CompTIA Data+

Description

Duration: 5 Days

About the Course

The CompTIA Data+ exam will certify the successful candidate has the knowledge and skills required to transform business requirements in support of data-driven decisions through mining and manipulating data, applying basic statistical methods, and analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle.

Audience Profile

CompTIA recommends exposure to databases and analytical tools, a basic understanding of statistics, and data visualization experience

Learning Objective

Data Concepts and Environments – Boost your knowledge in identifying basic concepts of data schemas and dimensions while understanding the difference between common data structures and file formats

Data Mining – Grow your skills to explain data acquisition concepts, reasons for cleansing and profiling datasets, executing data manipulation, and understanding techniques for data manipulation

Data Analysis – Gain the ability to apply the appropriate descriptive statistical methods and summarize types of analysis and critical analysis techniques

Visualization – Learn how to translate business requirements to form the appropriate visualization in the form of a report or dashboard with the proper design components

Data Governance, Quality, & Controls – Increase your ability to summarize important data governance concepts and apply data quality control concepts

Certification Exam

This training course prepares students for EXAM CODE DA0-001

Prerequisites

18–24 months of hands-on experience working in a business intelligence
report/data analyst job role

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

With several convenient training delivery methods offered, The Academy makes getting the training you need easy. Whether you prefer to learn in a classroom or an online live learning virtual environment, training videos hosted online, and private group classes hosted at your site. We offer expert instruction to individuals, government agencies, non-profits, and corporations. Our live classes, on-sites, and online training videos all feature certified instructors who teach a detailed curriculum and share their expertise and insights with trainees. No matter how you prefer to receive the training, you can count on The Academy for an engaging and effective learning experience.

Methods

  • Instructor-Led (the best training format we offer)
  • Live Online Classroom – Online Instructor-Led
  • Self-Paced Video

Speak to an Admissions Representative for complete details

StartFinishPublic PricePublic EnrollPrivate PricePrivate Enroll
12/20/202112/24/2021
01/10/202201/14/2022
01/31/202202/04/2022
02/21/202102/25/2022
03/14/202203/18/2022
04/04/202204/08/2022
04/25/202204/29/2022
05/16/202205/20/2022
06/06/202206/10/2022
06/27/202207/01/2022
07/18/202207/22/2022
08/08/202208/12/2022
08/29/202209/02/2022
09/19/202209/23/2022
10/10/202210/14/2022
10/31/202211/04/2022
11/21/202211/25/2022
12-12-202212-16-2022

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