📚 Topic Summary
Missing data is a common problem in data science. It occurs when some values are not stored in a dataset. Handling missing data is a crucial step in the data cleaning process because it can significantly impact the results of any analysis or model building. Common techniques include imputation (replacing missing values with estimated ones), deletion (removing rows or columns with missing values), and using algorithms that can handle missing data directly. Choosing the right approach depends on the amount and nature of the missing data, as well as the goals of the analysis.
🧠 Part A: Vocabulary
Match the terms with their definitions:
| Term |
Definition |
| 1. Imputation |
a. Removing rows or columns containing missing values. |
| 2. Deletion |
b. The percentage of values missing in a dataset. |
| 3. Missingness Rate |
c. Replacing missing values with estimated values. |
| 4. Mean Imputation |
d. Replacing missing values with the average of the available data. |
| 5. Data Cleaning |
e. The process of identifying and correcting errors and inconsistencies in a dataset. |
✏️ Part B: Fill in the Blanks
Fill in the blanks with the appropriate words from the list: imputation, deletion, missing, analysis, dataset.
Handling ________ data is a crucial step before performing any ________. If the ________ rate is low, ________ might be a viable option. However, if a significant portion of data is ________, ________ techniques are preferable to avoid losing too much information.
🤔 Part C: Critical Thinking
Imagine you are analyzing a survey about student study habits, and you notice that many students did not answer the question about their GPA. What are some potential reasons for this, and how might you handle this missing data? What are the pros and cons of each method you choose?