CompTIA DATA+ Training

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CompTIA Data+ gives you the confidence to bring data analysis to life. As the importance for data analytics grows, more job roles are required to set context and better communicate vital business intelligence. Collecting, analyzing, and reporting on data can drive priorities and lead business decision-making.

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CompTIA DATA+ KEY FEATURES

Training Delivery Options

SELF PACED TRAINING

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CORPORATE TRAINING

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SELF PACED TRAINING

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$599

LIVE BOOTCAMP

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$999

CORPORATE TRAINING

What is included?

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April 23, 2022

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09:00am - 01:00pm (EST)

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April 25, 2022

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10:00am - 06:00pm (EST)

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April 23, 2022

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07:00pm - 11:00pm (EST)

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Start Date – April 23, 2022

End Date – May 22, 2022

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09:00am – 01:00pm (EST)

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Start Date – April 25, 2022

End Date – April 29, 2022

Start/End Time
10:00am – 06:00pm (EST)

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Training Mode – Live Online

Batch Status[ Open ]

Start Date – April 23, 2022

End Date – May 22, 2022

Start/End Time
07:00pm – 11:00pm (EST)

Batch Type – Weekend Evening

Training Mode – Live Online

Batch Status[ Open ]

CompTIA DATA+ Certification Benefits

Organizations That Hire CompTIA DATA+ Certified

Our World-Class Certified  Instructor

Benefits You Will Access

Why Choose CareerMaker Solution

  • 100% passing Guarantee.

  • Fully Customized Training

  • Certified & 20 + Years of Experienced Trainers

  • 100% Money Back And Satisfaction Guarantee

  • 24*7 Support Dedicated Account Manager

  • Original Study Materials

  • Exam Protection

  • Unlimited Retakes for Life Time

  • Access to 100+ recorded Classes

  • Blended learning delivery model (self-paced eLearning and/or instructor-led options)

  • Cloud Based Learning Management System (LMS)

  • Guaranteed unbeatable pricing

CompTIA DATA+  Course Description

As the importance for data analytics grows, more job roles are required to set context and better communicate vital business intelligence. Collecting, analyzing, and reporting on data can drive priorities and lead business decision-making. CompTIA Data+ validates certified professionals have the skills required to facilitate data-driven business decisions, including:

  • Mining data
  • Manipulating data
  • Visualizing and reporting data
  • Applying basic statistical methods
  • Analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle
  • Mining data
  • Manipulating data
  • Visualizing and reporting data
  • Applying basic statistical methods
  • Analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle

CompTIA recommends 18–24 months of experience in a report/business analyst job role, exposure to databases and analytical tools, a basic understanding of statistics, and data visualization experience

Target Audience

CompTIA DATA+ Certification Prerequisites

CompTIA recommends 18–24 months of experience in a report/business analyst job role, exposure to databases and analytical tools, a basic understanding of statistics, and data visualization experience

Course Curriculum

Identify basic concepts of data schemas and dimensions. Compare and contrast different data types. Compare and contrast common data structures and file formats. 1.1

  • Databases – Relational – Non-relational
  • Data mart/data warehousing/data lake – Online transactional processing (OLTP) – Online analytical processing (OLAP)
  • Schema concepts – Snowfla
  • 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

(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
  • 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

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

Data Governance, Quality, and Controls

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

About the Exam

CompTIA Data+ Content

Domain 1.0: Mining data
Domain 2.0: Manipulating data
Domain 3.0: Visualizing and reporting data
Domain 4.0: Applying basic statistical methods
Domain 5.0: Analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle

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CompTIA DATA+ Training FAQs

CompTIA is a non-profit trade organisation best recognised for its IT certification examinations and test preparation courses.

There are no formal prerequisites required to attend this CompTIA Data+ Training course.

This CompTIA Data+ Training course is ideal for any professional tasked with developing and promoting data-driven business decision-making.

CompTIA courses help individuals to build solid foundational knowledge and skills, increase their salary, gives new opportunities, and build their confidence.

Pursuing this CompTIA Data+ training course will lead you to attain many greater opportunities such as training consultant, training developer/instructor, career technical training instructor, academic instructor, and many other reputed job titles.

Professionals with CompTIA Data+ Certification have the skills needed to make data-driven business decisions, such as mining, manipulating, visualising, and reporting data, applying basic statistical methods, and analysing complex datasets while adhering to governance and quality standards throughout the data life cycle.

During this training course, you will learn various essential concepts of data schemas and dimensions and the differences between common data structures and file formats. They will also learn how to translate business requirements into a proper visualisation in the form of a report or dashboard with the necessary design elements.

CareerMaker Solution is the Leading global training provider in the world for CompTIA Data+ Certification.

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

Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding). Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details: billing@careermaker.us

It is easy to enrol for the training online. There are several options to pay either through your debit/credit card that includes Visa Card, MasterCard; American Express or – via PayPal. Payment receipt will be issued to the candidate automatically by email.

Please send in an email to support@careermaker.us and we will answer any queries you may have!”

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