Master in Data Science (SMS, TU) Fundamentals of Data Science Question Paper 2082 (Set page 22; 2082 board/final exam (IOST,TU)) Nepal

What is Data Science? Explain EDA process in brief.

How encoding is done for categorical variables. Explain.

How do you mean by ETL and ELT operations? Which operation data lake uses? Explain.

Explain how forward selection and mutual information can be used for feature selection.

Explain fairness. Explain any two methods for addressing bias.

You are a data scientist. Take a data science project with title and complete it using the TDSP approach. Explain what should be done in every step detailly.

OR

Explain CRISP-DM with its steps and compare and contrast it with OSEMN framework.

What is data munging? Explain what are the different issues in real world data along with the steps needed for handling those steps detailly.

Explain linear regression, logistic regression and decision trees for fitting model in detail.

Explain node and weights in neural networks. Consider following Neural Network and compute its output considering sigmoid as activation function for all layers. Weights of synaptic links are provided above each link.

Input: $X1 = 2$, $X2 = 3$. Feedforward net: input nodes feed 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 = 0.4, $X2 \to$ node 1 = 1, $X2 \to$ 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.

OR

What is multi-layer neural network? How is learning done in neural networks? Explain backpropagation algorithm.

What is Hadoop? Explain its components in detail.