Session 56 – Data Science Fundamentals: Data Characteristics - Basic Concepts
Session topics:
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 57 – Data Science Fundamentals: Use of Data in Information Systems
Session topics:
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 58 – Data Science Fundamentals: Data Structures
Session topics:
1.2.1 Characteristics of Data Structures
1.2.2 Linear Structures
1.2.3 Tree Structures
1.2.4 Index and Pointer Structures
Session 59 – Data Science Fundamentals: Statistical Analysis
Session topics:
1.3.1 Populations and Samples
1.3.2 Statistical Modeling
1.3.3 Key Performance Indicators (KPIs)
 Session 60 – Data Science Fundamentals: Types of Databases
Session topics:
2.1.1 Introduction
2.1.2 Operational Databases
2.1.3 Relational vs. Non-Relational Databases
2.1.4 Autonomous Databases
Session 61 – Data Science Fundamentals: Data Management
Session topics:
2.1.5 Common Database Management Systems
2.1.6 Data Lakes
2.1.7 Data Warehouse
2.1.8 Data Management Platforms
Session 62 – Data Science Fundamentals: Governance
Session topics:
2.2 Governance
2.2.1 Data Governance
2.2.2 Legal and Regulatory Compliance
Session 63 – Data Science Fundamentals: Data Governance Roles and Responsibilities
Session topics:
2.2.3 Data Ethics
2.2.4 Data Roles and Responsibilities
Session 64 – Data Science Fundamentals: Access and Protection
Session topics:
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
 Session 65 – Data Science Fundamentals: Data Discovery and Collection
Session topics:
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 Techniques
Session 66 – Data Science Fundamentals: Data Classification
Session topics:
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 67 - Data Science Fundamentals: Data Processing Concepts
Session topics:
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
Session 68 – Data Science Fundamentals: Data Processing with Machine Learning, Part 1
Session topics:
3.4.6 Machine Learning
Session 69 - Data Science Fundamentals: Data Processing with Machine Learning, Part 2
Session Topics:
3.4.6 Machine Learning
Session 70 – Data Science Fundamentals: Communication of Results
Session Topics:
3.5.1 Reporting Techniques
3.5.2 Reporting Tools

Labs

 
- Computing Fundamentals
Removable Drives and Data Recovery
Users and Groups
Files, Permissions, and Links
Subnetting Analysis
Windows Directories
Network Topologies
RAM and CPU Identification
Shell and Navigation

- Networks and Infrastructure
Telnet & SSH
FTP & SCP
Firewall Configuration Analysis
Router Configuration
DNS Analysis
HTTP Packet Analysis
Network Scanning
ARP Analysis

- Cybersecurity Fundamentals
SQL Injection
Windows Event Monitoring & Defender
Threat Removal
Threat Detection
File Permissions on Windows and Linux
Forensics: File Recovery, Baselining with Lynis
Scanning Ports and Utilizing SSH
Windows and Linux OS Firewalls

- Software Development Fundamentals
Number Systems
Debugging
Command Creation
Scripting with Python
Integrated Development Environments (IDEs)
GCC and C
Batch and Bash Scripting
Standard Input and Output

- Data Science Fundamentals
Creating and Querying Databases with GUI Database Clients
Using GUI Database Clients
Data Cleansing
Metadata
Database Permissions
Data Integrity
File Hashing
Data Management Systems