
Explore CompTIA Data Plus crash course on collecting, analyzing, and reporting data. Master data transformation, ELT and ETL, APIs, normalization, descriptive and inferential statistics, governance, security, privacy, and exam objectives.
Explore the central role of data as a valuable enterprise asset, and learn how data professionals transform facts into actionable insights, reports, and forecasts that guide decisions.
Please go to Techcommanders(Digital Crest Institute) Data Plus Resource page for downloads.
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Explore data concepts and environments, including data types, schemes, processing, formats, databases, Cloud SQL, data warehouses and lakes, columnar formats, OLTP vs OLAP, and demos with Redshift and Demo Bench.
Explore data schemas and the data modeling process, distinguishing logical and physical database schemas, dimension and fact tables, and slowly changing dimensions with effective dates.
Explore Google Cloud SQL, a fully managed rdbms for OLTP workloads, compare databases with data warehouses like BigQuery and Redshift, and learn about instances, backups, and connect workflows.
Explore data warehouse basics and variations, including data lake, data cube, and data mart, and learn how to derive value and answer key questions from data.
Define and compare oltp and olap, highlighting oltp's high-rate transactions on relational databases and olap's big-data business intelligence analytics using data lakes or data warehouses.
Answer module review questions to reinforce key data concepts, from relational databases with tabular schemas to data modeling, oltp features, and data warehouse and data mart roles.
Explore data mining by acquiring, integrating, and deriving value from data, with hands-on demos of data acquisition, APIs, Google Vision, ETL/ELT, profiling, cleansing, and query optimization.
Explore data integration concepts and hands-on setup using Power BI, Amazon Redshift, and Excel, including data connectors, ETL, batch, micro-batch, and streaming processing.
Learn how APIs enable two programs to communicate, covering their specification and interface, endpoints, data formats, and authentication, with REST, SOAP, and gRPC examples.
Master the differences between etl and elt, including extraction, transformation, and loading, use cases for data warehouses and data lakes, and full and delta load strategies.
Boost query performance by planning, cost awareness, and optimization across Redshift and BigQuery, aligning data needs with business requirements and using parameterized queries or indexing.
Explore how APIs connect to cloud services via endpoint URLs, authenticate, and retrieve data; profile, clean, and transform data, and apply ETL/ELT, with statistics, outliers, and data queries.
Learn to perform hypothesis testing in Excel using p values and tests like t, z, chi-squared, F, and use Analysis Toolpak to assess significance and type I and II errors.
Summarize data with descriptive statistics, central tendency, dispersion, and frequency; apply confidence intervals and inferential statistics on population samples using Excel p value, z test, chi squared, t test.
Identify stakeholders and understand objectives to translate business requirements into targeted reports and dashboards. Assess data sources, filtering, frequency, and distribution lists to meet executive, data owner, and customer needs.
Master dashboard fundamentals, including dynamic interfaces with live data sources, KPI insights for different audiences, and key concepts like dimensions, measures, wireframes, drill down, and RBAC.
Explore dashboard fundamentals using a BigQuery example to query Bitcoin data, visualize with Looker Studio, and distinguish dimensions and measures for effective charts and reports.
Identify the difference between a data source and an attribute, and explore sources such as databases, data warehouses, spreadsheets, files, web services, and on prem and cloud services for analysis.
Align stakeholder requirements with data analysis by using dashboards to reveal kpis, dimensions, measures, data sources, and executive summary that prove value.
Explore data governance, data quality, and controls from security to DLP and IRM, with data lifecycle, ownership, classification, privacy, storage access, and master data management.
Explore data governance essentials, including security, compliance, and data quality within organizations, and master principles like transparency, accountability, privacy, and data models.
Identify roles in data ownership, including data owner, custodian, steward, processor, and chief privacy officer, and clarify who sets data classification, sensitivity, and CIA.
Discover data transformation across databases and warehouses, using constructive, destructive, aesthetic, and structural methods with tools like Excel, Tableau, Power BI, RapidMiner, Talend, Datameer.
Review module on data governance and the data lifecycle's seven phases, including access control, validation, verification, profiling, and data quality.
Access practice exams and resources on platforms like Tech commanders, Udemy, Kindle, and O'Reilly. A data plus blog and an upcoming exam guide provide extended study support in early 2024.
Pursue the data plus certification, pass efficiently, and add the credential to your LinkedIn profile. Reach out on LinkedIn or Tech Commanders comm if you have questions.
The demand for data professionals has been exponentially growing year over year and becoming CompTIA Data Plus (DAO-001) Certified can really elevate your career opportunities in the world of data and business analytics.
Data is the foundation for many organizations and offers the potential for growth in almost any industry. Data analysts collect, store, transform, maintain, analyze, and secure data for use in their organizations.
A CompTIA Data Plus certified professional is proficient at the collection, analysis, and visualization of data that provides value so that their organizations achieve goals and can make complex business decisions.
The Data+ exam is designed to be a vendor-neutral certification for data professionals and those seeking to enter the data fields.
Did you know in Oct 2023, the average salary for a Data Analyst is $74,753 dollars per year in United States according to Glassdoor.
Entry level business analysts in the United States can start at $64,026 per year while the most sought-after solutions architects can make over $154,000 per year.
Once you complete this CompTIA Data Plus Certification Crash Course you will truly be prepared to take your place amongst the highly sought-after data professionals in your organization.
