Course information

CompTIA Data+ Certification Training Course Outline

Module 1: Identifying Basic Concepts of Data Schemas

  • Identify the Key Differences Between Relational and Non-Relational Databases
    • Relational Databases
    • Non-Relational Databases

Lab: Navigating and Understanding Database Design

  • Identify the Way We Use Tables, Primary Keys, and Normalisation
    • Normalisation
    • Normalising Data
    • Relationships in Data
    • Types of Relationships
    • Referential Integrity
    • Denormalisation

Module 2: Understanding Different Data Systems

  • Describe Types of Data Processing and Storage Systems
    • Types of Data Processing
    • Source Systems
    • Data Warehouses and Data Marts
    • Schemas Used in Data Warehousing
    • Fact Table
    • Dimension Table
    • Star Schema
    • Snowflake Schema
    • Data Lakes and Lakehouses
  • Explain How Data Changes
    • Overview of Slowly Changing Dimensions
    • Impact of Slowly Changing Dimensions

Module 3: Understanding Data Types and Characteristics of Data

  • Understand Types of Data
    • Quantitative Data
    • Qualitative Data
    • Why the Data Types Matter?
  • Break Down the Field Data Types
    • Introduction to Field Data Types
    • Text/Alphanumeric Field Data Types
    • Date Data Type
    • Number Date Types
    • Currency Data Type
    • Boolean Data Type
    • Data Type Conversion

Lab: Understanding Data Types and Conversion

Lab: Understanding Data Structure and Types and Using Basic Statements

Module 4: Comparing and Contrasting Different Data Structures, Formats, and Markup Languages

  • Differentiate Between Structured Data and Unstructured Data
    • Structured Data
    • Unstructured Data
  • Recognise Different File Formats
    • Delimited Files
    • Why We Use Delimited Files?
    • Flat Files
    • File Extensions

Lab: Working with Different File Formats

  • Understand the Different Code Languages Used for Data
    • Structured Query Language (SQL)
    • Structured Hyper Text Markup Language (HTML)
    • Extensible Markup Language (XML)
    • JavaScript Object Notation (JSON)

Module 5: Explaining Data Integration and Collection Methods

  • Understand the Processes of Extracting, Transforming, and Loading Data
    • Extracting Data
    • Transforming Data
    • Loading Data
    • Full Load and Delta Load
    • Extract, Load, Transform (ELT)
  • Explain API/Web Scraping and Other Collection Methods
    • Application Programming Interface (API)
    • Web Services
    • Web Scraping
    • Machine Data
  • Collect and Use Public Data
    • Overview of Public and Publicly-Available Data
    • Finding Public and Publicly-Available Data

Lab: Using Public Data

  • Use and Collect Survey Data
    • Considerations for Using Surveys
    • Question Design
    • Types of Survey Answers

Module 6: Identifying Common Reasons for Data Cleansing and Profiling Datasets

  • Learn to Profile Data
    • Steps of Data Profiling
    • Data Profiling Tools and Techniques

Lab: Profiling Data Sets

  • Address Redundant and Duplicated Data
    • Redundant Data
    • Duplicated Data
    • Unnecessary Fields

Lab: Addressing Redundant and Duplicated Data

  • Work with Missing Values
    • Causes of Null Values
    • Filtering Null Values
    • Replacing Missing Values

Lab: Addressing Missing Values

  • Address Invalid Data
    • Identifying Invalid Data
    • Removing Invalid Data
    • Replacing Invalid Data with Valid Data
  • Convert Data to Meet Specifications
    • Data That Does Not Meet Specifications
    • Converting Data Types

Lab: Preparing Data for Use

Module 7: Executing Different Data Manipulation Techniques

  • Recode Data and Derived Variables
    • Recoding Numerical and Categorical Data
    • Derived Variables
    • Imputing Values
    • Reduction in Data Sets
    • Masking Values

Lab: Recoding Data

  • Transpose and Append Data
    • Transposing Data
    • Appending Data
  • Query Data
    • Querying Data
    • Types of Joins

