This course teaches you the knowledge and skills required to transform business requirements in support of data-driven decisions by mining data, manipulating data, applying basic statistical methods, and analyzing complex data sets while adhering to governance and quality standards throughout the entire data lifecycle. In addition, this course will help prepare candidates to take the CompTIA Data+ certification exam. This course may earn a Credly Badge.

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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

CompTIA Data+ validates that you 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

1

  • Identifying Basic Concepts of Data Schemas

  • Identify Relational and NonRelational Databases
    Understand the Way We Use Tables ,Primary Keys, and Normalization
2

  • Understanding Different Data Systems

  • Describe Types of Data Processing and Storage Systems
    Explain How Data Changes
3

  • Understanding Types and Characteristics of Data

  • Understand Types of Data
    Break Down the Field Data Types
4

  • Comparing and Contrasting Different Data Structures, Formats, and Markup Languages

  • Differentiate between Structured Data and Unstructured Data
    Recognize Different File Formats
    Understand the Different Code Languages Used for Data
5

  • Explaining Data Integration and Collection Methods

  • Understand the Processes of Extracting, Transforming, and Loading Data
    Explain API/Web Scraping and Other Collection Methods
    Collect and Use Public and Publicly Available Data
    Use and Collect Survey Data
6

  • Identifying Common Reasons for Cleansing and Profiling Data

  • Learn to Profile Data
    Address Redundant, Duplicated, and Unnecessary Data
    Work with Missing Values
    Address Invalid Data
    Convert Data to Meet Specifications
7

  • Executing Different Data Manipulation Techniques

  • Manipulate Field Data and Create Variables
    Transpose and Append Data
    Query Data
8

  • Explaining Common Techniques for Data Manipulation and Optimization

  • Use Functions to Manipulate Data
    Use Common Techniques for Query Optimization
9

  • Applying Descriptive Statistical Methods

  • Use Measures of Central Tendency
    Use Measures of Dispersion
    Use Frequency and Percentages
10

  • Describing Key Analysis Techniques

  • Get Started with Analysis
    Recognize Types of Analysis
11

  • Understanding the Use of Different Statistical Methods

  • Understand the Importance of Statistical Tests
    Break Down the Hypothesis Test
    Understand Tests and Methods to Determine Relationships Between Variables
12

  • Using the Appropriate Type of Visualization

  • Use Basic Visuals
    Build Advanced Visuals
    Build Maps with Geographical Data
    Use Visuals to Tell a Story
13

  • Expressing Business Requirements in a Report Format

  • Consider Audience Needs When Developing a Report
    Describe Data Source Considerations For Reporting
    Describe Considerations for Delivering Reports and Dashboards
    Develop Reports or Dashboards
    Understand Ways to Sort and Filter Data
14

  • Designing Components for Reports and Dashboards

  • Design Elements for Reports and Dashboards
    Utilize Standard Elements
    Creating a Narrative and Other Written Elements
    Understand Deployment Considerations
15

  • Distinguishing Different Report Types

  • Understand How Updates and Timing Affect Reporting
    Differentiate Between Types of Reports
16

  • Summarizing the Importance of Data Governance

  • Define Data Governance
    Understand Access Requirements and Policies
    Understand Security Requirements
    Understand Entity Relationship Requirements
17

  • Applying Quality Control to Data

  • Describe Characteristics, Rules, and Metrics of Data Quality
    Identify Reasons to Quality Check Data and Methods of Data Validation
18

  • Explaining Master Data Management Concepts

  • Explain the Basics of Master Data Management
    Describe Master Data Management Processes

Audience

Data+ is an ideal certification for not only data-specific careers, but other career paths that can benefit from analytics processes and data analytics knowledge, such as marketing specialists, financial analysts, human resource analysts or clinical health care analysts. This course is suited for those whose job role could be Data Analyst, Business Intelligence Analyst, Reporting Analyst, Marketing Analyst, Clinical Analyst, Business Data Analyst, or Operations Analyst.

Language

English

Prerequisites

While there are no prerequisites for this course, please ensure you have the right level of experience to be successful in this training.

$2,475

Length: 5.0 days (40 hours)

Level:

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