This self-paced 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.

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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 roles such as: Data Analyst Business Intelligence Analyst Reporting Analyst Marketing Analyst Clinical Analyst Business Data Analyst Operations Analyst

Language

English

Prerequisites

There are no prerequisites for this course.

Length: 365.0 days (40 hours)

Level:

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