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CompTIA Data+ (DA0-001)
This course, CompTIA Data+ DA0-001, aims to provide students with the basic skills needed for data analysis and interpretation as well as for converting data into practical insights. It encompasses a broad spectrum of subjects, including the comprehension of data concepts and environments, as well as data governance and quality control.
Obtaining the CompTIA Data+ certification confirms a professional’s data fluency, which makes the CompTIA DA0-001 course an essential resource for individuals looking to progress in data analysis.
Overview
The CompTIA Data+ (DA0-001) certification demonstrates your expertise and capability in performing data analytics to foster and advance data-informed business decisions within your organization. It is a vendor-neutral certification. This certification assesses your skills in analyzing and interpreting data more effectively, conveying insights, and showing proficiency in data analytics. The CompTIA Data+ exam targets data analysts in the early stages of their careers who possess the equivalent of 12 to 24 months of practical experience.
The CompTIA Data+ exam centers on data analytics to validate and convey essential business intelligence through the collection, analysis, and reporting of data that can influence an organization’s priorities and business decisions.
What you will Learn in this CompTIA Data+ (DA0-001)
course?
- Understand and identify the fundamental concepts of data schemas and the significance of different dimensions in data modeling.
- Differentiate between various data types and comprehend the implications of each in data processing and storage.
- Recognize and compare common data structures and file formats to select the most appropriate for a given scenario.
- Grasp data acquisition concepts and apply best practices for collecting and importing data from diverse sources.
- Understand the necessity for data cleansing and profiling to ensure accuracy and reliability in datasets.
- Execute data manipulation techniques, including cleaning, transforming, and enriching data to meet analytical requirements.
- Apply descriptive statistical methods to summarize and describe dataset characteristics effectively.
- Comprehend the purpose and application of inferential statistical methods to make predictions or decisions based on data sampling.
- Utilize various analysis techniques and analytics tools to extract insights and support business objectives.
- Design and develop effective reports and dashboards by translating business requirements, choosing appropriate design components, and applying suitable visualization types.
Who should go for CompTIA Data+ (DA0-001) course?
- Data Analysts
- Business Analysts
- Marketing Analysts
- Operations Analysts
- Entry-level Data Scientists
- IT Professionals seeking to transition into data roles
- Database Administrators
- Project Managers who handle data-driven projects
- Data Consultants who provide strategic data insights and recommendations
- Data Governance and Quality Officers
Our Package
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
Compare and contrast different data types.
• Date
• Numeric
• Alphanumeric
• Currency
• Text
• Discrete vs. continuous
• Categorical/dimension
• Images
• Audio
• Video
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)
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
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
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
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
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
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
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
Identify common data analytics 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
Given a scenario, translate business requirements to form a report.
• Data content
• Filtering
• Views
• Date range
• Frequency
• Audience for report
– Distribution list
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
– Frequently asked questions (FAQs)
– Appendix
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
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
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
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
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
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
Upcoming Batch
April 20th (Weekends)
FRI & SAT (4 Weeks)
08:30 PM to 01:00 AM (CDT)
April 18th (Weekdays)
MON – FRI (18 Days)
10:00 AM to 12:00 PM (CDT)
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