What are Data Collection Methods and Sources?
3 min read
To begin with, Data Science refers to the practice of studying and analyzing the data to extract meaningful insights for business. In addition, this practice combines numerous principles and practices for analyzing vast amounts of data. The primary objective of the data science practice is to generate meaning from data. Along with this, the Data Science practice uses various kinds of Data collection methods for getting the necessary raw data.
What are Data Collection Methods?
The Data collection methods refer to the techniques and procedures that are useful for gathering the information for research purposes. In addition, these methods consist of various simple self-reported surveys to more complex experiments. There are numerous Data Collection methods and the common ones include surveys, interviews, observations, focus groups, experiments, and secondary data analysis. Many institutes provide the Data Science Course and enrolling in them can help you start a career in this domain. These methods are useful for collecting a vast amount of data which is further sent for analysis and research hypotheses.
Types of Data Collection Methods
There are numerous methods for collecting the data and choosing the ideal one of them depends on various factors. Some such factors are the research question being addressed, the type of data needed, and the resources and time available. Preparing for the Data Science Certification will surely help you learn the types of data collection methods. However, the Data Collection methods are further categorized into two different methods which are primary and secondary.
Primary Data Collection Methods
It consists of collecting the primary data from first-hand experience. This data is highly accurate and specific to the research’s motive. Furthermore, the Primary data collection methods are further divided into two types which are quantitative methods and qualitative methods.
Quantitative Methods- It uses statistical tools which are useful for making long-term forecasts. Furthermore, the Statistical analysis methods are highly reliable as subjectivity is minimal. Its examples are as follows:
- Time Series Analysis
- Smoothing Techniques
- Barometric Method
Qualitative Methods- The Qualitative data methods are used when there are no historical data available. The Qualitative data methods are associated with feelings, emotions, colors, and non-quantifiable elements. Along with this, these methods don’t provide you with the motive behind the participants’ responses. Its examples are as follows:
- Surveys
- Polls
- Interviews
- Delphi Technique
- Focus Groups
- Questionnaire
Secondary Data Collection Methods
Secondary data refers to the data that has been used in the past. In addition, this kind of data can be easily obtained from the data sources which are both internal and external.
Here are the internal sources of secondary data:
- Organization’s health and safety records
- Mission and vision statements
- Financial Statements
- Magazines
- Sales Report
- CRM Software
- Executive summaries
External sources of secondary data:
- Government reports
- Press releases
- Business journals
- Libraries
- Internet
Conclusion
Data science relies on data to extract valuable insights. Data collection methods are the foundation for gathering this data. These methods range from simple surveys to complex experiments. Primary methods can be quantitative (numerical) or qualitative (descriptive). Common examples include surveys, interviews, observations, and focus groups. Secondary data comes from internal sources like reports or external sources like government data or journals. Choosing the right method depends on your research question, data needs, and available resources. In conclusion, by understanding these methods, you can effectively gather the data needed to fuel your data science explorations.
