Master in Data Science (SMS, TU) Fundamentals of Data Science Question Paper 2081 (Set pages 5-6; First Assessment 2081) Nepal

What is data driven decision making? How does data science assist data driven decision making?

Explain mean and median as the centrality measure. In what cases median is preferred?

What is data scaling? Why and how it is done?

What is linear regression? Explain best fit line in linear regression.

Explain confusion matrix and its use? How do you interpret precision and recall?

What is Data Science? Explain CRISP-DM approach for Data Science. [1+5]

OR

What is Data Science Lifecycle? Explain TDSP approach for Data Science.

What do you mean by Data Munging? Explain what are the different issues in real world data along with the steps needed for handling those issues. [1+5]

What are tabular data? Explain the pros and cons of row-based and column-based data in detail. [1+5]

The following table presents a dataset of 11 objects with attributes Color, Type, Origin and the 'class' whether the customer who bought was satisfied or not.

Now classify a new object using Naïve Bayes classifier with the following properties: Color = Red, Origin = Imported and Type = Formal

OR

What is clustering technique? Divide the data points $\{(2,10), (2,5), (8,4), (5,8), (7,5), (6,4)\}$ into two clusters using K-Means Clustering technique. [1+5]

Compute the output of following neural network using sigmoid activation function. Weights of synaptic links are provided above each link.

Input: $X1 = 2$, $X2 = 3$. Network is a feedforward net with input nodes feeding hidden nodes 1 and 2, which feed nodes 3 and 4, which feed output node 5. Weights: $X1 \to$ node 1 = 0.8, $X1 \to$ node 2 = 0.2, $X2 \to$ node 1 = 0.4, $X2 \to$ node 2 = 0.6, node 1 $\to$ node 3 = 1.2, node 1 $\to$ node 4 = 0.4, node 2 $\to$ node 3 = 0.7, node 2 $\to$ node 4 = 0.5, node 3 $\to$ node 5 = 1.5, node 4 $\to$ node 5 = 0.5.