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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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CMP422 Data Science and Analytics

Data Science and Analytics notes

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

Unit 1

Data Science Basics: Definitions, Scope & ApplicationsUnit 1 of Data Science and Analytics introduces core concepts—what data science is, its evolution, key components (data, tools, techniques), and real-world applications in industries like finance, healthcare, and e-commerce. Learn how it differs from traditional statistics and business intelligence, and explore its rol10 min read

Unit 2

Data Sources, Cleaning Techniques & Quality AssuranceUnit 2 of Data Science and Analytics covers structured/unstructured data sources, collection methods (web scraping, APIs, databases), data cleaning techniques (handling missing values, outliers, duplicates), and quality assurance metrics (accuracy, completeness, consistency) with real-world applications in Nepalese tec5 min read

Unit 3

Exploratory Data Analysis: Techniques, Tools & InsightsUnit 3 of Data Science and Analytics explores how to summarize, visualize, and interpret raw data to uncover patterns, anomalies, and trends before formal modeling. This note covers key techniques (univariate/multivariate analysis, statistical summaries, and visualization), tools (Python/R libraries), and real-world ap13 min read

Unit 4

Data Visualization: Charts, Dashboards & InsightsUnit 4 of Data Science and Analytics explores how to transform raw data into meaningful visuals—charts, graphs, maps, and dashboards—to uncover patterns, trends, and stories. Learn principles of design, tools (Python, Tableau), and real-world applications from eSewa fraud detection to Daraz sales trends.9 min read

Unit 5

Statistical Inference: Sampling, Estimation, Hypothesis TestingUnit 5 of Data Science and Analytics explores how to draw reliable conclusions from data using statistical inference, covering sampling methods, point/interval estimation, hypothesis testing (t-tests, chi-square), and confidence intervals with real-world applications in Nepalese tech and finance.14 min read

Unit 6

Predictive Modelling: Algorithms, Evaluation & ApplicationsUnit 6 of Data Science and Analytics explores supervised learning techniques for forecasting future outcomes, covering regression, classification, model evaluation metrics, and real-world deployment challenges.10 min read

Unit 7

Classification & Clustering: Algorithms, Models & ApplicationsUnit 7 of Data Science and Analytics explores supervised classification (decision trees, SVM, logistic regression) and unsupervised clustering (K-means, hierarchical, DBSCAN) techniques, their mathematical foundations, real-world implementations, and performance trade-offs—with visualizations of algorithm workflows and9 min read

Unit 8

Text Analytics · note coming

Unit 9

Big Data Tools: Hadoop, Spark, NoSQL, Cloud & Stream ProcessingUnit 9 of Data Science and Analytics explores the core tools and architectures for handling big data—Hadoop (HDFS, MapReduce), Spark (RDDs, DataFrames), NoSQL databases (MongoDB, Cassandra), cloud platforms (AWS, GCP), and stream processing (Kafka, Flink)—with real-world examples from Nepalese and global tech ecosystem18 min read

Unit 10

Data Science Projects · note coming