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Data Science Fundamentals

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Course Overview

This ISACA course introduces you to data science, a growing and rapidly changing field that is becoming increasingly vital to business survival, job stability, and national security. Data science demands skilled professionals who possess the knowledge, skills, and ability to address the evolving threat landscape.

Key Takeaways

Identify key concepts and terminology in data sciences.
Define the key concepts, roles and domains of data sciences.
Explain legal, regulatory and ethical considerations regarding use of data.
Explain activities performed to prepare data for analysis, categorization and modeling.
Distinguish among types of visualization and reporting tools.

Information Systems Audit and Control Association
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ISACA was incorporated in 1969 by a small group of individuals who recognized a need for a centralized source of information and guidance in the growing field of auditing controls for computer systems. Today, ISACA serves 140,000 professionals in 180 countries. As an independent, nonprofit, global association, ISACA engages in the development, adoption and use of globally accepted, industry-leading knowledge and practices for information systems. Previously known as the Information Systems Audit and Control Association, ISACA now goes by its acronym only, to reflect the broad range of IT governance professionals it serves.

Course Outline

Day 1
Session 1 – Data Characteristics: Basic Concepts

1.1.1 What Is Data Science?
1.1.2 Defining Big Data
1.1.3 The Evolution of Big Data
1.1.4 What Is Data?
1.1.5 Raw Data vs. Contextualized Data
1.1.6 Difference Between Data Statistics and Analytics
1.1.7 Data Types
1.1.8 ASCII and Unicode

Session 2 – Use of Data in Information Systems

1.1.9 DIKW Pyramid
1.1.10 Metadata
1.1.11 Data Flows and Data Diagrams
1.1.12 Applicability of Data to Business

Session 3 – Data Structures

1.2.1 Characteristics of Data Structures
1.2.2 Linear Structures
1.2.3 Tree Structures
1.2.4 Index and Pointer Structures

Day 2
Session 4 – Statistical Analysis

1.3.1 Populations and Samples
1.3.2 Statistical Modeling
1.3.3 Key Performance Indicators (KPIs)

Session 5 – Types of Databases

2.1.1 Introduction
2.1.2 Operational Databases
2.1.3 Relational vs. Non-Relational Databases
2.1.4 Autonomous Databases

Session 6 – Data Management

2.1.5 Common Database Management Systems
2.1.6 Data Lakes
2.1.7 Data Warehouse
2.1.8 Data Management Platforms

Day 3
Session 7 – Governance

2.2 Governance
2.2.1 Data Governance
2.2.2 Legal and Regulatory Compliance

Session 8 – Data Governance Roles

2.2.3 Data Ethics
2.2.4 Data Roles and Responsibilities

Session 9 – Access and Protection

2.3 Access and Protection
2.3.1 Data Accessibility and Protection
2.3.2 Managing Permissions
2.3.3 Third-Party and Vendor Access and Management
2.3.4 Data Obfuscation
2.3.5 Tokenization
2.3.6 Encryption

Day 4
Session 10 – Data Discovery and Collection

3.1 Data Discovery and Goal Identification
3.1.1 Requirements and Resources
3.1.2 Formulation of Hypotheses
3.2 Data Collection
3.2.1 Database Queries
3.2.2 Data Collection Methods and Session 

Session 11 – Data Classification

3.3 Data Classification
3.3.1 Data Cleansing
3.3.2 Data Clustering
3.3.3 Data Tagging
3.3.4 Data Governance Tools

Session 12 - Data Processing Concepts

3.4.1 Introduction
3.4.2 Exploratory Data Analysis
3.4.3 Model Development Tools
3.4.4 Statistical Analysis Tools
3.4.5 Business Analytics

Day 5
Session 13 – Data Processing with Machine Learning, Part 1

3.4.6 Machine Learning

Session 14 - Data Processing with Machine Learning, Part 2

3.4.6 Machine Learning

Session 15 – Communication of Results

3.5.1 Reporting Techniques
3.5.2 Reporting Tools

Who Should Attend?

This highly practical and interactive course has been specifically designed for
→ IT professionals with a familiarity of basic information technology and information systems concepts, who are:
→ New to data sciences
→ Interested in entering the field of data sciences
→ Interested in the ISACA Data Sciences Certification
→ Students and recent graduates interested in the field of data sciences
→ Individuals with zero to three years data sciences experience

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