1.1 Describe the importance of obtaining quality source data, including capturing, using and analyzing institutional knowledge (i.e., tribal knowledge).
1.2 Describe the differences in various types of data structures including: files, lists, arrays, records, trees and tables.
1.3 Explain the benefits of centralizing an organization's data in one application (e.g., Salesforce, NetSuite).
1.4 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.
1.5 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.
1.6 Explain the fundamentals of Search Engine Optimization (SEO), including analyzing for multiple channels (e.g., email, Twitter, Facebook, LinkedIn and offline channels).
1.7 Explain the data life cycle of an information system: from creation and initial storage to being obsolete and deleted.
1.8 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.
1.9 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.
1.10 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.
2.1 Describe the term big data.
2.2 Explain the importance of IT data management, including ethics and security.
2.3 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.
2.4 Explain how to work with cloud-native data.
2.5 Explain how to work with in-house data.
2.6 Determine when it makes sense to migrate in-house data to the cloud and how to do it.
2.7 Describe typical databases used for data analysis, including Oracle, MS-SQL, MySQL and Access.
2.8 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.
2.9 Explain how minor data errors can cause incorrect results for data analysis.
2.10 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.
2.11 Describe how to implement a database from a data model and why database maintenance and backup is necessary.
3.1 Explain how to obtain data by working with various organization departments, including customer service, marketing and sales.
3.2 Describe the purpose of Customer Relationship Management (CRM).
3.3 Given a scenario, determine how to integrate CRM and customer service.
3.4 Explain how to obtain data from email and user forums.
3.5 Describe how to access and obtain data from knowledge bases, including Facebook, Twitter, LinkedIn, enterprise resource management systems and accounting sources.
3.6 Determine how to obtain data from CRM and business-to-business frameworks.
3.7 Given a scenario, analyze transaction, payment and inventory data from various data sources.
3.8 Given a scenario, make business decisions using multiple data sources.
4.1 Describe various tools to capture data required for data analysis, including Tableau Public, Google Fusion Tables and OpenRefine.
4.2 Explain how to capture and analyze data from Hadoop-based environments.
4.3 Describe how the R Project enables data analysts to statistically explore data sets and create graphical displays.
4.4 Describe additional software for data capture and analysis, including Rapid Miner and KNIME.
5.1 Analyze network traffic from typical sources, such as Web logs, marketing, technical support, customer relations and sales.
5.2 Explain how the nature of data volumes being processed through data integrations using programming approaches make data analysis more efficient.
5.3 Describe how proper testing is essential to ensure unified data sets are correct, complete and up to date.
5.4 Explain how programming languages for statistical computing can be applied to data integration activities for improved speed, quality and data integration for better analysis.
5.5 Given a scenario, determine relationships between organizational efforts and business outcomes, such as the progress of efforts against business goals.
5.6 Given a scenario, identify the best methods to capture and report specific data.
5.7 Create a dashboard for data analysis and reporting, including executive summaries.
5.8 Create reports and charts for reporting data using office tools, including word processers, spreadsheets, databases, Web-based software.
5.9 Create presentations for reporting data using tools such as PowerPoint and Webcasts.
5.10 Create a list of Frequently Asked Questions (FAQ) to accompany a presentation.
5.11 Explain the need for ethics in presenting data to avoid personal or organizational bias.
5.12 Describe how ethics are a vital part of the Data Analyst responsibilities.