CompTIA Data+ gives your team members the confidence to bring data analysis to life.

CompTIA Data+ is an early-career data analytics certification that gives you the confidence to bring data analysis to life and make data-driven business decisions.

The CompTIA Data+ credential ensures professionals have foundational skills to better analyze and interpret data, communicate insights, and demonstrate competency. CompTIA Data+ can help you develop and refine these skills and prove you have the hands-on experience employers seek.

HRDC Claimable and Malaysian Bumiputeras are eligible for Yayasan Peneraju Financing Scheme. T&C applies.

Overview

The Official CompTIA Data+ training course have been developed by CompTIA for the CompTIA Data+ candidate.

CompTIA Data+ teaches learners the knowledge and skills required to transform business requirements in support of data-driven decisions by mining data, manipulating data, applying basic statistical methods, and analyzing complex data sets while adhering to governance and quality standards throughout the entire data lifecycle.

In addition, it will help prepare candidates to take the CompTIA Data+ certification exam.

Discover opportunities in Malaysia’s growing digital economy. With billions of dollars invested by global tech giants like AWS, Microsoft, Google, and Oracle, Malaysia is rapidly emerging as a hub for digital innovation.​ The demand for certified Data and AI professionals has never been higher, making it the perfect time to elevate your career.

Explore the top Data and AI certifications for 2026. Build Malaysia’s AI & Data-Driven future.

Skills Covered

  • Boost your knowledge in identifying basic concepts of data schemas and dimensions while understanding the difference between common data structures and file formats
  • Grow your skills to explain data acquisition concepts, reasons for cleansing and profiling datasets, executing data manipulation, and understanding techniques for data manipulation
  • Gain the ability to apply the appropriate descriptive statistical methods and summarize types of analysis and critical analysis techniques
  • Learn how to translate business requirements to form the appropriate visualization in the form of a report or dashboard with the proper design components
  • Increase your ability to summarize important data governance concepts and apply data quality control concepts

Prerequisites

There are no prerequisites required to attend this course.

Target Audience

  • Data Analyst
  • Reporting Analyst
  • Marketing Analyst
  • Clinical Analyst
  • Operations Analyst
  • Business Analyst
  • Business Intelligence Analyst

Data+ is an ideal certification for not only data-specific careers, but other career paths can benefit from analytics processes and data analytics knowledge. Jobs like marketing specialists, financial analysts, human resource analysts or clinical health care analysts can optimize performance and make well-informed decisions when they use and evaluate data correctly.

Course Curriculum

Module 1: Identifying Basic Concepts of Data Schemas

Topic 1A: Identify Relational and NonRelational Databases

  • Exam Objective: 1.1 Identify basic concepts of data schemas and dimensions.
  • Review Activity: Relational and Non-Relational Databases

Topic 1B: Understand the Way We Use Tables, Primary Keys, and Normalization

  • Exam Objectives:
    • 1.1 Identify basic concepts of data schemas and dimensions.
    • 2.3 Given a scenario, execute data manipulation techniques.
    • 5.1 Summarize important data governance concept
  • Video: Identifying Relationships in Data
  • Review Activity: Tables, Primary Keys, and Normalization

Module 2: Understanding Different Data Systems

Topic 2A: Describe Types of Data Processing and Storage Systems

  • Exam Objective: 1.1 Identify basic concepts of data schemas and dimensions.
  • Review Activity: Types of Data Processing and Storage Systems

Topic 2B: Explain How Data Changes

  • Exam Objective: 1.1 Identify basic concepts of data schemas and dimensions.
  • Review Activity: Explain How Data Changes

Module 3: Understanding Types and Characteristics of Data

Topic 3A: Understand Types of Data

  • Exam Objective: 1.2 Compare and contrast different data types
  • Review Activity: Types of Data

Topic 3B: Break Down the Field Data Types

  • Exam Objective: 1.2 Compare and contrast different data types
  • Video: Understanding Field Data Types
  • Review Activity: Field Data Types

Module 4: Comparing and Contrasting Different Data Structures, Formats, and Markup Languages

Topic 4A: Differentiate between Structured Data and Unstructured Data

  • Exam Objective: 1.3 Compare and contrast common data structures and file formats
  • Video: Structured Data versus Unstructured Data
  • Review Activity: Structured and Unstructured Data