Lab: Working with Queries and Join Types

Module 8: Explain Common Techniques for Data Manipulation and Optimisation

  • Use Functions to Manipulate Data
    • Text Functions
    • Text Functions - Left, Right, Mid
    • Text Functions - Upper, Lower, and Proper
    • Combining Data Fields
    • Parsing Strings for Information
    • Date Functions
    • Logical Functions and Conditional Formatting
    • Aggregation and the Basic Types of Aggregate Functions
    • System Functions
  • Use Common Techniques for Query Optimisation
    • Filtering Data
    • Parameterisation
    • Indexing Data
    • Temporary Tables
    • Sub Querying and Subsets of Information
    • Query Execution Plan

Lab: Building Queries and Transforming Data

Module 9: Applying Descriptive Statistical Methods

  • Use Measures of Central Tendency
    • Measures of Central Tendency Overview
    • Mean
    • Median
    • Mode

Lab: Using the Measures of Central Tendency

  • Use Measures of Dispersion
    • Overview of the Measures of Dispersion
    • Range of Data
    • Standard Deviation
    • Z-Scores
    • Distribution of a Data Set

Lab: Using the Measures of Variability

  • Use Frequencies and Percentages
    • Frequency
    • Percentage Difference
    • Percentage Change

Module 10: Describing Key Analysis Techniques

  • Get Started with Analysis
    • Research Questions
    • Sample Research Questions
    • Data Sources and Collection Methods
    • Observations
  • Recognise Types of Analysis
    • Exploratory Analysis
    • Performance Analysis
    • Gap Analysis
    • Trend Analysis
    • Link Analysis

Module 11: Understanding the Use of Different Statistical Methods

  • Understand the Importance of Statistical Tests
    • Confidence Intervals
    • T-Tests and P-Values
  • Break Down the Hypothesis Test
    • Null Hypothesis
    • Understanding the Results of Hypothesis Testing
  • Understand Tests and Methods to Determine Relationships Between Variables
    • Chi-Square
    • Chi-Square Tests
    • Simple Linear Regression
    • Correlation
    • Use Excel to Apply Statistical Methods

Lab: Analysing Data

Module 12: Using the Appropriate Type of Visualisation

  • Use Basic Visuals
    • Pie Chart
    • Treemaps
    • Column and Bar Charts
    • Line Graphs

Lab: Building Basic Visuals to Make Visual Impact

  • Build Advanced Visuals
    • Stacked Column/Bar Charts
    • Line Graphs with Multiple Lines
    • Combination Charts
    • Scatter Plots
    • Bubble Charts
    • Histograms
    • Waterfall Charts
  • Build Maps with Geographical Data
    • Preparing Geo Fields for Mapping
    • Geographic Maps

Lab: Building Maps with Geographical Data

  • Use Visuals to Tell a Story
    • Heat Maps
    • Word Clouds
    • Infographics

Lab: Using Visuals to Tell a Story

Module 13: Expressing Business Requirements in a Report Format

  • Consider Audience Needs When Developing a Report
    • Audience
    • Consumer Types
  • Describe Data Source Considerations for Reporting
    • Documenting the Source Data
    • Determining Access to Data
    • Developing Views of the Data
    • Data Fields and Attributes
  • Describe Considerations for Delivering Reports and Dashboards
    • Determining How Visuals Will Be Viewed
    • Determining How Data Will Be Delivered
    • Frequency of Reporting
    • Recurring Reports
  • Develop Reports or Dashboards
    • Visualisation Layouts
    • Mock-up and Wireframing for Design
    • Types of Visuals
    • Types of Dashboard Navigation
  • Understand Ways to Sort and Filter Data
    • Sorting Data
    • Filter Methods for Visuals
    • Filtering by Date Ranges

Lab: Filtering Data

Module 14: Designing Components for Reports and Dashboards

  • Design Elements for Reports/Dashboards
    • Branding Guidelines
    • Appropriate Colour Schemes
    • Appropriate Fonts and Layout
    • Naming Conventions

Lab: Designing Elements for Dashboards

  • Utilise Standard Elements
    • Standard Information and Formatting Elements for Reports
    • Other Special Fields
    • Watermarks
    • Important Dates
  • Create a Narrative and Other Written Elements
    • Narrative
    • Instructions for Using the Report/Dashboard
    • Other Supporting Materials
  • Understand Deployment Considerations
    • Techniques for Dashboard Optimisation
    • Expand and Collapse Options for Information
    • Drill Through
    • Tooltips
    • Other Considerations
    • Deploy to Production

