What is Data Science?

Data Mining

Data Mining >> What is Data Science?

1. According to the reading, the output of a data mining exercise largely depends on:

  • The programming language used.
  • The quality of the data.
  • The scope of the project.
  • The data scientist.

2. When data are missing in a systematic way, you can simply extrapolate the data or impute the missing data by filling in the average of the values around the missing data.

  • True.
  • False.

3. Prior Variable Analysis and Principal Component Analysis are both examples of a data reduction algorithm.

  • False.
  • True.

4. After the data are appropriately processed, transformed, and stored, what is a good starting point for data mining?

  • Non-parametric methods.
  • Machine learning.
  • Data Visualization.
  • Creating a relational database.

5. “Formal evaluation could include testing the predictive capabilities of the models on observed data to see how effective and efficient the algorithms have been in reproducing data.” This is known as:

  • In-sample forecast.
  • Reverse engineering.
  • Prototyping.
  • Overfitting.

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