Essential Data Science Concepts and Statistical Measures
Foundational Concepts in Data Science and Statistics
Essential Data Science Terminology
The following terms represent fundamental concepts used in data analysis and machine learning:
- Data Science: A field that uses scientific methods, algorithms, and tools to extract knowledge and insights from data.
- Datafication: The process of transforming information, activities, or objects into a data format.
- Population & Sample: The Population is the entire group being studied; the Sample is a representative subset of the population.
- Overfitting: A statistical model that performs exceptionally well on training data but poorly on new, unseen data.
- Attribute: A property or characteristic of a data object (often referred to as a feature or variable).
- Binary Attribute: An attribute that can only take on two possible values (e.g., Yes/No, True/False).
- Central Tendency: A measure that represents the center value or typical value of a dataset.
- Range: The difference calculated between the largest and smallest values in a dataset.
- Histogram: A graphical representation used to display the distribution of numerical data using bars.
- Basic Data Types in R: Includes fundamental types such as Numeric and Character.
Understanding Measures of Central Tendency
Measures of central tendency describe the center point of a dataset. These statistical measures—the Mean, Median, and Mode—summarize data effectively, offering insights into typical or representative values.
Mean (The Arithmetic Average)
The Mean is the arithmetic average, calculated by summing all values and dividing by the total number of observations. For example, the mean of 10, 20, and 30 is 20.
Key Consideration: The mean is sensitive to extreme values (outliers).
Median (The Middle Value)
The Median is the middle value when the data is arranged in sequential order. In the set {5, 7, 9}, the median is 7. For datasets with an even number of observations, the median is calculated as the average of the two middle values.
Key Consideration: The median is generally more robust than the mean, as it is less affected by outliers.
Mode (The Most Frequent Value)
The Mode is the most frequently occurring value within a dataset. It is particularly useful for analyzing categorical data, such as identifying the most common favorite colors or brands.
Summary of Statistical Uses
Each measure serves a distinct purpose in data analysis: the mean provides a true average but is sensitive to extremes, the median offers a robust center point, and the mode identifies common patterns and frequencies.