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CompTIA DataSys+ certification focuses on the integration and management of data systems, emphasizing the skills needed to design, deploy, and maintain data infrastructure. It covers database management, data architecture, and system performance optimization, making it ideal for professionals responsible for the seamless operation of data systems in diverse environments.

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CompTIA DataSys+ Training Course | Master Data Management & Security

Curriculum

Get certified with the CompTIA DataSys+ Training Course! Master database management, security & automation for a thriving IT career. Enroll now!

Ratings

( 4.4 Ratings )

Live Online Classes starting on 01 January, 1970

CompTIA DataSys+

The CompTIA DataSys+ certification validates the core knowledge and skills required for IT professionals working with data systems and infrastructure. This certification training provides comprehensive coverage of all exam objectives, helping you prepare effectively for the DataSys+ certification exam.

 

Training Delivery Methods:

  • In-Person: Attend classes at a training center.

  • Online: Access the training remotely via the internet.

Training Information:

In this course, you will learn:

  • The data lifecycle, from creation to disposal.

  • Data processing methods, including batch, streaming, and automation.

  • Data storage, access, warehousing, and mining.

  • SQL querying, data modeling, and database administration.

  • Data security controls, such as encryption, access controls, and auditing.

  • Legal and compliance regulations around data.

  • Data analytics concepts, including visualization and predictive modeling.

 

Training Prerequisites:

There are no formal prerequisites, but CompTIA recommends having CompTIA Network+ and some hands-on experience with database administration, data analysis, and SQL querying before attempting DataSys+.

 

Certification Information:

  • The exam consists of a maximum of 90 multiple-choice and performance-based questions.

  • Candidates have 90 minutes to complete the exam.

 

Course Outline:

Module 1 – Data Management Concepts

  • Foundations of Data Management

    • Introduction to Data Management

    • Types of Data (Structured, Unstructured, Semi-Structured)

    • Data Life Cycle Management

    • Data Governance and Compliance

  • Data Architecture

    • Data Models and Structures

    • Data Warehousing and Data Lakes

    • Big Data Technologies

    • Data Integration Techniques

Module 2 – Data Systems Operations

  • Database Management Systems (DBMS)

    • Types of DBMS (Relational, NoSQL, NewSQL)

    • Database Design and Normalization

    • Querying Databases with SQL

    • Indexing and Performance Tuning

  • Data Storage and Retrieval

    • Storage Technologies and RAID Configurations

    • Data Access Methods

    • Data Backup and Recovery Strategies

    • Cloud-Based Data Storage Solutions

Module 3 – Data Security and Compliance

  • Data Security Fundamentals

    • Data Encryption and Masking

    • Identity and Access Management (IAM)

    • Data Loss Prevention (DLP)

    • Securing Data in Transit and at Rest

  • Compliance and Legal Aspects

    • Data Privacy Laws and Regulations (GDPR, CCPA, etc.)

    • Auditing and Reporting Requirements

    • Managing Data Breaches and Incident Response

    • Ethical Considerations in Data Management

Module 4 – Data Analytics and Reporting

  • Data Analysis Techniques

    • Statistical Methods for Data Analysis

    • Data Visualization Techniques

    • Business Intelligence (BI) Tools and Dashboards

    • Machine Learning Basics for Data Analysis

  • Reporting and Communication

    • Creating Effective Data Reports

    • Communicating Data Insights to Stakeholders

    • Data Storytelling Techniques

    • Automating Reports and Dashboards

Module 5 – Emerging Data Technologies

  • Advanced Data Technologies

    • Introduction to Artificial Intelligence (AI) and Machine Learning (ML)

    • Exploring Data Science and Predictive Analytics

    • Internet of Things (IoT) and Data Management

    • Blockchain and Data Integrity

  • Future Trends in Data Systems

    • The Role of Data in Digital Transformation

    • Data Monetization Strategies

    • The Evolving Landscape of Data Privacy and Security

    • Preparing for the Future of Data Systems

(4.4 Ratings)

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