Course Description:
This 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.
There are 51 Questions on the exam which must be completed in 2 hours. It is available online via remote proctoring.
The Data Science Fundamentals Certificate is intended for:
A wide-range of individuals, including:
• Those new to IT, students, recent graduates and career changers.
• Audit, risk, security and governance professionals looking to gain base-line IT knowledge and skills.
• Current IT Professionals looking to reskill or upskill to broaden their IT knowledge and skills or keep up-to-date.
CPE Overview:
This certificate awards approximately 1 hour of CPE for every 1 hour of instructor led training.
Course Duration:
In-person Instructor Led Training: 2 days
Course Topics:
Data Characteristics: Basc 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
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
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
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)
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
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
• Data Management Platforms
Governance
Learning Objectives
• Explain legal, regulatory and ethical considerations regarding use of data.
Topics
• Governance
• Data Governance
• Legal and Regulatory Compliance
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
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
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
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
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
Data Processing with Machine Learning
Learning Objectives
• Distinguish among types of machine learning algorithms.
Topics
• Machine Learning
Communication of Results
Learning Objectives
• Distinguish among types of visualization and reporting tools.
Topics
• Reporting Techniques
• Reporting Tools
Practice Labs
• Creating and Querying Databases with GUI Database Clients
• Using GUI Database Clients
• Data Cleansing
• Metadata
• Database Permissions
• Data Integrity
• File Hashing
• Data Management Systems
