CSE437

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Data Science and Big Data Analytics

3 Credit Hour Course

Intended For Level 4 Term 2 Students

Prerequisite: CSE329

Introduction to data science and big data: business intelligence, application domains; Data analytics life cycle: discovery, data preparation, model planning, model building, communicating results & operationalize; Exploratory data analysis and statistical evaluation using R; Data wrangling and visualization: feature extraction, handling missing values, dimensionality reduction, PCA, t-SNE, UMAP, plotting with ggplot2, visualization with Tableau; Machine learning: libraries and platforms for classification, regression, clustering, association rules, recommendations and ranking; Time series analysis: Box-Jenkins method, ARIMA; Text analysis: TF/IDF, LDA, topic modeling; Streaming, spatial, audio, video and graph data analytics; Advanced data analytics using deep learning; Data engineering: extract, transform, and load (ETL), database, parallel database, data warehouse, data lake, OLAP, JSON, XML, NoSQL; Big data analytics: characteristics of big data, analytics using MapReduce, Hadoop, Spark, managing big data, HDFS, Hive, MongoDB, Cassandra; Data provenance, privacy, security and ethics; Applications in financial technology (Fintech), cybersecurity,governance, social media, smart city, healthcare, bioinformatics, physical sciences, environmental and climates sciences.