Once we understand the problem and set clear goals, the next step in DSLC is to collect the required data. Data is like raw material, and we must gather the right kind of data before we can clean, store, or analyze it.
In Step 1, we asked questions like "Which school activities do students enjoy the most?" or "Why are students arriving late to school?"
Step 2 is about collecting information that can help us answer these questions.
We will explore:
(i) data collection methods (ii) where to collect data from (sources) (iii) How to make sure it is useful and fair (data quality)
(i) Methods of Data Collection:
There are many ways to collect data, depending on the problem and available tools. Let us discuss the most common ones used in schools, homes, and businesses.
Surveys (Online/paper based): Surveys are the easiest and most common method. We ask people questions and record their answers. For example: To find out which sports students enjoy, we can create a survey with questions such as (i) Which sport do you like the most? (ii) How often do you play it? (iii) What time of day do you prefer to play?
Surveys can be done using Google Forms (online), and Printed paper forms (manual).
APIs and Online Sources: Application Programmer Interface (APIs) allow software collect data from websites or applications (apps in short) automatically. For example, A school may use an attendance API to pull daily attendance data into a report (use more by developers).
IoT and Sensor Data: Internet of Things (IoT) devices like smart watches, water dispensers, or temperature sensors can automatically collect data. For example, To study water drinking habits, schools can place smart water meters near fountains.
TIDBIT
IoT devices are useful in Health, safety, and environmental monitoring.
Web Scraping: It is a process of programmatically collecting public data from websites. For example, A business student may scrape extract prices of snacks from online stores to compare them with school canteen prices.