Topic 4B: Recognize Different File Formats

  • Exam Objective: 1.3 Compare and contrast common data structures and file formats
  • Review Activity: File Formats

Topic 4C: Understand the Different Code Languages Used for Data

  • Exam Objective: 1.3 Compare and contrast common data structures and file formats
  • Review Activity: Code Languages Used for Data

Module 5: Explaining Data Integration and Collection Methods

Topic 5A: Understand the Processes of Extracting, Transforming, and Loading Data

  • Exam Objective: 2.1 Explain data acquisition concepts
  • Review Activity: The Processes of Extracting, Transforming, and Loading Data

Topic 5B: Explain API/Web Scraping and Other Collection Methods

  • Exam Objectives:
    • 2.1 Explain data acquisition concepts
    • 1.3 Compare and contrast common data structures and file formats
  • Review Activity: API/Web Scraping and Other Collection Methods

Topic 5C: Collect and Use Public and Publicly Available Data

  • Exam Objective: 2.1 Explain data acquisition concepts
  • Video: Creating a Data Set from Census Data
  • Review Activity: Public and Publicly Available Data

Topic 5D: Use and Collect Survey Data

  • Exam Objective: 2.1 Explain data acquisition concepts
  • Video: Building a Survey and Collecting Data
  • Review Activity: Survey Data

Module 6: Identifying Common Reasons for Cleansing and Profiling Data

Topic 6A: Learn to Profile Data

  • Exam Objective: 2.2 Identify common reasons for cleansing and profiling datasets
  • Review Activity: Learn to Profile Data

Topic 6B: Address Redundant, Duplicated, and Unnecessary Data

  • Exam Objective: 2.2 Identify common reasons for cleansing and profiling datasets
  • Review Activity: Redundant, Duplicated, and Unnecessary Data

Topic 6C: Work with Missing Values

  • Exam Objective: 2.2 Identify common reasons for cleansing and profiling datasets
  • Review Activity: Missing Values

Topic 6D: Address Invalid Data

  • Exam Objective: 2.2 Identify common reasons for cleansing and profiling datasets
  • Video: Correcting or Removing Invalid Data
  • Review Activity: Invalid Data

Topic 6E: Convert Data to Meet Specifications

  • Exam Objective: 2.2 Identify common reasons for cleansing and profiling datasets
  • Review Activity: Convert Data to Meet Specifications

Module 7: Executing Different Data Manipulation Techniques

Topic 7A: Manipulate Field Data and Create Variables

  • Exam Objective: 2.3 Given a scenario, execute data manipulation techniques
  • Review Activity: Manipulate Field Data and Create Variables

Topic 7B: Transpose and Append Data

  • Exam Objective: 2.3 Given a scenario, execute data manipulation techniques
  • Video: Transposing Data and Appending Data Sets
  • Review Activity: Transpose and Append Data

Topic 7C: Query Data

  • Exam Objective: 2.3 Given a scenario, execute data manipulation techniques
  • Video: Discovering how Joins Impact Data Results
  • Review Activity: Query Data

Module 8: Explaining Common Techniques for Data Manipulation and Optimization

Topic 8A: Use Functions to Manipulate Data

  • Exam Objectives:
    • 2.3 Given a scenario, execute data manipulation techniques
    • 2.4 Explain common techniques for data manipulation and query optimization
  • Video: Using Functions to Manipulate Data
  • Review Activity: Functions to Manipulate Data

Topic 8B: Use Common Techniques for Query Optimization

  • Exam Objective: 2.4 Explain common techniques for data manipulation and query optimization
  • Review Activity: Common Techniques for Query Optimization

Module 9: Applying Descriptive Statistical Methods

Topic 9A: Use Measures of Central Tendency

  • Exam Objective: 3.1 Given a scenario, apply the appropriate descriptive statistical methods
  • Video: Calculating the Measure of Central Tendency
  • Review Activity: Measures of Central Tendency

Topic 9B: Use Measures of Dispersion

  • Exam Objective: 3.1 Given a scenario, apply the appropriate descriptive statistical methods
  • Review Activity: Measures of Dispersion

Topic 9C: Use Frequency and Percentages

  • Exam Objective: 3.1 Given a scenario, apply the appropriate descriptive statistical methods
  • Video: Calculating Percentages
  • Review Activity: Frequency and Percentages