About the Course
In this course we focus on preparing you for a successful sitting for the challenging CompTIA Data Plus DA0-001 exam.
This course provides full content, free practice questions and study eBook as well optional demonstration and exercises.
An important aspect that data professional must know is focused on data mining, data manipulation as well as visualizing and reporting data to stakeholders.
So, whether applying basic statistical methods or analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle a data professional is an important role to enterprises.
All of the exam objectives are covered as specified by CompTIA for the exam in these domains.
Data Concepts and Environments
Data Mining
Data Analysis
Data Visualization
Data Governance, Quality, and Controls
ABOUT THE COMPTIA DATA+ CERTIFICATION
CompTIA Data+ is an early-career data analytics certification for professionals tasked with developing and promoting data-driven business decision-making. The certification validates the data analytics skills and competencies that are needed to organize, understand, and act on relevant data.
This course covers 100% of the DA0-001 objective domains and also provides exam test tips, topic focused demonstrations and over 100 practice questions along with a free downloadable study guide.
What will you learn in the course?
Understand the importance of the CompTIA Data Plus exam and its objectives.
You'll learn to how to collect, analyze, and report on various types of commonly used data.
You’ll learn about how to transform raw data into usable information for your stakeholders.
Learn about database types, data structures, data schemes and other important aspects of data management.
Understand how JSON data, HTML data, and XML data is used with data professionals.
Understand how ETL and ELT processes work, how APIs connect us to the cloud and learn about profiling datasets.
Learn about data manipulation techniques and important concepts around data transposition and normalization.
Learn about descriptive statistics, inferential statistics, and various analytic techniques.
Identify common analytics tools used in data analysis.
Learn about the importance of Structured Query Language (SQL) and its main components.
Learn about reporting, reporting dashboards and the various visualization types.
Describe the importance of data governance, data stewardship and quality controls to ensure compliance and data consistency.
Identify the compliance requirements, security controls and privacy.
Course Content Covered
Course Welcome
Course Overview
Instructor Introduction
What is the CompTIA Data Plus Exam
Exam Objectives
Exam Acronym List
Data Roles to Know
The Importance of Data
Download Course Resources
Data Concepts and Environments
Data Schemes
Data Dimensions
Databases
Demonstration - Google Cloud SQL
Data Warehouses and Data Lakes
Online transactional processing (OLTP)
Demonstration - AWS Redshift
Online Analytical Processing (OLAP)
What is a Schema?
Importance of Dimensions
Demonstration - Google Cloud Big Query (OLAP)
Data Types
Demonstration - File Types
Demonstration - Deploy SQL Demo Bench
Data Structures
What is a Data Structure
Structured
Unstructured
Semi Structured
Data File Formats
Big Data File Formats
What is Columnar Format
Data Compression
Module Summary Review
Module Review Questions
Data Mining
Understanding Data Acquisition
Integration Concepts
What is An API?
Demonstration - APIs
Data Collection Method Options
Demonstration - Google Big Query Sample Data
Whiteboard Discussion - Data Collection
Data Cleansing and Profiling
Whiteboard Discussion - Data Cleansing/Profiling
Demonstration - Excel Data Cleansing and Profiling
Data Outliers
Understanding Data Manipulation Techniques
Recoding Data
Merge Data
Eliminate Redundancy
Data Normalization
ETL
Scenario - Data Manipulation
Common techniques for data manipulation and query optimization
Data Manipulation Workflow
Data Manipulation Techniques
Query Optimization
Demonstration - Query Optimization
Module Summary Review
Module Review Questions
Data Analysis
Understanding Descriptive Statistical Methods
Measures of Tendency
Measures of Dispersion
Understanding Percentages
Understanding Inferential Statistical Methods
Hypothesis Testing
Linear Regression and Correlation
Summarize types of analysis and key analysis techniques
Define Exploratory Data Analysis
Performance Analysis
Link Analysis
Common Data Tool Sets
Demonstration - MS Excel
Demonstration - Power BI
Demonstration - AWS Quicksight
Module Summary Review
Module Review Questions
Data Visualization
Module Overview
Translate Business Requirements to Reports
Design Components
Demonstration - Reports and Components
Dashboard Design
Dashboard Components
Demonstration - Dashboard Components
Data Sources and Attributes
Consumers
Delivery and Development
Visualization Types
Understanding Chart Types
Understanding Plot Types
Understanding Mapping
Demonstration - Visualization
Compare and Contrast Reports
Reports Type Overview
Recurring Report Types
Static and Dynamic Reports
Demonstration - Compliance
Module Summary Review
Module Review Questions
Data Governance, Quality and Controls
Module Overview
Data Governance
Requirements
Data Classification
Data Privacy
Data Breaches
Data Quality Control
Data Checks
Data Transformation
Data Validation
Data Quality
Data Quality Dimensions
Rules and Metrics
Master Data Management (MDM)
Module Summary Review
Module Review Questions
Exam Preparation and Practice Exams
Exam Experience
Certification CPE Requirements
Course Content Review
Top Ten Things to Know for the Exam
Practice Questions Pool 1
Practice Questions Pool 2
Additional Resources
Course Closeout
Who should take this course (Target Audience)?
Beginners looking for an entry point into the data world.
Data Engineers, Data Analysts with some experience working with data.
Software professionals, Database professionals looking to boost their knowledge and skillsets
What are the Couse Pre Requirements?
There are no course pre-requirements.