DSFData Science Fundamentals – Exam Preparation Course
Course Overview
Key Takeaways
Course Outline
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
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
1.2.1 Characteristics of Data Structures
1.2.2 Linear Structures
1.2.3 Tree Structures
1.2.4 Index and Pointer Structures
1.3.1 Populations and Samples
1.3.2 Statistical Modeling
1.3.3 Key Performance Indicators (KPIs)
2.1.1 Introduction
2.1.2 Operational Databases
2.1.3 Relational vs. Non-Relational Databases
2.1.4 Autonomous Databases
2.1.5 Common Database Management Systems
2.1.6 Data Lakes
2.1.7 Data Warehouse
2.1.8 Data Management Platforms
2.2 Governance
2.2.1 Data Governance
2.2.2 Legal and Regulatory Compliance
2.2.3 Data Ethics
2.2.4 Data Roles and Responsibilities
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
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
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
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
3.4.6 Machine Learning
3.4.6 Machine Learning
3.5.1 Reporting Techniques
3.5.2 Reporting Tools
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