Pengantar Sains Data 02 - Tipe & Format Data (1/2)

Summary

The script discusses the concepts of data in the context of Industry 4.0, exploring types of data, their formats, and their roles in data science. It explains the distinction between data, information, knowledge, insight, and wisdom. The transcript also delves into various classifications of data, including primary and secondary sources, and structured data types such as nominal, ordinal, interval, and ratio. The discussion includes practical examples and highlights the importance of understanding data from different perspectives in order to generate valuable insights, which are essential for data scientists and business decision-making.

Takeaways

• 😀 Data is defined as facts or measurements, such as body weight, wind speed, or vehicle speed.
• 😀 Information is data that has context added to it, such as the speed of a bus making a turn or a car traveling at a specific speed under certain circumstances.
• 😀 Knowledge refers to the theoretical or practical understanding gained from information, like knowing that a bus making a sharp turn at high speed may cause an accident.
• 😀 Insight is the deep understanding of a specific field, such as traffic safety or physics, obtained through thorough analysis and perspective.
• 😀 Wisdom is the application of knowledge and insight to make informed, well-judged decisions or actions.
• 😀 Data can be classified by its source into primary and secondary data.
• 😀 Primary data is directly collected by the researcher through methods like surveys or measurements, while secondary data is collected by others and reused by the researcher.
• 😀 Structured data refers to data that is organized in tables and can be classified into types such as nominal, ordinal, interval, and ratio.
• 😀 Nominal data involves categories without any particular order, such as gender, religion, or country.
• 😀 Ordinal data involves categories with a set order but no precise difference between them, such as education levels or military ranks.
• 😀 Interval data consists of numerical values with equal intervals but no true zero, like temperature in Celsius or Fahrenheit.
• 😀 Ratio data is similar to interval data but includes a true zero, making it possible to make direct comparisons, such as weight or salary.

Q & A

What is the difference between data, information, knowledge, insight, and wisdom?

Data refers to raw facts or measurements, such as weight, height, or wind speed. Information is data given context, like 'a vehicle is moving at 89 km/h'. Knowledge is understanding and interpreting data and information, often involving theory or practical experience. Insight is a deeper understanding of data and information, often acquired through analysis, which helps to understand the underlying patterns. Wisdom is the ability to apply insights effectively and make informed decisions based on the understanding of the situation.

What is the role of a data scientist in obtaining insights?

A data scientist’s role is to extract insights from data by analyzing it from multiple perspectives. This involves combining the raw data with business understanding, domain knowledge, and various analytical techniques to uncover meaningful patterns or conclusions.

How does wisdom differ from insight?

While insight refers to deep understanding, wisdom involves taking action based on that understanding. Wisdom is the practical application of insights, where the individual makes decisions with good judgment, considering all factors involved.

What are primary and secondary data sources?

Primary data is collected directly by the researcher or observer, such as surveys or firsthand measurements. Secondary data, on the other hand, is collected by others and used by the researcher, like data downloaded from the internet or from previously published research.

What are structured and unstructured data?

Structured data refers to data that is organized in a table format, often with specific fields and categories (e.g., spreadsheets or databases). Unstructured data, however, is not organized in a predefined manner and can include text, images, videos, and more, which require additional processing for analysis.

What are the four types of data classification based on measurement scales?

The four main types of data classification are: nominal (categorical data without any order, like gender or country), ordinal (data with a meaningful order, like education levels), interval (data with numerical values where the differences between values are meaningful, but there is no true zero, such as temperature), and ratio (data with a meaningful zero point and the ability to compare ratios, such as height or weight).

What is the significance of time-series data in analytics?

Time-series data refers to data points collected or recorded at specific time intervals. It is essential for analyzing trends over time, forecasting future events, and understanding patterns or cycles that emerge from the time dimension.

How is spatial data used in data analysis?

Spatial data refers to data related to locations or geographic information. It is essential in fields such as geography, urban planning, and environmental studies, as it helps analyze patterns based on physical locations, such as mapping population density or weather patterns.

What are some common debates around data classification?

One common debate revolves around the best way to classify data types. Some argue that the traditional classification (nominal, ordinal, interval, ratio) may not be sufficient for all types of data, and newer approaches or mixed classifications are needed to better represent the diverse range of modern data.

What is the difference between nominal and ordinal data?

Nominal data consists of categories with no inherent order (e.g., gender, country), while ordinal data includes categories with a meaningful order or ranking (e.g., education levels, military ranks). The key difference is that ordinal data has a hierarchical structure, while nominal data does not.