Module 10: Describing Key Analysis Techniques

Topic 10A: Get Started with Analysis

  • Exam Objective: 3.3 Summarize types of analysis and key analysis techniques
  • Review Activity: Get Started with Analysis

Topic 10B: Recognize Types of Analysis

  • Exam Objective: 3.3 Summarize types of analysis and key analysis techniques
  • Review Activity: Types of Analysis

Module 11: Understanding the Use of Different Statistical Methods

Topic 11A: Understand the Importance of Statistical Tests

  • Exam Objective: 3.2 Explain the purpose of inferential statistical methods
  • Video: Understanding the Importance of Statistical Tests
  • Review Activity: The Importance of Statistical Tests

Topic 11B: Break Down the Hypothesis Test

  • Exam Objective: 3.2 Explain the purpose of inferential statistical methods
  • Review Activity: The Hypothesis Test

Topic 11C: Understand Tests and Methods to Determine Relationships Between Variables

  • Exam Objective: 3.2 Explain the purpose of inferential statistical methods
  • Review Activity: Tests and Methods to Determine Relationships Between Variables

Module 12: Using the Appropriate Type of Visualization

Topic 12A: Use Basic Visuals

  • Exam Objective: 4.4 Given a scenario, apply the appropriate type of visualization
  • Review Activity: Basic Visuals

Topic 12B: Build Advanced Visuals

  • Exam Objective: 4.4 Given a scenario, apply the appropriate type of visualization
  • Video: Building and Reading Stacked Charts
  • Review Activity: Advanced Visuals

Topic 12C: Build Maps with Geographical Data

  • Exam Objective: 4.4 Given a scenario, apply the appropriate type of visualization
  • Review Activity: Maps with Geographical Data

Topic 12D: Use Visuals to Tell a Story

  • Exam Objective: 4.4 Given a scenario, apply the appropriate type of visualization
  • Review Activity: Visuals to Tell a Story

Module 13: Expressing Business Requirements in a Report Format

Topic 13A: Consider Audience Needs When Developing a Report

  • Exam Objective: 4.1 Given a scenario, translate business requirements to form a report
  • Review Activity: Audience Needs When Developing a Report

Topic 13B: Describe Data Source Considerations for Reporting

  • Exam Objective: 4.3 Given a scenario, use appropriate methods for dashboard development
  • Video: Accessing Source Data and Creating Reports
  • Review Activity: Data Source Considerations for Reporting

Topic 13C: Describe Considerations for Delivering Reports and Dashboards

  • Exam Objectives:
    • 4.1 Given a scenario, translate business requirements to form a report
    • 4.3 Given a scenario, use appropriate methods for dashboard development
  • Review Activity: Considerations for Delivering Reports and Dashboards

Topic 13D: Develop Reports or Dashboards

  • Exam Objectives:
    • 4.1 Given a scenario, translate business requirements to form a report
    • 4.3 Given a scenario, use appropriate methods for dashboard development
  • Video: Selecting Different Visualization Layouts
  • Review Activity: Develop Reports or Dashboards

Topic 13E: Understand Ways to Sort and Filter Data

  • Exam Objectives:
    • 4.1 Given a scenario, translate business requirements to form a report
    • 4.3 Given a scenario, use appropriate methods for dashboard development
  • Review Activity: Ways to Sort and Filter Data

Module 14: Designing Components for Reports and Dashboards

Topic 14A: Design Elements for Reports and Dashboards

  • Exam Objective: 4.2 Given a scenario, use appropriate design components for reports and dashboards
  • Video: Using Appropriate Design Components for Reports and Dashboards
  • Review Activity: Design Elements for Reports/Dashboards

Topic 14B: Utilize Standard Elements

  • Exam Objective: 4.2 Given a scenario, use appropriate design components for reports and dashboards
  • Review Activity: Standard Elements for Reports and Dashboards

Topic 14C: Creating a Narrative and Other Written Elements

  • Exam Objective: 4.2 Given a scenario, use appropriate design components for reports and dashboards
  • Review Activity: Narrative and Other Written Elements

Topic 14D: Understand Deployment Considerations

  • Exam Objective: 4.3 Given a scenario, use appropriate methods for dashboard development
  • Video: Deployment Considerations
  • Review Activity: Deployment Considerations