Module 15: Distinguish Different Report Types

  • Understand How Updates and Timing Affect Reporting
    • Static Vs Dynamic Reports
    • Point-in-Time Reporting
    • Real-Time Reporting
  • Differentiate Between Types of Reports
    • Operational and Compliance Reports
    • Tactical and Research-Driven Reporting
    • Ad-Hoc Reporting
    • Self-Service Reporting

Lab: Building an Ad Hoc Report

Lab: Visualising Data

Module 16: Summarising the Importance of Data Governance

  • Define Data Governance
    • Lifecycle of Data
    • Roles Within a Data Governance Team
    • Jurisdiction Requirements
    • Regulations and Compliance
    • Data Classifications
  • Understanding Access Requirements and Policies
    • Data Use Agreements
    • Release Approvals
    • Data Retention and Destruction Policies
  • Understand Security Requirements
    • Data Processing
    • Data Transmission
    • Data Encryption
    • De-Identification and Masking of Data
    • Data Breaches
    • Data Access
    • Saving Data Files and Storage Types

Lab: Building Basic Visuals to Make Visual Impact

  • Understanding Entity Relationship Requirements
    • Entity Relationship Models
    • Record Linkage Restrictions
    • Data Constraints

Module 17: Applying Quality Control to Data

  • Describe Characteristics, Rules, and Metrics of Data Quality
    • Reasons to Check Data Quality
    • Understanding Quality
    • Rules and Metrics for Data Quality
  • Identify Reasons to Quality Check Data and Methods of Data Validation
    • Data Validation Methods
    • Automated Validation
    • Data Verification Methods

Module 18: Explaining Master Data Management

  • Explain the Basics of Master Data Management
    • Master Data Management
    • Benefits of Master Data Management
    • Reasons for Master Data Management
    • Master Data Management Vs Data Warehouse
  • Describe Master Data Management Processes
    • Consolidation of Multiple Data Fields
    • Field Standardisation
    • Data Dictionary

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Who should attend this CompTIA Data+ Certification Training Course?

The CompTIA Data+ Certification is a vendor-neutral certification that validates the knowledge and skills required to manage data in a variety of environments. It is designed for IT professionals who are responsible for collecting, storing, processing, and analysing data.  This course can be beneficial for various professionals, including:

  • Data Analysts
  • Database Administrators
  • Data Engineers
  • Business Analyst
  • Entry-level Data Scientists
  • Systems Analysts
  • IT Managers
  • Data Consultants

Prerequisites of the CompTIA Data+ Certification Training Course

There are no formal prerequisites to attend the CompTIA Data+ Certification Training Course, but to be eligible for the certification exam, you must have a minimum of 18-24 months of experience in a report/Business Analyst role, be familiar with databases and analytics tools, possess a foundational knowledge of statistics, and have experience in data visualisation.

CompTIA Data+ Certification Training Course Overview

The CompTIA Data+ Certification Training Course is a comprehensive course designed for professionals aiming to excel in the field of data management and analysis. In today's data-driven world, where organisations rely heavily on data to make informed decisions, this course is invaluable as it equips individuals with the skills and knowledge needed to collect, analyse, and interpret data effectively.

Professionals involved in data management, analysis, and database administration should prioritise mastering the CompTIA Data+ Certification Training Course. This certification serves as a validation of their proficiency in managing and leveraging data assets to drive business insights and innovation, thereby enhancing their career prospects and credibility within the industry.

In our 2-day CompTIA Data+ Training, delegates gain a thorough understanding of the essential concepts of data schemas and dimensions, as well as the differences between common data structures and file formats. They learn how to translate business requirements into a proper visualisation in the form of a report or dashboard with the necessary design elements. 

Course Objectives

  • To understand the basics of data and data analytics, including data mining, manipulation, visualisation, and reporting
  • To apply basic statistical methods to data analysis
  • To understand data governance and quality standards
  • To use data analysis tools and techniques to solve real-world problems
  • To apply the best practices for managing and protecting data, including data governance policies and procedures, data quality management, and data security

Upon completing this training course, delegates gain the ability to use descriptive statistical methods, summarise different types of analysis, and apply critical analysis techniques, while also enhancing their skills in summarising significant data governance issues and implementing data quality control measures.

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What’s included in this CompTIA Data+ Certification Training Course?

