CompTIA Data+

What’s included?

• Authorized Courseware
• Intensive Hands on Skills Development with an Experienced Subject Matter Expert
• Hands-on practice on real Servers and extended lab support 1.800.482.3172
• Examination Vouchers & Onsite Certification Testing- (excluding Adobe and PMP Boot Camps)
• Academy Code of Honor: Test Pass Guarantee
• Optional: Package for Hotel Accommodations, Lunch and Transportation

Curriculum

1.0 Data Concepts and Environments

1.1 Identify basic concepts of data schemas and dimensions.

• Databases
– Relational
– Non-relational
• Data mart/data warehousing/data lake
– Online transactional processing (OLTP)
– Online analytical processing (OLAP)
• Schema concepts
– Snowflake
– Star
• Slowly changing dimensions
– Keep current information
– Keep historical and current information

1.2 Compare and contrast different data types.

• Date
• Numeric
• Alphanumeric
• Currency
• Text
• Discrete vs. continuous
• Categorical/dimension
• Images
• Audio
• Video

1.3 Compare and contrast common data structures and file formats.

• Structures
– Structured
– Defined rows/columns
– Key value pairs
– Unstructured
– Undefined fields
– Machine data
• Data file formats
– Text/Flat file
– Tab delimited
– Comma delimited
– JavaScript Object Notation (JSON)
– Extensible Markup Language (XML)
– Hypertext Markup Language (HTML)

2.0 Data Mining

2.1 Explain data acquisition concepts.

• Integration
– Extract, transform, load (ETL)
– Extract, load, transform (ELT)
– Delta load
– Application programming interfaces (APIs)
• Data collection methods
– Web scraping
– Public databases
– Application programming interface (API)/web services
– Survey
– Sampling
– Observation

2.2 Identify common reasons for cleansing and profiling datasets.

• Duplicate data
• Redundant data
• Missing values
• Invalid data
• Non-parametric data
• Data outliers
• Specification mismatch
• Data type validation

2.3 Given a scenario, execute data manipulation techniques. 

• Recoding data
– Numeric
– Categorical
• Derived variables
• Data merge
• Data blending
• Concatenation
• Data append
• Imputation
• Reduction/aggregation
• Transpose
• Normalize data
• Parsing/string manipulation

2.4 Explain common techniques for data manipulation and query optimization.

• Data manipulation
– Filtering
– Sorting
– Date functions
– Logical functions
– Aggregate functions
– System functions
• Query optimization
– Parametrization
– Indexing
– Temporary table in the query set
– Subset of records
– Execution plan

3.0 Data Analysis

3.1 Given a scenario, apply the appropriate descriptive statistical methods.

• Measures of central tendency
– Mean
– Median
– Mode
• Measures of dispersion
– Range
– Max
– Min
– Distribution
– Variance
– Standard deviation
• Frequencies/percentages
• Percent change
• Percent difference
• Confidence intervals

3.2 Explain the purpose of inferential statistical methods.

• t-tests
• Z-score
• p-values
• Chi-squared
• Hypothesis testing
– Type I error
– Type II error
• Simple linear regression
• Correlation

3.3 Summarize types of analysis and key analysis techniques.

• Process to determine type of analysis
– Review/refine business questions
– Determine data needs and sources to perform analysis
– Scoping/gap analysis
• Type of analysis
– Trend analysis
– Comparison of data over time
– Performance analysis
– Tracking measurements against defined goals
– Basic projections to achieve goals
– Exploratory data analysis
– Use of descriptive statistics to determine observations
– Link analysis
– Connection of data points or pathway

3.4 Identify common data analytics tools.(The intent of this objective is NOT to test specific vendor feature sets nor the purposes of the tools.

• Structured Query Language (SQL)
• Python
• Microsoft Excel
• R
• Rapid mining
• IBM Cognos
• IBM SPSS Modeler
• IBM SPSS
• SAS
• Tableau
• Power BI
• Qlik
• MicroStrategy
• BusinessObjects
• Apex
• Dataroma
• Domo
• AWS QuickSight
• Stata
• Minitab

4.0 Visualization

4.1 Given a scenario, translate business requirements to form a report.

• Data content
• Filtering
• Views
• Date range
• Frequency
• Audience for report
– Distribution list

4.2 Given a scenario, use appropriate design components for reports and dashboards.

• Report cover page
– Instructions
– Summary
– Observations and insights
• Design elements
– Color schemes
– Layout
– Font size and style
– Key chart elements
– Titles
– Labels
– Legends
– Corporate reporting standards/style guide
– Branding
– Color codes
– Logos/trademarks
– Watermark
• Documentation elements
– Version number
– Reference data sources
– Reference dates
– Report run date
– Data refresh date

4.3 Given a scenario, use appropriate methods for dashboard development.

• Dashboard considerations
– Data sources and attributes
– Field definitions
– Dimensions
– Measures
– Continuous/live data feed vs. static data
– Consumer types
– C-level executives
– Management
– External vendors/stakeholders
– General public
– Technical experts
• Development process
– Mockup/wireframe
– Layout/presentation
– Flow/navigation
– Data story planning
– Approval granted
– Develop dashboard
– Deploy to production
• Delivery considerations
– Subscription
– Scheduled delivery
– Interactive (drill down/roll up)
– Saved searches
– Filtering
– Static
– Web interface
– Dashboard optimization
– Access permissions

4.4 Given a scenario, apply the appropriate type of visualization.

• Line chart
• Pie chart
• Bubble chart
• Scatter plot
• Bar chart
• Histogram
• Waterfall
• Heat map
• Geographic map
• Tree map
• Stacked chart
• Infographic
• Word cloud

4.5 Compare and contrast types of reports.

• Static vs. dynamic reports
– Point-in-time
– Real time
• Ad-hoc/one-time report
• Self-service/on demand
• Recurring reports
– Compliance reports (e.g., financial, health, and safety)
– Risk and regulatory reports
– Operational reports [e.g., performance, key performance indicators (KPIs)]
• Tactical/research report

5.0 Data Governance, Quality, and Controls

5.1 Summarize important data governance concepts.

• Access requirements
– Role-based
– User group-based
– Data use agreements
– Release approvals
• Security requirements
– Data encryption
– Data transmission
– De-identify data/data masking
• Storage environment requirements
– Shared drive vs. cloud based vs. local storage
• Use requirements
– Acceptable use policy
– Data processing
– Data deletion
– Data retention
• Entity relationship requirements
– Record link restrictions
– Data constraints
– Cardinality
• Data classification
– Personally identifiable information (PII)
– Personal health information (PHI)
– Payment card industry (PCI)
• Jurisdiction requirements
– Impact of industry and governmental regulations
• Data breach reporting
– Escalate to appropriate authority

5.2 Given a scenario, apply data quality control concepts.

• Circumstances to check for quality
– Data acquisition/data source
– Data transformation/intrahops
– Pass through
– Conversion
– Data manipulation
– Final product (report/dashboard, etc.)
• Automated validation
– Data field to data type validation
– Number of data points
• Data quality dimensions
– Data consistency
– Data accuracy
– Data completeness
– Data integrity
– Data attribute limitations
• Data quality rule and metrics
– Conformity
– Non-conformity
– Rows passed
– Rows failed
• Methods to validate quality
– Cross-validation
– Sample/spot check
– Reasonable expectations
– Data profiling
– Data audits

5.3 Explain master data management (MDM) concepts.

• Processes
– Consolidation of multiple data fields
– Standardization of data field names
– Data dictionary
• Circumstances for MDM
– Mergers and acquisitions
– Compliance with policies and regulations
– Streamline data access