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This lesson covers various methods of data collection and how to identify and evaluate reliable data sources for analysis.
Data analysis follows a systematic lifecycle that ensures quality insights and reliable results. Understanding this lifecycle is fundamental to effective data analytics.
The collection phase involves gathering raw data from various sources. This is the foundation of any data analysis project.
Key Activities:
Collection Methods:
Data cleaning transforms raw, messy data into a reliable dataset ready for analysis.
Common Data Issues:
Cleaning Techniques:
The analysis phase involves applying statistical and analytical techniques to extract insights.
Analysis Approaches:
Visualization transforms complex data into understandable visual representations.
Visualization Types:
A census involves collecting data from every member of a population.
Advantages:
Disadvantages:
When to Use:
Sampling involves selecting a subset of the population to represent the whole.
Sampling Methods:
Probability Sampling:
Non-Probability Sampling:
Advantages:
Disadvantages:
Sample Size Determination:
Primary data is collected directly by the researcher for specific purposes.
Collection Methods:
Surveys and Questionnaires:
Observational Studies:
Experiments:
Secondary data is collected by others for different purposes but can be repurposed.
Types of Secondary Data:
Databases:
APIs (Application Programming Interfaces):
Public and Commercial Sources:
Web Scraping:
Reliability:
Validity:
Accessibility:
Completeness:
Decision Making:
Financial Impact:
Customer Experience:
Human Error:
Technical Issues:
Process Failures:
Data Validation Techniques:
Quality Control Processes:
Best Practices:
Quality Dimensions:
Quality Metrics:
Data Collection Strategy:
Data Collection Strategy:
In the next lesson, we'll explore data cleaning and preprocessing techniques to prepare collected data for analysis.