Module 15: Distinguishing Different Report Types

Topic 15A: Understand How Updates and Timing Affect Reporting

  • Exam Objective: 4.5 Compare and contrast types of reports
  • Review Activity: How Updates and Timing Affect Reporting

Topic 15B: Differentiate Between Types of Reports

  • Exam Objective: 4.5 Compare and contrast types of reports
  • Review Activity: Types of Reports

Module 16: Summarizing the Importance of Data Governance

Topic 16A: Define Data Governance

  • Exam Objective: 5.1 Summarize important data governance concepts
  • Video: Importance of Data Governance
  • Review Activity: Data Governance

Topic 16B: Understand Access Requirements and Policies

  • Exam Objective: 5.1 Summarize important data governance concepts
  • Review Activity: Access Requirements and Policies

Topic 16C: Understand Security Requirements

  • Exam Objective: 5.1 Summarize important data governance concepts
  • Review Activity: Security Requirements

Topic 16D: Understand Entity Relationship Requirements

  • Exam Objective: 5.1 Summarize important data governance concepts
  • Review Activity: Entity Relationship Requirements

Module 17: Applying Quality Control to Data

Topic 17A: Describe Characteristics, Rules, and Metrics of Data Quality

  • Exam Objective: 5.2 Given a scenario, apply data quality control concepts
  • Review Activity: Characteristics, Rules, and Metrics of Data Quality

Topic 17B: Identify Reasons to Quality Check Data and Methods of Data Validation

  • Exam Objective: 5.2 Given a scenario, apply data quality control concepts
  • Review Activity: Reasons to Quality Check Data and Methods of Data Validation

Module 18: Explaining Master Data Management Concepts

Topic 18A: Explain the Basics of Master Data Management

  • Exam Objective: 5.3
  • Video: Understanding Data Management
  • Review Activity: The Basics of Master Data Management

Topic 18B: Describe Master Data Management Processes

  • Exam Objective: 5.3 Explain master data management (MDM) concepts
  • Review Activity: Master Data Management Processes

Dates & Locations

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July 13, 2026 - July 17, 2026

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July 13, 2026 - July 17, 2026

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October 12, 2026 - October 16, 2026

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October 12, 2026 - October 16, 2026

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Trainocate exam and cert

Exam & Certification

CompTIA Data+ validates certified professionals have the skills required to facilitate data-driven business decisions, including:

  • Mining data
  • Manipulating data
  • Visualizing and reporting data
  • Applying basic statistical methods
  • Analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle

Training & Certification Guide

The CompTIA Data+ exam will certify the successful candidate has the knowledge and skills required to transform business requirements in support of data-driven decisions through mining and manipulating data, applying basic statistical methods, and analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle.

  • Exam Version: v2
  • Number of Questions: 90 questions
  • Type of Questions: Multiple choice and performance-based
  • Length of Test: 90 Minutes
  • Passing Score: 675 (on scale of 100–900)

CompTIA recommends 18–24 months of experience in a report/business analyst job role, exposure to databases and analytical tools, a basic understanding of statistics, and data visualization experience

Better Analyze and Interpret Data
Mine data more effectively. Analyze with rigor. Avoid confounding results.

Communicate Insights
Highlight what’s important. Produce reports that persuade, not confuse. Help the team make better data-driven decisions.

Demonstrate Competency
Make yourself a more valuable team member. Proof of data literacy means you’re more employable and more upwardly mobile.

Your CompTIA Data+ certification is good for three years from the date of your exam. The CE program allows you to extend your certification in three-year intervals through activities and training that relate to the content of your certification.

You can participate in a number of activities and training programs to renew your CompTIA Data+ certification. Collect at least 20 Continuing Education Units (CEUs) in three years and upload them to your certification account, and your CompTIA Data+ certification will automatically renew.

CT-DataSys+: CompTIA DataSys+

CompTIA DataSys+ is a foundational level data management certification that focuses on the logistics of data administration and security. It covers what database administrators (DBAs) need to deploy, manage and maintain systems, including employing the fundamentals of scripting and programming in a database environment while using security and business continuity best practices.

CT-CLOUD+: CompTIA Cloud+ Certification

The CompTIA Cloud+ certification is a highly sought-after credential that is recognized worldwide for validating the technical skills needed to deploy and automate secure cloud environments. This certification is designed to equip professionals with the expertise needed to support the high availability of business systems and data in the cloud.

