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ENCT 202Bachelor in Computer Engineering · Semester 31 Paper Available

Foundation of Data Science

Past examination question papers and complete curriculum syllabus for Foundation of Data Science (ENCT 202), Bachelor in Computer Engineering Semester 3 under Institute of Engineering (IOE), Tribhuvan University.

Sem 3 Subjects (6)View Full Semester Overview →
ENSH 201Engineering Mathematics IIIENSH 204Communication EnglishENCT 201Computer Graphics and VisualizationENCT 202Foundation of Data ScienceENCT 203Theory of ComputationENEX 201Microprocessors
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3rd-sem_Foundation of Data Science.pdf

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Most Frequently Asked Questions

Top recurring IOE board exam questions for Foundation of Data Science with verified mark schemes, formula notation, and recurrence frequency.

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Year-wise & Session-wise Exam Papers

Complete questions from each exam session (Regular & Back exams from 2082 to 2069) for Foundation of Data Science. Click any session paper to expand.

Showing 6 questions across 1 exam session papers
Institute of Engineering • 2081 Chaitra Board Examination
Q1[5 Marks]Introduction to Data Science
How is a programmer different from a data scientist? Explain the differences in terms of skill requirements and the various roles available for data scientists in the job market.
Q2[6 Marks]Mathematics for Data Science
a) Prepare a note on Central Limit Theorem. Why this is considered as important in data analysis and other tasks? Briefly explain. [4] b) An e-mail filter is planned to separate valid e-mails from spam. The word free occurs in 70% of the spam messages and only 4% of the valid messages. Also, 25% of the messages are spam. Determine the following probabilities: (i) The message contains free. (ii) The message is spam given that it contains free. (iii) The message is valid given that it does not contain free.
Q3[4 Marks]Data Understanding and Preprocessing
a) What is semi-structured data? How it is different than structured and unstructured data? Explain briefly. [3] b) How do you handle missing data during data preprocessing? Briefly explain two methods illustrating with a small sample data example.
Q4[4 Marks]Data Understanding and Preprocessing
Here is the 10 random sample data of graduate student age from Sociology and Political Science program class. a) Calculate normalized ages of 2-students (Data #1, both Women and Men) using z-score and min-max of Zero-to-one. [4] (Show your all calculations and consider all 10 data as a single sample.) b) Using the same data above, draw a box-plot for depiction of all 10 student age distribution. You need to show all calculations and label the plot properly.
Q5[5 Marks]Data Analysis
a) What are the purposes of data visualization? Explain with an example of heat-map based visualization. [5] b) What is regression, and how does it support predictive analytics? Explain with a small sample business application.
Q6[8 Marks]Ethics and Recent Trends
Write brief notes on: (Any Three) [3x4] (i) GDPR (ii) Generative AI (iii) Feature selection (iv) Performance measure using MAE

Curriculum Syllabus & Course Topics

Sourced from TU curriculum portal
Full Syllabus (PDF)
Chapter-wise Units & Micro-Syllabus Topics (7 Units)

  1. 1. Introduction to Data Science

    • 1.1Overview of data science
    • 1.2Jargons of data science
    • 1.3Modern data ecosystem
    • 1.4Data science lifecycle
    • 1.5Trends, markets and applications of data science
    • 1.6Tools and technologies in data science
    • 1.7Data scientist and their roles

  2. 2. Mathematics for Data Science

    • 2.1Introduction to linear algebra for data science
    • 2.2Vectors, matrices and matrix factorization
    • 2.3Gradient descent for optimization
    • 2.4Introduction to probability and random variable
    • 2.5Probability distributions: Normal, Bernoulli, Binomial, Poisson
    • 2.6Descriptive and inferential statistics
    • 2.7Central limit theorem and sample distribution concepts
    • 2.8Normal approximation; hypothesis testing procedures: Tests about the mean of a normal population
    • 2.9The t-test, Z-tests for differences between two populations means, the two- sample t-test, confidence interval for mean of normal population
    • 2.10ANOVA

  3. 3. Data Understanding and Preprocessing

    • 3.1Types of data: Structured, unstructured, semi-structured
    • 3.2Data preprocessing requirements
    • 3.3Data sources and collection methods
    • 3.4Data cleaning and preparation
    • 3.5Data wrangling and associated tools
    • 3.6Data enrichment, validation and publishing
    • 3.7Data transformation and normalization
    • 3.8Dimensionality reduction linear factor model, principal component analysis (PCA)

  4. 4. Data Analysis

    • 4.1Data analytics: Descriptive, diagnostic, predictive and prescriptive analytics
    • 4.2Exploratory data analysis using descriptive statistics
    • 4.3Data visualization
    • 4.4Data visualization techniques
    • 4.5Principles of effective data visualization
    • 4.6Feature engineering and other aspects of data manipulation

  5. 5. Regression and Predictive Modeling

    • 5.1Empirical models, simple linear regression, MLE and least square estimator
    • 5.2Multiple linear regression, matrix approach to multiple linear regression, polynomial regression models, categorical regressors, indicator variables, selection of variables and model building
    • 5.3Logistic regression

  6. 6. Modeling and Validation Processes

    • 6.1Introduction to machine learning
    • 6.2Introduction to supervised, unsupervised and reinforcement learning
    • 6.3Modeling process, training /validating model, cross validation methods, predicting new observations interpretation
    • 6.4Measures for model performance and evaluation: Classification accuracy, confusion matrix, sensitivity, specificity, precision, recall, F-score, ROC curve, clustering performance measures, other measures

  7. 7. Ethics and Recent Trends

    • 7.1Ethical considerations in data science
    • 7.2Data privacy regulations
    • 7.3Responsible data usage
    • 7.4The five Cs
    • 7.5Future trends

Examination Scheme & Marks Distribution

Evaluation Structure

  • Final Board Theory Exam: 60 Marks (Pass mark: 24)
  • Internal Assessment: 40 Marks (Pass mark: 16)
  • Practical / Lab Exam: 25 or 50 Marks (Continuous lab evaluation + viva, where applicable)

* This is the general current IOE 60/40 scheme; verify course-specific details in the syllabus above.

Exam Preparation Guidelines

  • Review the available past examination paper to understand question styling, typical derivation topics, and marks allocation.
  • Cross-reference key answers with official syllabus units, standard textbooks, and lecture notes.
  • Structure answers with labeled diagrams, concise bullet points, and highlight final answers in numerical solutions.
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