ITCA-DSF: ISACA Data Science Fundamentals Certificate

Module 1: Data Characteristics: Basic Concepts

Learning Objectives

Define the terms and concepts of data science.
Describe the relationship between data science and statistics.
Describe the classifications and characteristics of data.

Topics

What Is Data Science?
Defining Big Data
The Evolution of Big Data
What Is Data?
Raw Data vs. Contextualized Data
Comprehensive version
Difference Between Data Statistics and Analytics
Data Types
ASCII and Unicode

Module 2: Use of Data in Information Systems

Learning Objectives

Explain the different types of data structures, flows and diagrams.

Topics

DIKW Pyramid
Metadata
Data Flows and Data Diagrams
Applicability of Data to Business

Module 3: Data Structures

Learning Objectives

Explain the different types of data structures, flows and diagrams.

Topics

Characteristics of Data Structures
Linear Structures
Tree Structures
Index and Pointer Structures

Module 4: Statistical Analysis

Learning Objectives

Use statistical analysis to gather populations and samples.
Distinguish among sampling techniques.

Topics

Populations and Samples
Statistical Modeling
Key Performance Indicators (KPIs)

Module 5: Types of Databases

Learning Objectives

Distinguish among different data storage and management systems.
Describe the benefits of using automated processes to manage data.

Topics

Introduction
Operational Databases
Relational vs. Non-Relational Databases
Autonomous Databases

Module 6: Data Management

Learning Objectives

Identify elements within a database management system.
Explain the use of data in online and cloud-based applications.

Topics

Common Database Management Systems
Data Lakes
Data Warehouse

Module 7: Governance

Learning Objectives

Explain legal, regulatory and ethical considerations regarding use of data.

Topics

Governance
Data Governance
Legal and Regulatory Compliance

Module 8: Data Governance Roles and Responsibilities

Learning Objectives

Explain legal, regulatory and ethical considerations regarding use of data.
Detail data governance roles and responsibilities.

Topics

Data Ethics
Data Roles and Responsibilities

Module 9: Access and Protection

Learning Objectives

Distinguish among data obfuscation, tokenization and encryption.

Topics

Access and Protection
Data Accessibility and Protection
Managing Permissions
Third-Party and Vendor Access and Management
Data Obfuscation
Tokenization
Encryption

Module 10: Data Discovery and Collection

Learning Objectives

Identify open and cross-industry standards used to process data.
Describe techniques used to collect data.

Topics

Data Discovery and Goal Identification
Requirements and Resources
Formulation of Hypotheses
Data Collection
Database Queries
Data Collection Methods and Techniques

Module 11: Data Classification

Learning Objectives

Explain activities performed to prepare data for analysis, categorization and modeling.

Topics

Data Classification
Data Cleansing
Data Clustering
Data Tagging
Data Governance Tools

Module 12: Data Processing Concepts

Learning Objectives

Identify methods to uncover relationships among data.
Identify tools used to build, model and analyze data.
Describe concepts related to business analytics.

Topics

Introduction
Exploratory Data Analysis
Model Development Tools
Statistical Analysis Tools
Business Analytics

Module 13: Data Processing with Machine Learning

Learning Objectives

Distinguish among types of machine learning algorithms.

Topics

Machine Learning

Module 14: Communication of Results

Learning Objectives

Distinguish among types of visualization and reporting tools.

Topics

Reporting Techniques
Reporting Tools

Module 15: Practice Lab

Creating and Querying Databases with GUI Database Clients
Using GUI Database Clients
Data Cleansing
Metadata
Database Permissions
Data Integrity
File Hashing
Data Management System