This lesson covers various methods of data collection and how to identify and evaluate reliable data sources for analysis.
What You'll Learn:
Data collection methods (surveys, sensors, web scraping, etc.)
Primary vs secondary data sources
Evaluating data quality and reliability
Ethical considerations in data collection
Key Concepts:
Primary Data: Data collected directly by the researcher
Secondary Data: Data collected by someone else
Data Quality: Accuracy, completeness, and reliability of data
Data Ethics: Moral principles in data collection and usage
The Data Lifecycle
Data analysis follows a systematic lifecycle that ensures quality insights and reliable results. Understanding this lifecycle is fundamental to effective data analytics.
1. Collect
The collection phase involves gathering raw data from various sources. This is the foundation of any data analysis project.
Key Activities:
Identifying data sources
Designing collection methods
Implementing data gathering procedures
Ensuring proper documentation
Collection Methods:
Surveys and Questionnaires: Structured data collection from targeted populations
Interviews: In-depth qualitative data collection
Observations: Direct recording of behaviors or events
Sensors and IoT Devices: Automated data collection from physical systems
Web Scraping: Extracting data from websites and online sources
API Integration: Programmatic access to data from external services
2. Clean
Data cleaning transforms raw, messy data into a reliable dataset ready for analysis.
Common Data Issues:
Missing values
Duplicate records
Inconsistent formats
Outliers and anomalies
Incorrect data types
Cleaning Techniques:
Data Validation: Checking for accuracy and consistency
Normalization: Standardizing data formats
Imputation: Filling missing values using statistical methods
Deduplication: Removing duplicate records
Outlier Detection: Identifying and handling anomalous values
3. Analyze
The analysis phase involves applying statistical and analytical techniques to extract insights.
Analysis Approaches:
Descriptive Statistics: Summarizing data characteristics
Inferential Statistics: Making predictions about populations
Exploratory Data Analysis: Discovering patterns and relationships
Hypothesis Testing: Validating assumptions about data
Machine Learning: Building predictive models
4. Visualize
Visualization transforms complex data into understandable visual representations.
Visualization Types:
Charts and Graphs: Bar charts, line graphs, scatter plots
Dashboards: Interactive data displays
Heat Maps: Showing density and intensity
Infographics: Combining visuals with narrative
Geospatial Maps: Location-based data representation
Sampling vs Census
Census
A census involves collecting data from every member of a population.
Advantages:
Complete coverage of the population
No sampling error
Detailed subgroup analysis possible
High accuracy for population parameters
Disadvantages:
Expensive and time-consuming
May be impractical for large populations
Higher risk of non-response bias
Resource-intensive
When to Use:
Small, accessible populations
Government requirements (e.g., national census)
Critical business decisions requiring complete data
Regulatory compliance
Sampling
Sampling involves selecting a subset of the population to represent the whole.
Sampling Methods:
Probability Sampling:
Simple Random Sampling: Every member has equal chance
Stratified Sampling: Population divided into subgroups
Cluster Sampling: Population divided into clusters
Systematic Sampling: Select every kth element
Non-Probability Sampling:
Convenience Sampling: Easily accessible subjects
Quota Sampling: Specific characteristics required
Snowball Sampling: Referrals from initial subjects
Purposive Sampling: Specific criteria-based selection
Advantages:
Cost-effective and time-efficient
Feasible for large populations
Faster data collection
Reduced respondent fatigue
Disadvantages:
Sampling error potential
May not represent population accurately
Requires statistical expertise
Limited subgroup analysis
Sample Size Determination:
Confidence Level: Typically 95% or 99%
Margin of Error: Usually ±3% to ±5%
Population Size: Total population consideration
Expected Variability: Data diversity impact
Data Sources
Primary Data Sources
Primary data is collected directly by the researcher for specific purposes.
Collection Methods:
Surveys and Questionnaires:
Online surveys (Google Forms, SurveyMonkey)
Telephone interviews
Face-to-face interviews
Mail surveys
Mobile app surveys
Observational Studies:
Direct observation
Video recording
Sensor data
Behavioral tracking
User interaction logs
Experiments:
A/B testing
Controlled experiments
Field experiments
Laboratory studies
Clinical trials
Secondary Data Sources
Secondary data is collected by others for different purposes but can be repurposed.
Types of Secondary Data:
Databases:
Relational Databases: MySQL, PostgreSQL, SQL Server
NoSQL Databases: MongoDB, Cassandra, Redis
Data Warehouses: Snowflake, Redshift, BigQuery
Data Lakes: Hadoop, Amazon S3, Azure Data Lake
APIs (Application Programming Interfaces):
REST APIs: HTTP-based data access
GraphQL APIs: Flexible data querying
WebSocket APIs: Real-time data streams
Public APIs: Government, social media, financial data
Public and Commercial Sources:
Government Data: Census, economic indicators, health statistics
Academic Research: University studies, research papers