CompTIA DataAI Certification Prep
- Course Code G020
- Duration 5 days
Course Delivery
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Course Delivery
This course is available in the following formats:
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Company Event
Event at company
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Public Classroom
Traditional Classroom Learning
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Virtual Learning
Learning that is virtual
Request this course in a different delivery format.
Course Overview
TopThe CompTIA DataAI exam was developed to meet an industry need, and bridge gaps in training and knowledge for data science professionals. The exam objectives include real-world scenarios where learners apply their knowledge, ensuring they are prepared for various challenges they may face in their careers as data scientists. Based on extensive input from experienced data scientists, DataAI validates successful candidates can properly utilize and analyze vast amounts of data, extract meaning and generate actionable insights.
Organizations are rapidly enabling their workforces with AI assistants. However many are finding that simply giving employees access to these tools does not mean that employees have the ability to use these tools to their fullest potential. This “AI Fluency Gap” is preventing organizations from achieving the desired return on their AI investments.
CompTIA DataAI (DY0-001) can broaden your knowledge for a diverse range of career paths, creating opportunities for advancement and specialization in the rapidly evolving data science industry. With CompTIA DataAI, possible career paths include data scientist, quantitative analyst, machine learning engineer/specialist, predictive analyst, and artificial intelligence (AI) engineer.
Company Events
These events can be delivered exclusively for your company at our locations or yours, specifically for your delegates and your needs. The Company Events can be tailored or standard course deliveries.
Course Schedule
TopTarget Audience
TopThis course is designed for data science professionals who are seeking to validate and expand their skillsets.
Primary job roles
- Data Scientist
Course Objectives
TopSuccessful DataAI candidates gain the skills and knowledge required to:
- Understand and implement data science operations and processes.
- Apply mathematical and statistical methods appropriately and understand the importance of data processing and cleaning, statistical modeling, linear algebra and calculus concepts.
- Apply machine learning models and understand deep learning concepts.
- Utilize appropriate analysis and modeling methods and make justified model recommendations.
- Demonstrate understanding of industry trends and specialized data science applications.
Course Content
Top1.0 Illustrating the Data Science Lifecycle
2.0 Analyzing Business Problems
3.0 Collecting Data
4.0 Cleaning and Preparing Data
5.0 Describing Data Features
6.0 Exploring Data
7.0 Navigating the Model Selection Process
8.0 Employing Machine Learning Methods
9.0 Experimenting with Deep Learning
10.0 Evaluating and Refining Data Models
11.0 Communicating for Business Impact
12.0 Deploying Data Models
13.0 Discovering Specialized Data Science Applications
A.0 CompTIA DataAI DY0-001 Practice Test
Course Prerequisites
Top- Having a minimum of five years of hands-on experience as a data scientist or the equivalent knowledge is strongly recommended.
Test Certification
TopCompTIA DataAI Certification (Exam DY0-001)
Further Information
TopCertMaster Perform for DataAI DY0-001 brings together narrative instructional content, videos, performance-based questions (PBQs), skills assessments, live labs, and more to offer a comprehensive learning experience to prepare candidates for their CompTIA certification exam and validate their expertise in the field of data science. For data scientists with five or more years of experience, this course recognizes that it’s not necessary to start from the beginning; instead, it’s designed to identify knowledge gaps and provide learning content to get candidates up to speed on expert-level topics.
The course starts with a pre-assessment to help the learner identify areas of strengths and weaknesses. Instruction is centered around a learning progression model to help students learn concepts and actively build skills related to the course objectives. This learning methodology uses a series of steps to contextualize the material, elaborate on areas where additional instruction is needed, and provide relevance through practice and customized feedback.
The course is broken into modules and lessons, and different activities throughout the course take learning to the next level by providing hands-on skills practice and personalized instruction including:
- Lab activities in live environments for hands-on practice
- Technologies such as Jupyter notebooks, SQL, R, and Python
- Videos are integrated into the text to provide instruction and illustration at the point where it's the most relevant
- A variety of assessments are included throughout the course, including pre-assessments, module quizzes, and a certification practice test