| Data Science. Interview questions |
| 1 | KPI: KPI stands for Key Performance Indicator that measures how well the business achieves its objectives. |
| 2 | What is Bayes' Theorem and when is it used in data science? The Bayes theorem predicts the probability that an event connected to any condition would occur. It is also taken into account in the situation of conditional probability. The probability of "causes" formula is another name for the Bayes theorem. |
| 3 | Define variance and conditional variance. A statistical concept known as variance quantifies the spread or dispersion of a group of data points within a dataset. It sheds light on how widely individual data points depart from the dataset's mean (average). It assesses the variability or "scatter" of data. Conditional Variance. A measure of the dispersion or variability of a random variable under certain circumstances or in the presence of a particular event, as the name implies. It reflects a random variable's variance that is dependent on the knowledge of another random variable's variance. |
| 4 | Explain the concepts of mean, median, mode, and standard deviation. Mean: The mean, often referred to as the average, is calculated by summing up all the values in a dataset and then dividing by the total number of values. Median: When data are sorted in either ascending or descending order, the median is the value in the middle of the dataset. The median is the average of the two middle values when the number of data points is even. In comparison to the mean, the median is less impacted by extreme numbers, making it a more reliable indicator of central tendency. Mode: The value that appears most frequently in a dataset is the mode. One mode (unimodal), several modes (multimodal), or no mode (if all values occur with the same frequency) can all exist in a dataset. |
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