2.1 Data sources and types

Data collection and acquisition are crucial steps in data science. This section covers various data sources, including databases, files, and web services, as well as modern sources like IoT devices and social media platforms.

Understanding data structures is key to effective analysis. We'll explore structured, semi-structured, and unstructured data types, along with their characteristics and limitations. This knowledge helps in choosing appropriate tools and techniques for data processing.

Data Sources

Database Types and Characteristics

Top images from around the web for Database Types and Characteristics

• 

  Chapter 7 The Relational Data Model – Database Design – 2nd Edition View original

  Is this image relevant?

• 

  CMAP - Database Structure View original

  Is this image relevant?

• 

  NoSQL, Graph Database for Enterprises – How to derive Logical Graph Domain Data Model from ... View original

  Is this image relevant?

• 

  Chapter 7 The Relational Data Model – Database Design – 2nd Edition View original

  Is this image relevant?

• 

  CMAP - Database Structure View original

  Is this image relevant?

1 of 3

Top images from around the web for Database Types and Characteristics

• 

  Chapter 7 The Relational Data Model – Database Design – 2nd Edition View original

  Is this image relevant?

• 

  CMAP - Database Structure View original

  Is this image relevant?

• 

  NoSQL, Graph Database for Enterprises – How to derive Logical Graph Domain Data Model from ... View original

  Is this image relevant?

• 

  Chapter 7 The Relational Data Model – Database Design – 2nd Edition View original

  Is this image relevant?

• 

  CMAP - Database Structure View original

  Is this image relevant?

1 of 3

• Databases organize data for efficient retrieval and manipulation
  • Relational databases (MySQL, PostgreSQL) structure data in tables with predefined schemas
  • NoSQL databases (MongoDB, Cassandra) offer flexible schemas for unstructured or semi-structured data
• Files store data on computer systems
  • Text files (CSV, JSON) contain human-readable data
  • Binary files (images, audio) store non-textual information
  • Spreadsheets (Excel, Google Sheets) combine features of databases and text files
• Web services enable machine-to-machine interaction over networks
  • RESTful APIs use HTTP methods for data operations
  • SOAP services employ XML-based messaging protocols
  • GraphQL endpoints allow clients to request specific data structures

Modern Data Sources

• Internet of Things (IoT) devices generate real-time data
  • Smart home sensors (temperature, humidity)
  • Wearable fitness trackers (heart rate, steps)
• Social media platforms provide user-generated content
  • Text posts (tweets, status updates)
  • Visual content (Instagram photos, TikTok videos)
• Open data initiatives increase data accessibility
  • Government datasets (census data, crime statistics)
  • Scientific research data (genomic sequences, climate records)

Data Structures

Structured Data

• Follows predefined schema or model
• Organized in tables with rows and columns
• Examples include relational databases and spreadsheets
• Easily queried using standard query languages (SQL)
• Supports efficient indexing and searching
• Ideal for financial records, inventory management, and customer databases

Semi-Structured Data

• Possesses some organizational properties without rigid structure
• Common formats include XML and JSON
• Allows for nested data structures and flexible schemas
• Supports tags or metadata for improved organization
• Used in web services, configuration files, and document databases
• Requires more complex parsing compared to structured data

Unstructured Data

• Lacks predefined data model or organization
• Includes free-form text, images, audio, and video files
• Challenging to query and analyze using traditional methods
• Requires advanced techniques for information extraction
  • Natural Language Processing for text analysis
  • Computer Vision for image and video processing
• Examples include social media posts, email content, and surveillance footage

Data Types and Limitations

Numeric Data Types

• Integers represent whole numbers
  • Limited by available bits (32-bit, 64-bit)
• Floating-point numbers represent decimal values
  • Subject to rounding errors in calculations
• Complex numbers combine real and imaginary components
  • Used in scientific and engineering applications

Categorical and Textual Data

• Categorical data represents discrete categories or labels
  • Nominal data (unordered categories like colors)
  • Ordinal data (ordered categories like education levels)
• Text data encompasses strings of characters
  • Requires specialized processing techniques (tokenization, stemming)
  • Challenges include handling different languages and encodings

Temporal and Geospatial Data

• Temporal data includes dates and times
  • Requires careful handling of time zones and formats
  • Supports time-series analysis and event sequencing
• Geospatial data represents geographic locations and shapes
  • Utilizes specialized data structures (points, lines, polygons)
  • Enables spatial analysis and mapping applications

Binary and Specialized Data

• Binary data represents non-textual information
  • Includes images, audio files, and compiled software
  • Requires specific tools and libraries for manipulation
• Choice of data type impacts:
  • Storage requirements (space efficiency)
  • Processing speed (computational complexity)
  • Available analysis techniques (statistical methods, machine learning algorithms)

Data Quality and Reliability

Data Quality Dimensions

• Accuracy measures correctness of data values
• Completeness assesses presence of all required data
• Consistency ensures data aligns across different sources or time periods
• Timeliness evaluates how up-to-date the data is
• Validity confirms data adheres to defined rules or constraints
• Uniqueness prevents duplicate records or redundant information

Evaluating Data Source Reliability

• Assess reputation of data source
  • Consider credibility of organization or individual providing data
• Examine data collection methodologies
  • Evaluate sampling techniques, survey design, or measurement processes
• Review documentation of data provenance
  • Trace data lineage and transformation steps
• Implement data cleaning and preprocessing techniques
  • Handle missing values through imputation or deletion
  • Detect and address outliers
  • Normalize data to common scales or formats

Enhancing Data Reliability

• Employ version control for datasets
  • Track changes and updates over time
• Maintain data lineage records
  • Document sources, transformations, and usage of data
• Utilize cross-validation techniques
  • Compare data across multiple sources for consistency
• Consider ethical implications of data usage
  • Ensure compliance with privacy regulations (GDPR, CCPA)
  • Obtain informed consent for data collection when necessary
• Address potential biases in data collection
  • Recognize sampling biases (selection bias, response bias)
  • Mitigate biases through statistical techniques or diverse data sources