CT-NETWORK+: CompTIA Network+ Certification

As one of the top IT certifications for beginners, the CompTIA Network+ credential is the essential certification for networking professionals. It covers the hands-on skills and precise knowledge needed in today’s networking environments. With this certification, you will be able to support networks on any platform.

CT-SECURITY+: CompTIA Security+ (Plus) Certification

The CompTIA Security+ credential is a global cybersecurity certification that validates the baseline skills necessary to perform core security functions and pursue an IT security career. It is the first security certification a candidate should earn. It establishes the core knowledge required of any cybersecurity role and provides a springboard to intermediate-level cybersecurity jobs.

Data Science Certifications for Higher Salaries in Malaysia

Curious about data science salaries in Malaysia? Well, according to Glassdoor, a data scientist can earn anywhere between RM 3,000 to RM 18,000 per month, with an average pay of RM6,000 in Kuala Lumpur. Our guide delves deeper, exploring how variables like your geographic location, years of experience, and professional certifications can either boost or alter your data science salary in Malaysia.

Top Data Science Certifications You Should Know

Which data science certifications will optimize your career potential in 2024? This analysis presents the most advantageous and industry-acknowledged certifications available today. Shortlist the top data science certifications and discern how each can directly impact your professional trajectory without wasting time on sales fluff. Keep reading to uncover which certifications align with your career objectives.

Data and AI: How AI is Used in Data Analytics

In the age of digital transformation, data is the new gold. It’s not just about gathering data, but about making sense out of it. Here’s where Artificial Intelligence (AI) steps in, revolutionizing the field of data analysis and turning raw data into valuable insights that drive strategic decision-making. But how is AI used in data analysis? Let’s dive in and explore.

AI Mastery Program: Learn AI with Microsoft

Artificial Intelligence (AI) has been one of the hottest topics in the tech industry for the past decade. With its rapid advancements and potential to impact our daily lives, learning AI has become a highly sought-after skill.

In addition, Microsoft has also announced during its Microsoft Build: AI Day 2024 event that it will will invest RM10.5 billion over the next four years to support Malaysia’s digital transformation – the single largest investment in its 32-year history in the country. Its investment includes building cloud and AI infrastructure in Malaysia as well as creating AI skilling opportunities for an additional 200,000 people in the country.

As such, we’re thrilled to announce the launch of our Microsoft AI Mastery Program in 2024, made possible through our partnership with Microsoft, a renowned leader in AI technology. This program is tailored for individuals and tech professionals pass

5 Job Opportunities with CompTIA Data+ Certification

The expertise to analyze data is at the heart of many of our emerging technologies. As the importance of data analytics grows, more job roles are required to set context and better communicate vital business intelligence.

How to Become a Data Analyst: Step by Step Guide

If you have a passion for numbers, a keen attention to detail, and a strong analytical mindset, a rewarding career as a data analyst could be the ideal path for you.

Become a Data Analyst with CompTIA Data+

If you enjoy working with numbers and exploring data sets, but also have a creative side and a curious nature, a career as a data analyst might be a great fit for you.

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CompTIA certifications are designed to validate IT professionals skills and knowledge in various areas of information technology, from computer hardware and networking to cybersecurity and cloud computing.

The Ultimate Guide to the Top Data and AI Skills for 2026

This guide serves as your comprehensive roadmap to navigating this new landscape. We will deconstruct the four essential pillars of Data and AI mastery for 2026, providing a clear blueprint for a future-proof career.

This cornerstone article will provide a high-level strategic overview, setting the stage for our deep-dive blog series that will explore each pillar in granular detail.

Mastering the Foundational Data Analytics Skills That Get You Hired in 2026

This article is your deep dive into the three non-negotiable, must-have data analytics skills that will get you hired in 2026.

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Frequently Asked Questions

Data Specialists are tasked with overseeing the development of new databases, monitoring database performance and interpreting raw data and turning it into usable feedback and applications. A Data Specialist knows what all the numbers and patterns mean and can communicate the findings in layman’s terms.

In addition to drawing meaning from raw data, these individuals are also charged with collecting and cleaning up the data ensuring that the information is accurate and without error. Some Database Administrators specialize in specific systems or applications.

Making sure confidential information is secure and available to those who need it is a top priority of Data Specialists.

