Consuming statistical results 7 Data analysis tasks I Data analysis tasks in

Consuming statistical results 7 data analysis tasks i

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Consuming statistical results 7 Data analysis tasks (I) Data analysis tasks in data mining, statistics, and machine learning Supervised learning
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Syllabus for INF 549 Fall 2018, Page 4 o Classification tasks o Classification algorithms o Evaluation of classifiers 8 Data analysis tasks (II) Unsupervised learning o Clustering o Pattern detection o Anomaly detection Simulation and prediction 9 Data analysis tasks (III) Causality o Probabilistic graphical models o Bayesian networks o Causal models Section III: Data Analysis in Practice 10 Analyzing different kinds of data (I) Analyzing text data o Pre-processing text o Document classification o Document clustering o Topic detection o Sentiment analysis 11 Analyzing different kinds of data (II) Analyzing time series data o Collecting time series data o Pre-processing time series data o Event detection o Granger causality Homework HW3: Analyzing data with workflows 12 Analyzing different kinds of data (III) Analyzing network data o Network structure o Dynamic networks o Scale-free networks o Network analysis 13 Analyzing different kinds of data (IV) Analyzing multimedia data o Pre-processing images o Segmentation o Edge detection o Object detection o Video analysis Analyzing geospatial data o Coordinate systems o GIS systems Section IV: User interfaces and user studies 14 Data visualization Quality of visualizations Major types of visualizations Homework HW3: Data visualization
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Syllabus for INF 549 Fall 2018, Page 5 Time series visualizations Geospatial visualizations Multi-dimensional spaces Network visualizations 15 User experience and user interfaces UX/UI Design Principles AB testing Basics of user study design Homework HW5: Project part 2 – Design of data analysis approach 16 User studies User study design Null hypothesis significance testing Advanced analysis for experiments 17 Causal claims from user studies Correlational research Comparing correlational research to experiments Ensuring internal validity Section V: Data analysis at large scale 18 Parallel and distributed computing for big data (I) Cost of computation Divide and conquer Speedup with parallel processing Limits of speedup: Critical path Amdahl’s law When problems are not parallelizable 19 Parallel and distributed computing for big data (II) Multi-core computing Distributed computing Cluster computing Cloud computing Grid computing Virtual machines Web services Practical concerns in distributed computing Parallel programming languages o MapReduce/Hadoop Homework HW6: Data analysis with parallel processing Section VI: Metadata 20 Semantic metadata What is metadata Basic metadata versus semantic metadata Metadata about data collection Metadata about data processing Metadata for search and retrieval Metadata standards
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Syllabus for INF 549 Fall 2018, Page 6 Domain metadata and ontologies 21 Ontologies (I) What is an ontology Taxonomies and class inheritance Properties Logical constraints 22 Ontologies (II)
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