CompTIA Data+ Course | CompTIA Training in Vilnius

CompTIA Data+ 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 do 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 Analyses
  • 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