  • World-Class Training Sessions from Experienced Instructors
  • CompTIA Data+ Certificate
  • Digital Delegate Pack

Why choose us

Our Calgary venue


Free Wi-Fi

To make sure you’re always connected we offer completely free and easy to access wi-fi.

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To keep you comfortable during your course we offer a fully air conditioned environment.

Full IT support

IT support is on hand to sort out any unforseen issues that may arise.

Video equipment

This location has full video conferencing equipment.

Calgary is located in the south of Alberta, Canada and has a population of around 1 million people, making it the largest city in Alberta and the third largest in Canada. It also became the first Canadian city to host the Olympic Winter Games. There is a strong economy in Calgary, as it boasts the second most corporate head offices in Canada and has activity based in the health, retail, tourism, transportation, film, technology and energy sectors. There are a large amount of educational facilities in Calgary, including a large number of public and private schools ran by different school boards including English and French language boards. There is also a high school dedicated to training athletes ready for the Olympics. Each high school in Calgary is large, consisting of around 2000 students each. Private schools include Rundle Academy, Calgary French and International School, Chinook Winds Adventist Academy and Delta West Academy. The biggest university in Calgary is the ‘University of Calgary’ which enrols around 30,000 students. It offers a wide variety of degrees, diplomas and certificates. Also in Calgary are Mount Royal University, SAI Polytechnic and Athabasca University, which provides distance education programs for students. There are a number of private universities as well, including Reeves College and Columbia College.

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Ways to take this course

Experience live, interactive learning from home with The Knowledge Academy's Online Instructor-led CompTIA Data+ Course | CompTIA Training in Calgary. Engage directly with expert instructors, mirroring the classroom schedule for a comprehensive learning journey. Enjoy the convenience of virtual learning without compromising on the quality of interaction.

Unlock your potential with The Knowledge Academy's CompTIA Data+ Course | CompTIA Training in Calgary, accessible anytime, anywhere on any device. Enjoy 90 days of online course access, extendable upon request, and benefit from the support of our expert trainers. Elevate your skills at your own pace with our Online Self-paced sessions.

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CompTIA Data+ Course | CompTIA Training in Calgary 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+ Course.
This CompTIA Data+ Course is ideal for any professional tasked with developing and promoting data-driven business decision-making.
Pursuing this CompTIA Data+ 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.
The CompTIA Data+ Training Course is designed to equip individuals with the skills and knowledge necessary to analyse, interpret, and manage data in a business environment. It covers fundamental data concepts, data analytics practices, and how to work with data effectively.
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.
Upon passing the exam, the CompTIA Data+ certification remains valid for a duration of three years. To maintain the certification beyond this period, individuals can opt for renewal through the CompTIA Continuing Education (CE) program. This training program enables certification holders to extend their certification in three-year increments by participating in activities and training relevant to the certification's content.
Yes, there is an expiry date for the CompTIA Data+ Course. Upon successful completion of the exam, this certification remains valid for a duration of three years. To ensure the certification's continuity, it is essential to renew it before the expiration date.
To maintain the validity of your certification, it is necessary to accumulate a minimum of 20 Continuing Education Units (CEUs) within a three-year timeframe and submit them to your certification account. There are various options available for certification renewal, including engaging in CompTIA CertMaster CE eLearning, obtaining a higher-level CompTIA certification, renewing through multiple activities, renewing through a single activity, and renewing multiple certifications simultaneously.
Earning the CompTIA Data+ Certification opens up career opportunities such as Data Analyst, Business Intelligence Analyst, and Junior Data Scientist.
The CompTIA Data+ Certification provides a foundational understanding of data analytics, suitable for beginners and distinct from more specialised certifications that focus on specific tools or methodologies.
Candidates may struggle with mastering a broad range of data concepts and analytics practices. Overcoming these challenges can be achieved through using study materials, and engaging in hands-on practice through our CompTIA Data+ Course
The Knowledge Academy stands out as a prestigious training provider known for its extensive course offerings, expert instructors, adaptable learning formats, and industry recognition. It's a dependable option for those seeking CompTIA Data+ Courses.
The training fees for CompTIA Data+ Course certification in Calgary starts from CAD4295
The Knowledge Academy is the Leading global training provider for CompTIA Data+ Course.
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