Master in Data Science (SMS, TU) Fundamentals of Data Science Question Paper 2082 (Set page 15; Second Assessment 2082) Nepal
This is the official Master in Data Science (SMS, TU) Fundamentals of Data Science question paper for 2082 Set page 15; Second Assessment 2082, as set in the Second Assessment examination. It carries 45 full marks and a time allowance of 120 minutes, across 10 questions. On Kekkei you can attempt this Fundamentals of Data Science past paper online with a timer, get instant AI feedback and step-by-step solutions, and track the topics where you lose marks — completely free. Whether you are revising for your Master in Data Science (SMS, TU) Fundamentals of Data Science exam or solving previous years' question papers, this 2082 paper is a great way to practise under real exam conditions.
| Level | Master in Data Science (SMS, TU) |
|---|---|
| Subject | Fundamentals of Data Science |
| Year | 2082 BS |
| Exam session | Second Assessment · Set page 15; Second Assessment 2082 |
| Full marks | 45 |
| Time allowed | 120 minutes |
| Questions | 10, all with step-by-step solutions |
Group A
What is data munging? Why it is done? Explain detailly.
What is probability distribution? Explain discrete and continuous probability distribution.
Explain how classification is done by SVM classifier.
Explain time series data. What are the components of time series data.
What is big data? Explain the characteristics of big data.
Group B
You are a data scientist. Take a data science project and complete it using the CRISP-DM approach. Explain what should be done in every step. [1+5]
OR
What is data driven decision making and how does data science assist data driven decision making? Explain OSEMN lifecycle for your data science project. [3 + 3]
What is feature selection? Explain filters and wrappers method for feature selection. [1 + 5]
The following table represents a dataset of 10 objects with attributes Color, Type, Origin and the "class", whether the customer who bought was satisfied or not.
| S. No | Color | Type | Origin | Satisfied? |
|---|---|---|---|---|
| 1 | Red | Casual | Domestic | Yes |
| 2 | Red | Casual | Domestic | No |
| 3 | Red | Casual | Domestic | Yes |
| 4 | Yellow | Casual | Domestic | No |
| 5 | Yellow | Casual | Imported | Yes |
| 6 | Yellow | Casual | Imported | Yes |
| 7 | Yellow | Formal | Imported | No |
| 8 | Yellow | Formal | Imported | Yes |
| 9 | Yellow | Formal | Domestic | No |
| 10 | Red | Formal | Imported | No |
| 11 | Red | Casual | Imported | Yes |
Use ID3 algorithm to find the attribute with maximum information gain.
OR
What is multi-layer neural network? How is learning done in neural networks? Explain backpropagation algorithm. [1 + 1 + 4]
What is Hadoop? Explain its components in detail. [2 + 4]
What is cognitive bias? Explain any two cognitive biases. Explain techniques for addressing bias.
Frequently asked questions
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- How many marks is the Master in Data Science (SMS, TU) Fundamentals of Data Science 2082 paper?
- The Master in Data Science (SMS, TU) Fundamentals of Data Science 2082 paper carries 45 full marks and is meant to be completed in 120 minutes, across 10 questions.
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