Master in Data Science (SMS, TU) Fundamentals of Data Science Question Paper 2081 Nepal

Why is Data Science often regarded as a role with blurry or ambiguous boundaries? Provide rationale to support your explanation.

Compare and contrast feature generation and feature selection algorithms.

Discuss on machine learning and its types.

Discuss common methods of data validation that can be applied to ensure the quality and integrity of the dataset.

Briefly explain the ETL and ELT process of data migration.

Elaborate on TDSP (Team Data Science Process) as a framework for the data science lifecycle.

OR

Discuss CRISP-DM (Cross-Industry Standard Process for Data Mining) as an agile approach to the data science lifecycle.

Consider a dataset representing whether students passed an exam based on three features: Study Hours (Low, Medium, High), Previous Grades (Low, Medium, High), and Tutoring (Yes or No). The target variable is Exam Result (Pass or Fail).

Using the ID3 algorithm, calculate the information gain for each feature (Study Hours, Previous Grades, Tutoring) and determine which feature should be chosen as the root node for the decision tree.

OR

Consider a dataset containing the coordinates of 8 points in a two-dimensional space:

• Point 1: (2, 3)
• Point 2: (3, 4)
• Point 3: (3, 5)
• Point 4: (4, 6)
• Point 5: (7, 8)
• Point 6: (8, 7)
• Point 7: (9, 8)
• Point 8: (10, 9)

Apply the K-Means algorithm to cluster these points into 3 clusters.

You are analyzing a dataset containing information about customer orders for an e-commerce platform. However, upon initial inspection, you notice several data quality issues that may impact the reliability of your analysis.

Describe three common data quality issues that you may have identified in the dataset, providing specific examples for each issue. Explain the potential consequences of these issues on your analysis and propose strategies to address them effectively.

Explain the linear regression algorithm with appropriate example.

Consider a dataset containing monthly sales data for a retail store over a period of two years. The dataset consists of the following columns: Date (representing the month), Sales (the total sales for that month) and profit. Using this dataset, answer the following questions:

a) Define what a time series is and explain its importance in data analysis. b) Identify and describe the different types of time series patterns that may exist in the sales data.