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Tribhuvan University

BSc CSITBachelor of Science in Computer Science and Information TechnologyBCABachelor of Computer ApplicationBITBachelor of Information TechnologyBIMBachelor of Information ManagementBBABachelor of Business AdministrationBBSBachelor of Business StudiesBBMBachelor of Business ManagementBITMBachelor of Information Technology Management

Pokhara University

BE Computer (PU)Bachelor of Engineering in Computer EngineeringBBA (PU)Bachelor of Business Administration (Pokhara University)

National Examinations Board (+2)

Class 10Class 10 (SEE)+2 Science+2 Science (Class 11 and 12)+2 Management+2 Management (Class 11 and 12)+2 Humanities+2 Humanities (Class 11 and 12)
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Elective Data Analysis and Modeling

Data Analysis and Modeling notes

8 chapter notes, in syllabus order. Each starts with the key points.

Unit 1

Introduction to Data Analysis · note coming

Unit 2

Spreadsheet Data Management: Tools, Functions & WorkflowsUnit 2 of Data Analysis and Modeling explores spreadsheet fundamentals—data entry, formulas, functions, validation, and automation—using Excel/Google Sheets, with real-world applications in finance, logistics, and decision-making.8 min read

Unit 3

Descriptive Analytics: Measures, Visuals & Data SummarizationUnit 3 of Data Analysis and Modeling covers how to summarize raw data into meaningful insights using measures (central tendency, dispersion), graphical tools (histograms, boxplots), and techniques (data cleaning, binning) to reveal patterns—essential for business decisions like inventory optimization or customer segmen16 min read

Unit 4

Probability Models: Distributions, Rules & Real-World ApplicationsUnit 4 of Data Analysis and Modeling explores foundational probability models—discrete vs. continuous distributions, probability rules (addition/multiplication), Bayes’ theorem, and their applications in business decision-making. Learn through visual examples, real-world cases (e.g., Ncell’s call-drop prediction, Daraz10 min read

Unit 5

Hypothesis Testing: Types, Tests & Decision RulesUnit 5 of Data Analysis and Modeling explores how to make data-driven decisions by testing assumptions (hypotheses) about populations using sample data, covering null/alternative hypotheses, test statistics, p-values, significance levels, and common tests (z-test, t-test, chi-square, ANOVA) with real-world applications15 min read

Unit 6

Regression Modelling: Types, Assumptions, and ApplicationsUnit 6 of Data Analysis and Modeling explores regression modeling—how to predict relationships between variables, test assumptions, and apply linear/logistic regression in business. Learn formulas, diagnostics, and real-world uses in finance, marketing, and operations.8 min read

Unit 7

Time Series Forecasting: Models, Trends & ApplicationsUnit 7 of Data Analysis and Modeling teaches how to analyze past data patterns (trends, seasonality, cycles) to predict future values using ARIMA, exponential smoothing, and machine learning, with real-world applications in business, finance, and logistics.18 min read

Unit 8

Linear Programming Models · note coming

Unit 9

Decision Analysis: Trees, Payoffs, and Optimal ChoicesUnit 9 of Data Analysis and Modeling explores decision-making under uncertainty using decision trees, payoff matrices, expected value, and sensitivity analysis, with real-world applications in finance, logistics, and risk management.10 min read

Unit 10

Simulation Models: Types, Applications & Worked ExamplesUnit 10 of Data Analysis and Modeling explores simulation models—how to replicate real-world systems (queues, supply chains, financial risks) using probability, random variables, and computational tools. Learn discrete-event, Monte Carlo, and system dynamics simulations, with step-by-step examples (e.g., Pathao driver 17 min read