Data-related jobs are projected to be among the fastest-growing occupations through the balance of this decade, with some positions, such as data scientists, expected to grow up to five times faster than other occupations.

But the need for core data skills extends beyond data-specific job roles. Employers listed over 1 million job postings over the past 12 months seeking candidates with skills in data analysis, data mining or data visualization.

CompTIA Data+ can help you develop and refine these skills and prove you have the hands-on experience employers are looking for.

Data analytics helps people make sense of raw data. Companies use data to improve their internal operations and to better understand their customers. Typically, this is done in three stages.

The three stages of data use include:

  1. Collect and store: Customer transactions, social media streams, M2M sensors and business activity
  2. Process and organize: Calculate, transform, process, protect and augment
  3. Analyze and visualize: Dashboards, reports, predictive analytics and ad-hoc queries

Let’s take a look at how each stage works and how each one feeds into the larger umbrella we call data analytics.

Data Infrastructure

The individuals responsible for building a data infrastructure are the people designing, building and maintaining the technology needed to collect and store data. This is the first stage and requires the skills of storage engineers, business intelligence (BI) developers, data warehouse analysts, network administrators, server administrators and data architects.

Data Management and Processing

Once the data is collected and securely stored, companies need qualified IT pros to design and maintain how the flow of data is then processed and organized within the organization. Database administrators, systems analysts, developers, and data architects and engineers are some of the job titles that fit the bill.

Data Interpretation and Visualization

The final stage of data use is interpretating it for actionable insights and decision making. This advanced stage requires advanced analytics. For example, data scientists use sophisticated data modeling techniques to build new datasets. Roles like business analyst and data analyst also work in this stage.

One part of the data use process doesn’t exist without the other. When these stages come together, a comprehensive data analytics program benefits the entire organization, from marketing campaigns and sales goals to research, development and finance.

Simply put, organizations have been collecting data for quite some time. But poor data management or insufficient data analysis is impacting the bottom line. Most companies have data silos in every department, limiting the ability to build a holistic view of corporate data. Moreover, new data sources – like social media and smart devices – necessitate a more efficient structure and analytics process.

Data is a critical resource that needs to be handled properly. A solid understanding of the data function, techniques and tools provides the context for building a comprehensive big data analytics strategy.

CompTIA Data+ is an early-career data analytics certification for professionals tasked with developing and promoting data-driven business decision-making. That means that you can make yourself more employable by learning how to better analyze and interpret data, communicate insights and demonstrate competency. CompTIA Data+ validates certified professionals have the skills required to facilitate data-driven business decisions, including:

  • Mining data
  • Manipulating data
  • Visualizing and reporting data
  • Applying basic statistical methods
  • Analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle

You can expect performance-based and multiple-choice questions across five domains:

  • Data Concepts and Environments (15%)
  • Data Mining (25%)
  • Data Analysis (23%)
  • Visualization (23%)
  • Data Governance, Quality and Controls (14%)

These domains relate back to job roles that are tasked with developing and promoting data-driven business decision-making.

For example, your organization collects all sorts of data, but is it easy to parse through that data, analyze the data and make sense of it? This is where data visualization comes into play. Telling the story of your data and getting stakeholders to understand is a valuable skill that employers want.

As the importance of data analytics grows, more job roles are required to set context and better communicate vital business intelligence. Collecting, analyzing and reporting on data can drive priorities and lead business decision-making. The following data and analytics primary job roles align with CompTIA Data+:

  • Data analyst
  • Clinical analyst
  • Reporting analyst
  • Marketing analyst
  • Business data analyst
  • Operations analyst
  • Business intelligence analyst

CompTIA Data+ is an ideal certification for not only data-specific careers, but also other career paths that benefit from analytics processes and data analytics knowledge. Jobs like marketing specialists, financial analysts, human resource analysts or clinical health care analysts can optimize performance and make well-informed decisions when they use and evaluate data correctly.

CompTIA Data+ is the only data analyst certification that covers baseline data analytics skills, assesses hands-on abilities and is vendor neutral and tool agnostic.

Vendor-neutral certifications provide analyst skills needed to perform various job roles regardless of the specific programs and tools being used. Compare this to vendor-specific certifications, which equip you to work with just one platform (such as SAS, Tableau or Microsoft). There are also certifications that dive deeper, such as big data and business analytics that can be pursued after receiving CompTIA Data+.

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