
• Demonstration - APIs
• Data Collection Method Options
• Demonstration - Google Big Query Sample Data
• Whiteboard Discussion - Data Collection
• Data Cleansing and Profiling
• Whiteboard Discussion - Data Cleansing/Profiling
• Demonstration - Excel Data Cleansing and Profiling
• Data Outliers
• Understanding Data Manipulation Techniques
• Recoding Data
• Merge Data
• Eliminate Redundancy
• Data Normalization
• Extract, Transform and Load (ETL)
• Scenario - Data Manipulation
• Common techniques for data manipulation and query optimization
• Data Manipulation Workflow
• Data Manipulation Techniques
• Query Optimization
• Demonstration - Query Optimization
• Module Summary Review
• Module Review Questions
• Data Analysis
• Understanding Descriptive Statistical Methods
• Measures of Tendency
• Measures of Dispersion
• Understanding Percentages
• Understanding Inferential Statistical Methods
• Hypothesis Testing
• Linear Regression and Correlation
• Summarize types of analysis and key analysis techniques
• Define Exploratory Data Analysis
• Performance Analysis
• Link Analysis
• Common Data Tool Sets
• Demonstration - MS Excel
• Demonstration - Power BI
• Demonstration - AWS Quicksight
• Module Summary Review
• Module Review Questions
• Data Visualization
• Module Overview
• Translate Business Requirements to Reports
• Design Components
• Demonstration - Reports and Components
• Dashboard Design
• Dashboard Components
• Demonstration - Dashboard Components
• Data Sources and Attributes
• Consumers
• Delivery and Development
• Visualization Types
• Understanding Chart Types
• Understanding Plot Types
• Understanding Mapping
• Demonstration - Visualization
• Compare and Contrast Reports
• Reports Type Overview
• Recurring Report Types
• Static and Dynamic Reports
• Demonstration - Compliance
• Module Summary Review
• Module Review Questions
• Data Governance, Quality and Controls
• Module Overview
• Data Governance
• Requirements
• Data Classification
• Data Privacy
• Data Breaches
• Data Quality Control
• Data Checks
• Data Transformation
• Data Validation
• Data Quality
• Data Quality Dimensions
• Rules and Metrics
• Validation Techniques
• Master Data Management (MDM)
• Importance of MDM
• Process and Circumstances
• Module Summary Review
• Module Review Questions
• Exam Preparation and Practice Exams
• Exam Experience
• Certification CPE Requirements
• Course Content Review
• Top Ten Things to Know for the Exam
• Practice Questions Pool 1
• Practice Questions Pool 2
• Additional Resources
• Course Closeout

Who should take this course (Target Audience)? 

• Beginners looking for an entry point into the data world.
• Data Engineers, Data Analysts with some experience working with data.
• Software professionals, Database professionals looking to boost their knowledge and skillsets

What are the Couse Pre Requirements?

There are no course pre-requirements but have some experience would be advisable for this challenging exam.

Course Author – Joseph Holbrook

What You'll Learn

✔ Student will learn about common data models and types.

✔ Learn about reporting, reporting dashboards and the various visualization types.

✔ Describe the importance of data governance, data stewardship and quality controls to ensure compliance and data consistency.

✔ Students will understand how to prepare for the Data Plus exam and pass the exam with proper study

 
✔ You'll learn to how to collect, analyze, and report on various types of commonly used data.

✔ You’ll learn about how to transform raw data into usable information for your stakeholders.

✔ Learn about database types, data structures, data schemes and other important aspects of data management.

✔Understand how JSON data, HTML data, and XML data is used with data professionals.

Course is targeted towards an audience such as: 

• Beginners looking for an entry point into the data world.
• Data Engineers, Data Analysts with some experience working with data.
• Software professionals, Database professionals looking to boost their knowledge and skillsets