"Data Science and its Relationship to Big Data and Data-Driven Decision Making"
1. Data Science
1.1. Collecting Data
1.1.1. Sources of Data
1.1.1.1. Internal Databases
1.1.1.1.1. Metrics
1.1.1.2. Social Media
1.1.1.2.1. Facebook
1.1.1.2.2. Twitter
1.1.1.3. Surveys
1.1.1.3.1. Questionnaires
1.1.2. Methods of Data Collection
1.1.2.1. Automated
1.1.2.1.1. Software
1.1.2.2. Manual Entry
1.1.2.2.1. Spreadsheets
1.1.2.3. Data Acquisition
1.1.2.3.1. Third-party Company
1.1.3. Challenges
1.1.3.1. Quality
1.1.3.1.1. Reliable
1.1.3.2. Integration
1.1.3.3. Privacy
1.1.3.3.1. Biometrics
1.1.3.3.2. Authentication
1.2. Analyze Data
1.2.1. Data Mining
1.2.2. Predictive Pattern
1.3. Data Visualization
1.3.1. Charts
1.3.2. Dashboards
1.4. Data Processing
1.4.1. Cleaning Data
1.4.1.1. Transformation
1.4.1.2. Reduction
1.4.2. Data Integration
1.4.2.1. Decision Making
2. Data-Driven Decision Making
2.1. Identify Sources
2.1.1. Articles
2.1.2. Surveys
2.2. Collecting Data
2.2.1. Data Sets
2.3. Analyze Data
2.3.1. Statistical Analysis
2.3.1.1. Collecting Data
2.3.1.2. Explore
2.3.1.3. Infer
2.3.2. Machine Learning
2.3.2.1. Algorithms
2.3.2.2. Predictions
2.3.3. Data Mining
2.3.3.1. Patterns
2.3.3.2. Relationships
3. Big Data
3.1. Volume
3.1.1. Data Storage
3.1.1.1. Data Bases
3.1.2. Data Scale
3.1.2.1. Size of Data
3.2. Velocity
3.2.1. Data Processing Time
3.2.1.1. Anlyzing Big Data
3.2.1.1.1. Real Time Processing
3.2.1.1.2. Latency
3.2.1.1.3. Data Stream
3.3. Variety
3.3.1. Structured Data
3.3.1.1. Spreadsheets
3.3.1.1.1. Excel
3.3.1.1.2. CSV
3.3.1.1.3. Google Sheets
3.3.1.2. Relational Data
3.3.1.2.1. SQL
3.3.1.2.2. Customer Relationship Management
3.3.2. Unstructured Data
3.3.2.1. Web Data
3.3.2.1.1. Blogs
3.3.2.1.2. Web Pages
3.3.2.2. Text Data
3.3.2.2.1. Emails
3.3.2.2.2. Documents
3.3.3. Semi-Structured Data
3.3.3.1. XML
3.3.3.2. JSON