Jawaharlal Nehru Technological University Hyderabad B.Tech III Year DATA ANALYTICS Previous Question Paper (July/August - 2023 - R18)

An outlier is a data point in a dataset that significantly deviates from the other observations, appearing to be inconsistent with the remainder of the data. These points often indicate measurement errors, experimental variability, or genuine anomalies that represent extreme phenomena.

In statistical analysis, outliers are typically identified using methods such as:

• Z-score analysis: Data points with a Z-score greater than 3 or less than -3 are often considered outliers.
• Interquartile Range (IQR) method: Data points falling below Q1 - 1.5 × IQR or above Q3 + 1.5 × IQR are classified as outliers.

Detecting outliers is crucial in data analytics because they can disproportionately influence statistical measures like the mean and standard deviation, potentially leading to biased models or incorrect conclusions.

The process of extracting data from signals involves converting continuous-time physical phenomena into a discrete-time digital format that can be processed by computational systems. This transformation follows a systematic pipeline:

• Sampling: The continuous signal is measured at discrete intervals defined by the Sampling Frequency (fs). According to the Nyquist-Shannon Sampling Theorem, the sampling rate must be at least twice the highest frequency component present in the signal to avoid aliasing, which is the distortion that occurs when high-frequency components are incorrectly represented as lower frequencies.
• Quantization: After sampling, the amplitude of each discrete sample is mapped to a finite set of values. Since the original signal has infinite precision, quantization introduces quantization noise, which is the difference between the actual signal value and the rounded digital value. The number of bits used for quantization determines the resolution of the data.
• Encoding: The quantized values are converted into binary sequences (bits). This step prepares the data for storage or transmission within a digital computer architecture.
• Preprocessing and Feature Extraction: Once in digital form, raw data is often cleaned using digital filters (e.g., Low-pass, High-pass) to remove noise. Finally, meaningful features are extracted using techniques such as Fast Fourier Transform (FFT) to move from the time domain to the frequency domain, or Wavelet Transforms to analyze transient signal characteristics.