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1D0-622 Exam Topics and Domains
1D0-622 is organized into 5 weighted domains.
Fundamentals of Data Analysis
Obtaining Quality Source Data
Describe the importance of obtaining quality source data, including capturing, using and analyzing institutional knowledge (i.e., tribal knowledge).
Data Structure Types
Describe the differences in various types of data structures including: files, lists, arrays, records, trees and tables.
Centralizing Organizational Data
Explain the benefits of centralizing an organization's data in one application (e.g., Salesforce, NetSuite).
Structured vs. Unstructured Data
Compare and contrast structured (e.g., databases) and unstructured data (e.g., audio, video, social media feeds, etc.), and explain how to rapidly analyze both to maximize insight through analysis.
Types of Data and Sources of Business Data
Identify different types of data, including open, public, administrative and research data and sources of business data, including mobile platforms, traditional e-commerce sites, social media, sales, accounting, marketing, customers and partners.
Search Engine Optimization (SEO) Fundamentals
Explain the fundamentals of Search Engine Optimization (SEO), including analyzing for multiple channels (e.g., email, Twitter, Facebook, LinkedIn and offline channels).
Data Life Cycle Management
Explain the data life cycle of an information system: from creation and initial storage to being obsolete and deleted.
Data Protection and Privacy Laws
Identify local and international data protection and data privacy laws and regulations, how to maintain compliance within data collection and analysis infrastructure, and why compliance is necessary.
Data Analysis Process Steps
Describe data analysis routine steps including: problem hypothesis, what to measure, collecting data, cleansing data, model data, visualize data, analyze data, interpret results and documenting and communicating results.
Asking the Right Questions
Explain why the key to effective data analysis is asking the right questions in the requirements provides answers to business problems, and not just the data itself.
Introduction to Big Data
Big Data Fundamentals
Describe the term big data.
IT Data Management
Explain the importance of IT data management, including ethics and security.
IT Business Environments and Cloud-Based Data
Describe the various IT business environments, data architecture and the nature of working with cloud-based data, including rules, policies, standards and models of how data is used, stored and managed.
Cloud-Native Data
Explain how to work with cloud-native data.
In-House Data
Explain how to work with in-house data.
Cloud Migration
Determine when it makes sense to migrate in-house data to the cloud and how to do it.
Databases for Data Analysis
Describe typical databases used for data analysis, including Oracle, MS-SQL, MySQL and Access.
Data-Driven Business Decisions
- Given a scenario, analyze data to make business decisions such as building a case for change
- exploring, generating, and testing business assumptions
- and using historical data to analyze trends.
Impact of Data Errors
Explain how minor data errors can cause incorrect results for data analysis.
Organizational Strategy and Data Quality
Explain how defining an organizational strategy, improving data quality and applying statistical programming in preparing data directly benefit the data analytics and improve business decisions.
Database Implementation and Maintenance
Describe how to implement a database from a data model and why database maintenance and backup is necessary.
Working with Data Sources
Obtaining Data from Organization Departments
Explain how to obtain data by working with various organization departments, including customer service, marketing and sales.
Customer Relationship Management (CRM)
Describe the purpose of Customer Relationship Management (CRM).
CRM and Customer Service Integration
Given a scenario, determine how to integrate CRM and customer service.
Data from Email and User Forums
Explain how to obtain data from email and user forums.
Data from Knowledge Bases
Describe how to access and obtain data from knowledge bases, including Facebook, Twitter, LinkedIn, enterprise resource management systems and accounting sources.
CRM and B2B Frameworks
Determine how to obtain data from CRM and business-to-business frameworks.
Transaction, Payment, and Inventory Data
Given a scenario, analyze transaction, payment and inventory data from various data sources.
Business Decisions from Multiple Data Sources
Given a scenario, make business decisions using multiple data sources.
Tools for Capturing and Analyzing Data
Data Capture Tools
Describe various tools to capture data required for data analysis, including Tableau Public, Google Fusion Tables and OpenRefine.
Hadoop-Based Environments
Explain how to capture and analyze data from Hadoop-based environments.
The R Project
Describe how the R Project enables data analysts to statistically explore data sets and create graphical displays.
Additional Data Analysis Software
Describe additional software for data capture and analysis, including Rapid Miner and KNIME.
Analyzing and Reporting Data
Network Traffic Analysis
Analyze network traffic from typical sources, such as Web logs, marketing, technical support, customer relations and sales.
Data Integration and Programming Efficiency
Explain how the nature of data volumes being processed through data integrations using programming approaches make data analysis more efficient.
Data Testing and Validation
Describe how proper testing is essential to ensure unified data sets are correct, complete and up to date.
Statistical Computing Languages for Data Integration
Explain how programming languages for statistical computing can be applied to data integration activities for improved speed, quality and data integration for better analysis.
Organizational Efforts and Business Outcomes
Given a scenario, determine relationships between organizational efforts and business outcomes, such as the progress of efforts against business goals.
Best Methods for Data Capture and Reporting
Given a scenario, identify the best methods to capture and report specific data.
Dashboards for Data Analysis and Reporting
Create a dashboard for data analysis and reporting, including executive summaries.
Reports and Charts Using Office Tools
Create reports and charts for reporting data using office tools, including word processors, spreadsheets, databases, Web-based software.
Presentations for Reporting Data
Create presentations for reporting data using tools such as PowerPoint and Webcasts.
Frequently Asked Questions for Presentations
Create a list of Frequently Asked Questions (FAQ) to accompany a presentation.
Ethics in Data Presentation
Explain the need for ethics in presenting data to avoid personal or organizational bias.
Ethics as a Core Data Analyst Responsibility
Describe how ethics are a vital part of the Data Analyst responsibilities.
How do I earn this certification?
Passing 1D0-622 earns the CIW Data Analyst Specialist certification. It sits in the Web and Mobile Design Series / Database Professional Series track.
- 1D0-184 - CIW AI Data Science SpecialistAdvanced data science skills with AI focus, complementary to data analysis
- 1D0-181 - CIW Artificial Intelligence AssociateAI foundational knowledge that complements data analysis skills
- 1D0-735 - CIW JavaScript SpecialistProgramming skills for web-based data analysis and visualization
- 1D0-761 - CIW Web Security AssociateSecurity knowledge for data protection and privacy compliance
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How to study for this exam?
The most effective way to prepare for 1D0-622 is by using the PlanetCert Simulator to practice questions and review detailed explanations.
What's changed on this exam?
- ACTIVE
- Last content update: v1.2 (current version)
- Google Fusion Tables Still referenced in official exam objectives; course also covers Google BigQuery as a modern alternative. Candidates should understand both.
- Tableau Public Current Core tool covered in Domain 4; remains relevant for data visualization.
- R Project / RStudio Current Core statistical programming tool in Domain 4. Exam still references R Project and RStudio.
- KNIME Current Referenced in Domain 4 as additional data analysis software.
- Hadoop Current Core topic in Domain 4; candidates must understand Hadoop-based environments.
Who should take this exam?
This exam is typically taken by High school and college students and Technical/trade school students.
- Foundational knowledge of spreadsheets
- Basic understanding of databases
- Basic coding skills
- Understanding of business processes
- Familiarity with websites